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An executive summary is a thorough overview of a research report or other type of document that synthesizes key points for its readers, saving them time and preparing them to understand the study's overall content. It is a separate, stand-alone document of sufficient detail and clarity to ensure that the reader can completely understand the contents of the main research study. An executive summary can be anywhere from 1-10 pages long depending on the length of the report, or it can be the summary of more than one document [e.g., papers submitted for a group project].

Bailey, Edward, P. The Plain English Approach to Business Writing . (New York: Oxford University Press, 1997), p. 73-80.

Importance of a Good Executive Summary

Although an executive summary is similar to an abstract in that they both summarize the contents of a research study, there are several key differences. With research abstracts, the author's recommendations are rarely included, or if they are, they are implicit rather than explicit. Recommendations are generally not stated in academic abstracts because scholars operate in a discursive environment, where debates, discussions, and dialogs are meant to precede the implementation of any new research findings. The conceptual nature of much academic writing also means that recommendations arising from the findings are distributed widely and not easily or usefully encapsulated. Executive summaries are used mainly when a research study has been developed for an organizational partner, funding entity, or other external group that participated in the research . In such cases, the research report and executive summary are often written for policy makers outside of academe, while abstracts are written for the academic community. Professors, therefore, assign the writing of executive summaries so students can practice synthesizing and writing about the contents of comprehensive research studies for external stakeholder groups.

When preparing to write, keep in mind that:

  • An executive summary is not an abstract.
  • An executive summary is not an introduction.
  • An executive summary is not a preface.
  • An executive summary is not a random collection of highlights.

Christensen, Jay. Executive Summaries Complete The Report. California State University Northridge; Clayton, John. "Writing an Executive Summary that Means Business." Harvard Management Communication Letter (July 2003): 2-4; Keller, Chuck. "Stay Healthy with a Winning Executive Summary." Technical Communication 41 (1994): 511-517; Murphy, Herta A., Herbert W. Hildebrandt, and Jane P. Thomas. Effective Business Communications . New York: McGraw-Hill, 1997; Vassallo, Philip. "Executive Summaries: Where Less Really is More." ETC.: A Review of General Semantics 60 (Spring 2003): 83-90 .

Structure and Writing Style

Writing an Executive Summary

Read the Entire Document This may go without saying, but it is critically important that you read the entire research study thoroughly from start to finish before you begin to write the executive summary. Take notes as you go along, highlighting important statements of fact, key findings, and recommended courses of action. This will better prepare you for how to organize and summarize the study. Remember this is not a brief abstract of 300 words or less but, essentially, a mini-paper of your paper, with a focus on recommendations.

Isolate the Major Points Within the Original Document Choose which parts of the document are the most important to those who will read it. These points must be included within the executive summary in order to provide a thorough and complete explanation of what the document is trying to convey.

Separate the Main Sections Closely examine each section of the original document and discern the main differences in each. After you have a firm understanding about what each section offers in respect to the other sections, write a few sentences for each section describing the main ideas. Although the format may vary, the main sections of an executive summary likely will include the following:

  • An opening statement, with brief background information,
  • The purpose of research study,
  • Method of data gathering and analysis,
  • Overview of findings, and,
  • A description of each recommendation, accompanied by a justification. Note that the recommendations are sometimes quoted verbatim from the research study.

Combine the Information Use the information gathered to combine them into an executive summary that is no longer than 10% of the original document. Be concise! The purpose is to provide a brief explanation of the entire document with a focus on the recommendations that have emerged from your research. How you word this will likely differ depending on your audience and what they care about most. If necessary, selectively incorporate bullet points for emphasis and brevity. Re-read your Executive Summary After you've completed your executive summary, let it sit for a while before coming back to re-read it. Check to make sure that the summary will make sense as a separate document from the full research study. By taking some time before re-reading it, you allow yourself to see the summary with fresh, unbiased eyes.

Common Mistakes to Avoid

Length of the Executive Summary As a general rule, the correct length of an executive summary is that it meets the criteria of no more pages than 10% of the number of pages in the original document, with an upper limit of no more than ten pages [i.e., ten pages for a 100 page document]. This requirement keeps the document short enough to be read by your audience, but long enough to allow it to be a complete, stand-alone synopsis. Cutting and Pasting With the exception of specific recommendations made in the study, do not simply cut and paste whole sections of the original document into the executive summary. You should paraphrase information from the longer document. Avoid taking up space with excessive subtitles and lists, unless they are absolutely necessary for the reader to have a complete understanding of the original document. Consider the Audience Although unlikely to be required by your professor, there is the possibility that more than one executive summary will have to be written for a given document [e.g., one for policy-makers, one for private industry, one for philanthropists]. This may only necessitate the rewriting of the introduction and conclusion, but it could require rewriting the entire summary in order to fit the needs of the reader. If necessary, be sure to consider the types of audiences who may benefit from your study and make adjustments accordingly. Clarity in Writing One of the biggest mistakes you can make is related to the clarity of your executive summary. Always note that your audience [or audiences] are likely seeing your research study for the first time. The best way to avoid a disorganized or cluttered executive summary is to write it after the study is completed. Always follow the same strategies for proofreading that you would for any research paper. Use Strong and Positive Language Don’t weaken your executive summary with passive, imprecise language. The executive summary is a stand-alone document intended to convince the reader to make a decision concerning whether to implement the recommendations you make. Once convinced, it is assumed that the full document will provide the details needed to implement the recommendations. Although you should resist the temptation to pad your summary with pleas or biased statements, do pay particular attention to ensuring that a sense of urgency is created in the implications, recommendations, and conclusions presented in the executive summary. Be sure to target readers who are likely to implement the recommendations.

Bailey, Edward, P. The Plain English Approach to Business Writing . (New York: Oxford University Press, 1997), p. 73-80; Christensen, Jay. Executive Summaries Complete The Report. California State University Northridge; Executive Summaries. Writing@CSU. Colorado State University; Clayton, John. "Writing an Executive Summary That Means Business." Harvard Management Communication Letter , 2003; Executive Summary. University Writing Center. Texas A&M University;  Green, Duncan. Writing an Executive Summary.   Oxfam’s Research Guidelines series ; Guidelines for Writing an Executive Summary. Astia.org; Markowitz, Eric. How to Write an Executive Summary. Inc. Magazine, September, 15, 2010; Kawaski, Guy. The Art of the Executive Summary. "How to Change the World" blog; Keller, Chuck. "Stay Healthy with a Winning Executive Summary." Technical Communication 41 (1994): 511-517; The Report Abstract and Executive Summary. The Writing Lab and The OWL. Purdue University; Writing Executive Summaries. Effective Writing Center. University of Maryland; Kolin, Philip. Successful Writing at Work . 10th edition. (Boston, MA: Cengage Learning, 2013), p. 435-437; Moral, Mary. "Writing Recommendations and Executive Summaries." Keeping Good Companies 64 (June 2012): 274-278; Vassallo, Philip. "Executive Summaries: Todorovic, Zelimir William, PhD. and Frye, Marietta Wolczacka,B.A., B.B.A. "Writing Effective Executive Summaries: An Interdisciplinary Examination." United States Association for Small Business and Entrepreneurship, 2009; " Where Less Really is More." ETC.: A Review of General Semantics 60 (Spring 2003): 83-90 .

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  • How to write an executive summary, with ...

How to write an executive summary, with examples

Julia Martins contributor headshot

The best way to do that is with an executive summary. If you’ve never written an executive summary, this article has all you need to know to plan, write, and share them with your team.

What is an executive summary?

An executive summary is an overview of a document. The length and scope of your executive summary will differ depending on the document it’s summarizing, but in general an executive summary can be anywhere from one to two pages long. In the document, you’ll want to share all of the information your readers and important stakeholders need to know.

Imagine it this way: if your high-level stakeholders were to only read your executive summary, would they have all of the information they need to succeed? If so, your summary has done its job.

You’ll often find executive summaries of:

Business cases

Project proposals

Research documents

Environmental studies

Market surveys

Project plans

In general, there are four parts to any executive summary:

Start with the problem or need the document is solving.

Outline the recommended solution.

Explain the solution’s value.

Wrap up with a conclusion about the importance of the work.

What is an executive summary in project management?

In project management, an executive summary is a way to bring clarity to cross-functional collaborators, team leadership, and project stakeholders . Think of it like a project’s “ elevator pitch ” for team members who don’t have the time or the need to dive into all of the project’s details.

The main difference between an executive summary in project management and a more traditional executive summary in a business plan is that the former should be created at the beginning of your project—whereas the latter should be created after you’ve written your business plan. For example, to write an executive summary of an environmental study, you would compile a report on the results and findings once your study was over. But for an executive summary in project management, you want to cover what the project is aiming to achieve and why those goals matter.

The same four parts apply to an executive summary in project management:

Start with the problem or need the project is solving.  Why is this project happening? What insight, customer feedback, product plan, or other need caused it to come to life?

Outline the recommended solution, or the project’s objectives.  How is the project going to solve the problem you established in the first part? What are the project goals and objectives?

Explain the solution’s value.  Once you’ve finished your project, what will happen? How will this improve and solve the problem you established in the first part?

Wrap up with a conclusion about the importance of the work.  This is another opportunity to reiterate why the problem is important, and why the project matters. It can also be helpful to reference your audience and how your solution will solve their problem. Finally, include any relevant next steps.

If you’ve never written an executive summary before, you might be curious about where it fits into other project management elements. Here’s how executive summaries stack up:

Executive summary vs. project plan

A  project plan  is a blueprint of the key elements your project will accomplish in order to hit your project goals and objectives. Project plans will include your goals, success metrics, stakeholders and roles, budget, milestones and deliverables, timeline and schedule, and communication plan .

An executive summary is a summary of the most important information in your project plan. Think of the absolutely crucial things your management team needs to know when they land in your project, before they even have a chance to look at the project plan—that’s your executive summary.

Executive summary vs. project overview

Project overviews and executive summaries often have similar elements—they both contain a summary of important project information. However, your project overview should be directly attached to your project. There should be a direct line of sight between your project and your project overview.

While you can include your executive summary in your project depending on what type of  project management tool  you use, it may also be a stand-alone document.

Executive summary vs. project objectives

Your executive summary should contain and expand upon your  project objectives  in the second part ( Outline the recommended solution, or the project’s objectives ). In addition to including your project objectives, your executive summary should also include why achieving your project objectives will add value, as well as provide details about how you’re going to get there.

The benefits of an executive summary

You may be asking: why should I write an executive summary for my project? Isn’t the project plan enough?

Well, like we mentioned earlier, not everyone has the time or need to dive into your project and see, from a glance, what the goals are and why they matter.  Work management tools  like Asana help you capture a lot of crucial information about a project, so you and your team have clarity on who’s doing what by when. Your executive summary is designed less for team members who are actively working on the project and more for stakeholders outside of the project who want quick insight and answers about why your project matters.

An effective executive summary gives stakeholders a big-picture view of the entire project and its important points—without requiring them to dive into all the details. Then, if they want more information, they can access the project plan or navigate through tasks in your work management tool.

How to write a great executive summary, with examples

Every executive summary has four parts. In order to write a great executive summary, follow this template. Then once you’ve written your executive summary, read it again to make sure it includes all of the key information your stakeholders need to know.

1. Start with the problem or need the project is solving

At the beginning of your executive summary, start by explaining why this document (and the project it represents) matter. Take some time to outline what the problem is, including any research or customer feedback you’ve gotten . Clarify how this problem is important and relevant to your customers, and why solving it matters.

For example, let’s imagine you work for a watch manufacturing company. Your project is to devise a simpler, cheaper watch that still appeals to luxury buyers while also targeting a new bracket of customers.

Example executive summary:

In recent customer feedback sessions, 52% of customers have expressed a need for a simpler and cheaper version of our product. In surveys of customers who have chosen competitor watches, price is mentioned 87% of the time. To best serve our existing customers, and to branch into new markets, we need to develop a series of watches that we can sell at an appropriate price point for this market.

2. Outline the recommended solution, or the project’s objectives

Now that you’ve outlined the problem, explain what your solution is. Unlike an abstract or outline, you should be  prescriptive  in your solution—that is to say, you should work to convince your readers that your solution is the right one. This is less of a brainstorming section and more of a place to support your recommended solution.

Because you’re creating your executive summary at the beginning of your project, it’s ok if you don’t have all of your deliverables and milestones mapped out. But this is your chance to describe, in broad strokes, what will happen during the project. If you need help formulating a high-level overview of your project’s main deliverables and timeline, consider creating a  project roadmap  before diving into your executive summary.

Continuing our example executive summary:

Our new watch series will begin at 20% cheaper than our current cheapest option, with the potential for 40%+ cheaper options depending on material and movement. In order to offer these prices, we will do the following:

Offer watches in new materials, including potentially silicone or wood

Use high-quality quartz movement instead of in-house automatic movement

Introduce customizable band options, with a focus on choice and flexibility over traditional luxury

Note that every watch will still be rigorously quality controlled in order to maintain the same world-class speed and precision of our current offerings.

3. Explain the solution’s value

At this point, you begin to get into more details about how your solution will impact and improve upon the problem you outlined in the beginning. What, if any, results do you expect? This is the section to include any relevant financial information, project risks, or potential benefits. You should also relate this project back to your company goals or  OKRs . How does this work map to your company objectives?

With new offerings that are between 20% and 40% cheaper than our current cheapest option, we expect to be able to break into the casual watch market, while still supporting our luxury brand. That will help us hit FY22’s Objective 3: Expanding the brand. These new offerings have the potential to bring in upwards of three million dollars in profits annually, which will help us hit FY22’s Objective 1: 7 million dollars in annual profit.

Early customer feedback sessions indicate that cheaper options will not impact the value or prestige of the luxury brand, though this is a risk that should be factored in during design. In order to mitigate that risk, the product marketing team will begin working on their go-to-market strategy six months before the launch.

4. Wrap up with a conclusion about the importance of the work

Now that you’ve shared all of this important information with executive stakeholders, this final section is your chance to guide their understanding of the impact and importance of this work on the organization. What, if anything, should they take away from your executive summary?

To round out our example executive summary:

Cheaper and varied offerings not only allow us to break into a new market—it will also expand our brand in a positive way. With the attention from these new offerings, plus the anticipated demand for cheaper watches, we expect to increase market share by 2% annually. For more information, read our  go-to-market strategy  and  customer feedback documentation .

Example of an executive summary

When you put it all together, this is what your executive summary might look like:

[Product UI] Example executive summary in Asana (Project Overview)

Common mistakes people make when writing executive summaries

You’re not going to become an executive summary-writing pro overnight, and that’s ok. As you get started, use the four-part template provided in this article as a guide. Then, as you continue to hone your executive summary writing skills, here are a few common pitfalls to avoid:

Avoid using jargon

Your executive summary is a document that anyone, from project contributors to executive stakeholders, should be able to read and understand. Remember that you’re much closer to the daily work and individual tasks than your stakeholders will be, so read your executive summary once over to make sure there’s no unnecessary jargon. Where you can, explain the jargon, or skip it all together.

Remember: this isn’t a full report

Your executive summary is just that—a summary. If you find yourself getting into the details of specific tasks, due dates, and attachments, try taking a step back and asking yourself if that information really belongs in your executive summary. Some details are important—you want your summary to be actionable and engaging. But keep in mind that the wealth of information in your project will be captured in your  work management tool , not your executive summary.

Make sure the summary can stand alone

You know this project inside and out, but your stakeholders won’t. Once you’ve written your executive summary, take a second look to make sure the summary can stand on its own. Is there any context your stakeholders need in order to understand the summary? If so, weave it into your executive summary, or consider linking out to it as additional information.

Always proofread

Your executive summary is a living document, and if you miss a typo you can always go back in and fix it. But it never hurts to proofread or send to a colleague for a fresh set of eyes.

In summary: an executive summary is a must-have

Executive summaries are a great way to get everyone up to date and on the same page about your project. If you have a lot of project stakeholders who need quick insight into what the project is solving and why it matters, an executive summary is the perfect way to give them the information they need.

For more tips about how to connect high-level strategy and plans to daily execution, read our article about strategic planning .

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Grad Coach

How To Write A High-Impact Executive Summary

By Derek Jansen | January 2018

exec summary is your first impression

In this post, I’ll deconstruct the often-misunderstood executive summary and show you how to develop a high-impact executive summary for your assignment, research report or even your dissertation or thesis.

So, what is an executive summary?

An executive summary (sometimes called an abstract ) is quite simply a summary of summaries. In other words, an executive summary provides a concise summary of each of your assignment or report chapters/sections . More specifically, it should communicate the key points/insights/findings/suggestions from the following chapters:

  • Introduction
  • Recommendations
  • Implementation (if applicable)
  • Reflection (if applicable)

I’ll discuss which key points from each section need to be addressed a bit later. On a separate note – if you’re writing an executive summary for a dissertation or thesis, all of the concepts described in this post will still apply to you, however, you’ll include an additional paragraph about your methodology, and you’ll likely spend more word count discussing your analysis findings.

The 4 Important Attributes Of An Exec Summary

Before I discuss what goes into the executive summary, let’s quickly look at 4 attributes that make for a strong executive summary:

#1 – It should be able to stand alone.

The executive summary should be able to stand independently as an informative document . In other words, the reader should be able to grasp your broad argument without having to read the full document. Further reading should be purely for attaining more detail. Simply put, the executive summary should be a “Mini-Me” of the assignment.

This independence means that anything you write in the executive summary will need to be re-stated in the body of your assignment. A common mistake that students make is to introduce key points in the executive summary and then not discuss them again in the document – accordingly, the marker must view the main document as missing these key points. Simply put – make sure you discuss key points in both the executive summary and the main body . It will feel repetitive at times – this is normal.

Henley MBA Help

#2 – It should be written for the intelligent layman.

When crafting your executive summary, its useful to keep the intelligent layman front of mind. What I mean by this is that you should write your summary assuming that your reader (i.e. the marker) will be intelligent but won’t be familiar with your topic and/or industry. This means that you should explain any technical concepts, avoid jargon and explain acronyms before using them.

#3 – It should be concise.

Typically, your executive summary should be a one-pager (one and a half pages at worst). To summarise a 3000 – 5000-word document into one page is no easy task, so you’ll need to:

  • Present only the most important information (key insights, recommendations, etc).
  • Write concisely – i.e. with brevity and completeness.

To the first point, I’ll explain what the “most important” information is for each chapter shortly. To the second point (writing concisely), there are various ways to do this, including:

  • Using simple, straightforward language.
  • Using the active voice.
  • Removing bloaty adverbs and adjectives.
  • Reducing prepositional phrases.
  • Avoiding noun strings.

Does this sound like gibberish to you? Don’t worry! The Writing Center at the University of Wisconson-Madison provides a practical guide to writing more concisely, which you can download here.

On a related note, you typically would not include headings, citations or bulleted/numbered lists in your executive summary. These visual components tend to use a lot of space, which comes at a premium, as you know.

#4 – It should be written last.

Given that your executive summary is a summary of summaries, it needs to be written last , only once you’ve identified all your key insights, recommendations and so on. This probably sounds obvious, but many students start writing the summary first (potentially because of its position in the document) and then end up re-writing it multiple times, or they don’t rewrite it and consequently end up with an executive summary which is misaligned with the main document.

Simply put, you should leave this section until everything else is completed. Once your core body content is completed, you should read through the entire document again and create a bullet-point list of all the key points . From this list, you should then craft your executive summary . The approach will also help you identify gaps, contradictions and misalignments in your main document.

Dissertation Coaching

So, what goes into an executive summary?

Right, let’s get into the meat of it and consider what exactly should go into your executive summary. As I’ve mentioned, you need to present only the absolutely key point points from each of your chapters, but what does this mean exactly?

Each chapter will typically take the form of 1 paragraph (with no headings) in your executive summary. So, 5 chapters means 5 paragraphs. Naturally, some will be longer than others (let this be informed by the mark allocation), but assuming one page contains 500 words, you’re aiming for roughly 100 words per paragraph (assuming a 5-paragraph structure). See why conciseness is key!

Now, let’s look at what the key points are for each chapter in the case of a typical MBA assignment or report. In the case of a dissertation or thesis, the paragraph structure would still mimic the chapter structure – you’d just have more chapters, and therefore, more paragraphs.

Paragraph 1: Introduction

This paragraph should cover the following points:

  • A very brief explanation of the business (what does it do, for whom and where?).
  • Clear identification and explanation of the problem or opportunity that will be the focus of the assignment/report.
  • A clear statement of the purpose of the assignment (i.e. what research questions will you seek to answer?).
  • Brief mention of what data sources were utilised (i.e. secondary research) and any fieldwork undertaken (i.e. primary research ).

In other words, your first paragraph should introduce the business, the problem/opportunity to be addressed, why it’s important, and how you approached your analysis. This paragraph should make it clear to the reader what the assignment is all about at a broad level. Here’s a practical example:

This assignment focuses on ABC Ltd, a XXX business based in XXX, which provides XXX to XXX customers. To date, the firm has relied almost exclusively on XXX marketing channel. Consequently, ABC Ltd has little understanding of consumer segments, wants, and needs. This marketing channel is now under regulatory threat due to XXX.  The core challenge, therefore, is that whilst ABC Ltd seeks to grow its market share, it has little understanding of its market characteristics or competitive set, and its sole marketing channel under regulatory threat. Accordingly, the objective of this assignment is XXX. The assignment draws on survey, interview, and industry data.

Paragraph 2: Analysis and findings

In this paragraph, you should discuss the following:

  • What exactly did you analyse? For example, you might have analysed the macro context (i.e. PESTLE analysis), followed by the meso (i.e. competitor or industry analysis) and then the micro (i.e. internal organisational analysis).
  • What were your key findings in relation to the purpose of the assignment? For example, you may have identified 4 potential causes of a problem and would then state them.

In other words, your second paragraph should concisely explain what you analysed and what your main findings were . An example of this:

Segmentation analysis, consisting of macro, industry and firm-level analyses, revealed a strong segmentation variable in the form of XXX, with distinct needs in each segment. Macro analysis revealed XXX, while industry and firm-level analyses suggested XXX. Subsequently, three potential target segments were established, namely XXX, XXX and XXX.  These were then evaluated using the Directional Policy Matrix, and the results indicated XXX.

From a presentation perspective, you might structure this section as:

  • Analysis 1, findings from analysis 1.
  • Analysis 2, findings from analysis 2.
  • Analysis 3, findings from analysis 3.

Importantly, you should only discuss the findings that are directly linked to the research questions (i.e. the purpose of the assignment) – don’t digress into interesting but less relevant findings. Given that the analysis chapter typically counts for a large proportion of marks, you could viably write 2-3 paragraphs for this. Be guided by the mark allocation.

Lastly, you should ensure that the findings you present here align well with the recommendations you’ll make in the next paragraph. Think about what your recommendations are, and, if necessary, reverse engineer this paragraph to create a strong link and logical flow from analysis to recommendations.

exec summary components

Paragraph 3: Recommendations

With the key findings from your analysis presented in the preceding paragraph, you should now discuss the following:

  • What are your key recommendations?
  • How do these solve the problems you found in your analysis?
  • Were there any further conclusions?

Simply put, this paragraph (or two) should present the main recommendations and justify their use (i.e. explain how they resolve the key issue). As mentioned before, it’s critically important that your recommendations tightly align with (and resolve) the key issues that you identified in the analysis. An example:

Based on the Directional Policy Matrix analysis, it is recommended that the firm target XXX segment, because of XXX. On this basis, a positioning of XXX is proposed, as this aligns with the segment’s key needs. Furthermore, a provisional high-level marketing mix is proposed. The key aspects of the marketing mix include XXX, XXX and XXX, as these align with the firm’s positioning of XXX. By adopting these recommendations, the key issue of XXX will be resolved.

Also, note that (typically) the tone changes from past to present tense when you get to the recommendations section.

Paragraph 4: Implementation

If your assignment brief requires an implementation/project plan-type section, this paragraph will typically include the following points:

  • Time requirements (how long will it take?)
  • People requirements (what skills are needed and where do you find them?)
  • Money requirements (what budget is required?)
  • How will the project or change be managed? (i.e. project management plan)
  • What risks exist and how will these be managed?

Depending on what level of detail is required by your assignment brief, you may need to present more, less or other details in this section. As always, be guided by the assignment brief.

A practical example:

A high-level implementation plan is proposed, including a stakeholder analysis, project plan and business case. Resource requirements are presented, detailing XXX, XXX and XXX requirements. A risk analysis is presented, revealing key risks including XXX, XXX and XXX. Risk management solutions are proposed, including XXX and XXX.  

how to write an executive summary of a paper

Paragraph 5: Reflection

As with the implementation chapter, the need for a reflection chapter/section will vary between assignments and universities. If your assignment has this requirement, it’s typically good to cover the following points:

  • What were your key learnings? What were your ah-ha moments?
  • What has changed in the real world as a consequence of these learnings? I.e. how has your actual behaviour and approach to “X” changed, if any?
  • What are the benefits and/or disadvantages of this change, if any?

This section is very personal, and so each person’s reflections will be different. Don’t take the above points as gospel.

Time to test it out.

Once you’ve written up your executive summary and feel confident that it’s in good shape, it’s time to test it out on an unsuspecting intelligent layman. This is a critically important step, since you, as the writer, are simply too close to the work to judge whether it all makes sense to a first-time reader. In fact, you are the least suitable person on the planet!

So, find someone who is not familiar with your assignment topic (and ideally, not familiar with your industry), and ask them to have a read through your executive summary. Friends and family will usually tell you its great, regardless of the quality, so you need to test them on their understanding. Do this by asking them to give the details back to you in their own words. Poke and prod – can they tell you what the key issues and recommendations were (in their own words!). You’ll quickly spot the gaps this way, and be able to flesh out any weak areas.

  Wrapping up.

In this post, I’ve discussed how to write the all too often undercooked executive summary. I’ve discussed some important attributes of a strong executive summary, as well as the contents that typically go into it. To recap on the key points:

The key attributes of a high-impact executive summary:

  • It should be able to stand alone.
  • It should be written for the intelligent layman.
  • It should be concise.
  • It should be written last.

The key contents of a high-impact executive summary:

Each paragraph should cover a chapter from the document. For example, In the case of a typical assignment, it would be something like:

  • Summary of the introduction chapter.
  • Summary of the analysis chapter.
  • Summary of the recommendations and/or conclusions chapter.
  • Depending – summary of the implementation and reflection.

Lastly, don’t forget to test out your executive summary on an unsuspecting layman or two. This is probably the most important step of them all!

If you have any questions or suggestions, we’d love to hear from you. Please get in touch here or leave a comment below.

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Business General Guide (UNH Manchester): Writing Executive Summaries

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Executive Summaries

" T he executive summary is usually no longer than 10% of the original document. It can be anywhere from 1-10 pages long, depending on the report's length. Executive summaries are written literally for an executive who most likely DOES NOT have the time to read the original.

  • Executive summaries make a recommendation
  • Accuracy is essential because decisions will be made based on your summary by people who have not read the original
  • Executive summaries frequently summarize more than one document"

--Colorado State University: Writing @ CSU

Executive Summary Tips

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Being asked to write an executive summary, whether for a policy paper, pamphlet, briefing paper or report, may be a daunting prospect if you’ve never done it before.

However, ask a few questions, and keep a few simple rules in your mind and it becomes much more straightforward. This page sets out the questions to ask, whether of yourself or someone else, and a few warnings and conventions to bear in mind.

Executive Summary Content

Two key questions you need to ask before you start

  • Who is the intended audience of my executive summary?
  • Which of the contents of the paper that I am summarising do they really need to know?

These questions are important because they tell you what you need to include in the executive summary, so let’s unpack them a little:

The Intended Audience

As with all writing projects it is important to know your audience . The intended audience for an executive summary may be quite different from the intended audience for the longer document, whether it’s a policy paper, report, or something else.

The executive summary serves several possible purposes.

People may read the executive summary to find out if they need to read the full report. This group may include people within the organisation and outside, but the report is likely to touch on what they do every day. They will often be subject experts; they just need to know if there is anything new that they should read. This group will be looking for a broad summary of the contents of the wider paper.

People may want to find out if they’d find the full report interesting and relevant , even if not strictly essential. Again, this group is likely to be subject experts, but may also include analysts searching for a particular ‘angle’ on the subject. This group will also welcome a straightforward summary of the contents.

They may read the executive summary instead of the full report . It’s this group that you really need to worry about, because they’re likely to include the Board or executive team of your organisation, as well as journalists. What goes into the executive summary, therefore, is the message that they’re going to take away, that may well be spread more widely. For these people, the executive summary is their window onto the subject and it needs to be transparent, not opaque, if they are to understand it.

Think about your intended audience: who do you want to read your executive summary and why?

Don’t worry about other people who might read it; this is your intended audience , the people to whom you or your immediate line manager are going to send the summary. If the summary is for publication, which groups do you most want to read it?

What Does Your Intended Audience Need to Know?

Once you have identified your intended audience, you can then think about what they need to know or do as a result of reading your paper. This can be split into two parts:

First categorise the document by whether it needs action or is for information only. This will determine the language that you use.

Next, you need to identify what, when they have finished reading, are the key messages that you want your audience to have in their heads. Information and concepts that they did not have before.

Top Tip A good way to think about the key content is to imagine meeting your boss or CEO in the car park or at the coffee machine. What three key points about your document would you want to tell them?

Work on reducing your key messages down to three, or at the most, five bullet points of one or two sentences. Working on them before you start writing will mean that they are absolutely clear in your head as you write.

Writing your Executive Summary

Some organisations have very clear structures that are used for documents like executive summaries and others are more open.

Before you start, check whether you need to work within a specific structure or not. For example, if you are writing a summary of an academic report for submission, you may have a word count restriction, or need to remain within one side of paper.

When you are writing your executive summary, you should keep your intended audience in mind at all times and write it for them.

If your audience includes your boss or Chief Executive think: how much do they already know, and how much do you need to explain?

If your audience includes journalists, you probably need to explain everything. If it’s simply as a summary of a paper because you have to publish one, then you simply need to summarise the paper.

If you find yourself getting bogged down in the detail at this stage, it’s a good idea to talk to someone else about what to include.

The language you use needs to be fairly formal, whether or not the summary is intended for publication. If in doubt, check out our page: Formal and Informal Writing .

Broadly, an executive summary, as you might expect, summarises the main points of the underlying paper, and draws out the key points. It usually has three sections: introduction, main body and conclusion.

The introduction sets the scene, and explains what the paper is about, including what action needs to be taken as a result. It doesn’t need to be more than one or two sentences. For an internal paper, you might write:

This paper explains the findings of the research about [subject] and its relevance to the organisation. It notes five key findings, and makes three recommendations for action within the organisation. You are asked to take note of these, and decide whether the recommendations should be implemented.

For an executive summary of a published paper, it is not unusual for the first paragraph to be more attention-grabbing.

For example, from a recently-published report about green energy and the internet:

For the estimated 2.5 billion people around the world who are connected to the internet, it is impossible to imagine life without it. The internet has rewoven the fabric of our daily lives – how we communicate with each other, work and entertain ourselves – and become a foundation of the global economy.

[Source: Greenpeace, Clicking Clean ].

This example still sets the scene: the importance of the internet, but the idea here is to keep people reading, not just provide information. Again, it’s all about your audience and what they need or want.

The main body of the text outlines the key findings and/or recommendations from the report or paper to which this is the summary. The main section needs to focus on the interesting and most relevant bits of the report.

Most importantly, the main section of the executive summary needs to stand alone without the reader having to refer to the main body of the report or policy paper. This is worth checking by getting someone who doesn’t know much about the subject to read it over for you.

Finally, you need a conclusion , which outlines the take-home messages or action needed from the person reading the report. Bullet points are a useful form to highlight the key points, and this is where your three to five messages come in.

Once you’ve finished, check it against our checklist to make sure that you’ve covered everything.

Checklist for writing an executive summary

  • Have you kept in mind the audience at all times?
  • Have you addressed it to them?
  • Have you met any word count or structural requirements?
  • Have you clearly outlined the key messages and any action needed as a result?
  • Does the executive summary make sense by itself, without the report attached?

Final Words of Warning

An executive summary cannot be all things to all people. You only have a few hundred words. You need to focus firmly on your intended audience and their needs. Other people may find it useful; your intended audience relies on it.

Continue to: How to Write a Report How to Write a Business Case

See also: Commercial Awareness Employability Skills Note-Taking

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The Report Abstract and Executive Summary

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This resource is an updated version of Muriel Harris’s handbook Report Formats: a Self-instruction Module on Writing Skills for Engineers , written in 1981. The primary resources for the editing process were Paul Anderson’s Technical Communication: A Reader-Centered Approach (6th ed.) and the existing OWL PowerPoint presentation, HATS: A Design Procedure for Routine Business Documents.

The Abstract

The abstract is a crucial part of your report as it may be the only section read by people at the executive or managerial level who must make decisions based on what they read in your abstract. When you include specific content, it is important to remember these readers are looking for the information they need to make decisions.

The abstract is an overview that provides the reader with the main points and results, though it is not merely a listing of what the report contains. It is a summary of the essence of a report. For this reason, it should be crafted to present the most complete and compelling information possible. It is not a detective story building suspense as the reader hunts for clues, and should not be vague or obtuse in its content.

The abstract should include

  • Why the work was done (the basic problem), the specific purpose or objective, and the scope of the work if that is relevant. (College lab reports may not require this part of the abstract.)
  • How the work was done, the test methods or means of investigation
  • What was found—the results, conclusions, and recommendations

The abstract should

  • Not make references to material in the text
  • Not lose the message by burying the methods, results, conclusions, and recommendations in a sea of words
  • Not be written before the rest of the report

Therefore, a good abstract is

  • Self-sufficient

Evaluating abstracts

Because the abstract is of major importance in a report, a summary of effective qualities of abstracts is offered here.

A well-written abstract

  • Considers the readers it will encounter
  • States what was done and what results were found
  • Avoids vagueness by stating specific results
  • Uses past tense to report what was done
  • Is informative
  • Is self-sufficient and does not refer to the body of the report
  • Makes concrete, useful recommendations

Below are two abstracts. The first one, (A), was written by a student for a lab report, and the other one (B) was a revision written by someone with more experience in writing abstracts. Read both versions and try to figure out why the changes were made in B.

We studied the flow characteristics of meters, valves, and pipes that constitute a flow network. The meter coefficients for orifice and venture meters were determined. The orifice and venture coefficients were, on the average, 0.493 and 0.598, respectively. Fanning friction factors for pipes of different sizes and for gate and globe valves were also determined.

The accuracy with which the meter coefficients and friction factors were determined was affected by leaks in the piping network. In addition, air bubbles trapped in the pipes and manometers affected the accuracy with which pressure drops were measured. Hence, it is recommended that the piping system be checked to ensure the absence of any leaks. Furthermore, the fluid should be allowed to flow in the network for some time before taking any measurements, in order to get rid of the air trapped in the pipes and manometer.

In an orifice and a venturimeter in a flow network, we measured the meter coefficients to be 0.5 0.1 and 0.6 0.15. We measured the Fanning friction factors at steady state for several pipes and for gate and globe valves. The most important source of error was a leak in the piping network which has to be repaired in order to obtain more precise results.

The Executive Summary

The government and some companies have begun to request executive summaries at the beginning of a long report. An executive summary is a one-page statement of the problem, the purpose of the communication, and a summary of the results, conclusions, and recommendations. The same considerations of readers and situation should guide your executive summaries.

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Video styles and samples, celebrating educators – the whole hearted school counseling teacher appreciation giveaway, pick of the week: robot turtles – a fun and educational board game, we found all the best classroom deals in prime early access sale, the ultimate digital pen tablet giveaway: a dream come true for creatives, win a trip to orlando for carnegie learning’s teaching excellence institute, should i transfer to another grade level weighing the pros and cons, the teacher report: at-home activities that keep students learning, principal helpline: “everyone wants answers immediately, how to write an executive summary for a research paper (with template).

how to write an executive summary of a paper

Writing an executive summary for a research paper can be a daunting task for many students. Fortunately, there are some steps you can take to make the process easier. By following some simple tips and using a template, you can write an effective executive summary for your research paper.

First, it is important to understand what an executive summary is. An executive summary is a short overview of a research paper’s main points. It should provide readers with a brief description of the paper’s purpose, main findings, and conclusions. The executive summary should not include any new information or data; instead, it should serve as a summary of the paper’s key points.

When writing the executive summary, it is important to use the same language and tone that was used in the research paper. This will ensure that the executive summary is a cohesive and effective summary of the paper’s main points.

It is also important to keep the executive summary brief. You should strive to make the executive summary no longer than one page long. This will ensure that readers are able to quickly understand the main points of your paper without having to read through a long and complex document.

Before writing the executive summary, you should read through the entire research paper. This will ensure that you have a clear understanding of the paper’s main points and that you capture them effectively in the executive summary.

To help you write an effective executive summary, you might find it helpful to use a template. Below is an example of an executive summary template for a research paper:

[Paper Title]

This paper examines [brief description of paper’s main points]. The research found that [main finding]. It was concluded that [conclusion].

Based on these findings, it is recommended that [recommendation].

Overall, the research shows that [summary of main findings]. This paper provides valuable insight into [brief description of research’s purpose].

By following these guidelines and using a template, you can write an effective executive summary for your research paper. Writing an executive summary can be a daunting task, but with the right steps and guidance, it can be a simple and straightforward process.  

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How to Write an Executive Summary (Example & Template Included)

ProjectManager

Here’s the good news: an executive summary is short. It’s part of a larger document like a business plan, business case or project proposal and, as the name implies, summarizes the longer report.

Here’s the bad news: it’s a critical document that can be challenging to write because an executive summary serves several important purposes. On one hand, executive summaries are used to outline each section of your business plan, an investment proposal or project proposal. On the other hand, they’re used to introduce your business or project to investors and other stakeholders, so they must be persuasive to spark their interest.

Writing an Executive Summary

The pressure of writing an executive summary comes from the fact that everyone will pay attention to it, as it sits at the top of that heap of documents. It explains all that follows and can make or break your business plan or project plan . The executive summary must know the needs of the potential clients or investors and zero in on them like a laser. Fortunately, we’ll show you how to write and format your executive summary to do just that.

Getting everything organized for your executive summary can be challenging. ProjectManager can help you get your thoughts in order and collaborate with your team. Our powerful task management tools make it easy to get everything prioritized and done on time. Try it free today.

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What Is an Executive Summary?

An executive summary is a short section of a larger document like a business plan , investment proposal or project proposal. It’s mostly used to give investors and stakeholders a quick overview of important information about a business plan like the company description, market analysis and financial information.

It contains a short statement that addresses the problem or proposal detailed in the attached documents and features background information, a concise analysis and a conclusion. An executive summary is designed to help executives and investors decide whether to go forth with the proposal, making it critically important. Pitch decks are often used along with executive summaries to talk about the benefits and main selling points of a business plan or project.

Unlike an abstract, which is a short overview, an executive summary format is a condensed form of the documents contained in the proposal. Abstracts are more commonly used in academic and research-oriented writing and act as a teaser for the reader to see if they want to read on.

how to write an executive summary of a paper

Get your free

Executive Summary Template

Use this free Executive Summary Template for Word to manage your projects better.

How to Write an Executive Summary

Executive summaries vary depending on the document they’re attached to. You can write an executive summary for a business plan, project proposal, research document, or business case, among other documents and reports.

However, when writing an executive summary, there are guidelines to ensure you hit all the bases.

Executive Summary Length

According to the many books that have been written about executive summaries, as well as training courses, seminars and professional speakers, the agreed-upon length for an executive summary format should be about five to 10 percent of the length of the whole report.

Appropriate Language

The language used should be appropriate for the target audience. One of the most important things to know before you write professionally is to understand who you’re addressing. If you’re writing for a group of engineers, the language you’ll use will differ greatly from how you would write to a group of financiers.

That includes more than just the words, but the content and depth of explanation. Remember, it’s a summary, and people will be reading it to quickly and easily pull out the main points.

Pithy Introduction

You also want to capture a reader’s attention immediately in the opening paragraph. Just like a speech often opens with a joke to break the tension and put people at ease, a strong introductory paragraph can pull a reader in and make them want to read on. That doesn’t mean you start with a joke. Stick to your strengths, but remember, most readers only give you a few sentences to win them over before they move on.

Don’t forget to explain who you are as an organization and why you have the skills, personnel and experience to solve the problem raised in the proposal. This doesn’t have to be a lengthy biography, often just your name, address and contact information will do, though you’ll also want to highlight your strengths as they pertain to the business plan or project proposal .

Relevant Information

The executive summary shouldn’t stray from the material that follows it. It’s a summary, not a place to bring up new ideas. To do so would be confusing and would jeopardize your whole proposal.

Establish the need or the problem, and convince the target audience that it must be solved. Once that’s set up, it’s important to recommend the solution and show what the value is. Be clear and firm in your recommendation.

Justify your cause. Be sure to note the key reasons why your organization is the perfect fit for the solution you’re proposing. This is the point where you differentiate yourself from competitors, be that due to methodology, testimonials from satisfied clients or whatever else you offer that’s unique. But don’t make this too much about you. Be sure to keep the name of the potential client at the forefront.

Don’t neglect a strong conclusion, where you can wrap things up and once more highlight the main points.

Related: 10 Essential Excel Report Templates

What to Include in an Executive Summary

The content of your executive summary must reflect what’s in the larger document which it is part of. You’ll find many executive summary examples on the web, but to keep things simple, we’ll focus on business plans and project proposals.

How to Write an Executive Summary for a Business Plan

As we’ve learned above, your executive summary must extract the main points of all the sections of your business plan. A business plan is a document that describes all the aspects of a business, such as its business model, products or services, objectives and marketing plan , among other things. They’re commonly used by startups to pitch their ideas to investors.

Here are the most commonly used business plan sections:

  • Company description: Provide a brief background of your company, such as when it was established, its mission, vision and core values.
  • Products & services: Describe the products or services your company will provide to its customers.
  • Organization and management: Explain the legal structure of your business and the members of the top management team.
  • SWOT analysis: A SWOT analysis explains the strengths, weaknesses, opportunities and threats of your business. They describe the internal and external factors that impact your business competitiveness.
  • Industry & market analysis: This section should provide an overview of the industry and market in which your business will compete.
  • Operations: Explain the main aspects of your business operations and what sets it apart from competitors.
  • Marketing plan: Your marketing plan describes the various strategies that your business will use to reach its customers and sell products or services.
  • Financial planning: Here, you should provide an overview of the financial state of your business. Include income statements, balance sheets and cash flow statements.
  • Funding request: If you’re creating your business plan to request funding, make sure to explain what type of funding you need, the timeframe for your funding request and an explanation of how the funds will be used.

We’ve created an executive summary example to help you better understand how this document works when using it, to sum up a business plan.

To put all of that information together, here’s the basic format of an executive summary. You can find this same information in our free executive summary template :

  • Introduction, be sure to know your audience
  • Table of contents in the form of a bulleted list
  • Explain the company’s role and identify strengths
  • Explain the need, or the problem, and its importance
  • Recommend a solution and explain its value
  • Justify said solution by explaining how it fits the organization
  • A strong conclusion that once more wraps up the importance of the project

You can use it as an executive summary example and add or remove some of its elements to adjust it to your needs. Our sample executive summary has the main elements that you’ll need project executive summary.

Executive summary template for Word

Executive Summary Example

For this executive summary example, we’ll imagine a company named ABC Clothing, a small business that manufactures eco-friendly clothing products and it’s preparing a business plan to secure funding from new investors.

Company Description We are ABC Clothing, an environmentally-friendly manufacturer of apparel. We’ve developed a unique method of production and sourcing of materials that allows us to create eco-friendly products at a low cost . We have intellectual property for our production processes and materials, which gives us an advantage in the market.

  • Mission: Our mission is to use recycled materials and sustainable methods of production to create clothing products that are great for our customers and our planet.
  • Vision: Becoming a leader in the apparel industry while generating a positive impact on the environment.

Products & Services We offer high-quality clothing products for men, women and all genders. (Here you should include pictures of your product portfolio to spark the interest of your readers)

Industry & Market Analysis Even though the fashion industry’s year-over-year growth has been affected by pandemics in recent years, the global apparel market is expected to continue growing at a steady pace. In addition, the market share of sustainable apparel has grown year-over-year at a higher pace than the overall fashion industry.

Marketing Plan Our marketing plan relies on the use of digital marketing strategies and online sales, which gives us a competitive advantage over traditional retailers that focus their marketing efforts on brick-and-mortar stores.

Operations Our production plant is able to recycle different types of plastic and cotton waste to turn it into materials that we use to manufacture our products . We’ve partnered with a transportation company that sorts and distributes our products inside the United States efficiently and cost-effectively.

Financial Planning Our business is profitable, as documented in our balance sheet, income statement and cash flow statement. The company doesn’t have any significant debt that might compromise its continuity. These and other financial factors make it a healthy investment.

Funding Request We’re requesting funding for the expansion of our production capacity, which will allow us to increase our production output in order to meet our increasing customer demand, enter new markets, reduce our costs and improve our competitiveness.

If you’d like to see more executive summary examples for your business plan, you can visit the U.S. small business administration website. They have business plans with executive summary examples you can download and use.

Executive summaries are also a great way to outline the elements of a project plan for a project proposal. Let’s learn what those elements are.

How to Write an Executive Summary for a Project Proposal

An executive summary for your project proposal will capture the most important information from your project management plan. Here’s the structure of our executive summary template:

  • Introduction: What’s the purpose of your project?
  • Company description: Show why you’re the right team to take on the project.
  • Need/problem: What is the problem that it’s solving?
  • Unique solution: What is your value proposition and what are the main selling points of your project?
  • Proof: Evidence, research and feasibility studies that support how your company can solve the issue.
  • Resources: Outline the resources needed for the project
  • Return on investment/funding request: Explain the profitability of your project and what’s in for the investors.
  • Competition/market analysis: What’s your target market? Who are your competitors? How does your company differentiate from them?
  • Marketing plan: Create a marketing plan that describes your company’s marketing strategies, sales and partnership plans.
  • Budget/financial planning: What’s the budget that you need for your project plan?
  • Timeline: What’s the estimated timeline to complete the project?
  • Team: Who are the project team members and why are they qualified?
  • Conclusions:  What are the project takeaways?

Now that we’ve learned that executive summaries can vary depending on the type of document you’re working on, you’re ready for the next step.

What to Do After Writing an Executive Summary

As with anything you write, you should always start with a draft. The first draft should hit all the marks addressed above but don’t bog yourself down in making the prose perfect. Think of the first draft as an exploratory mission. You’re gathering all the pertinent information.

Next, you want to thoroughly review the document to ensure that nothing important has been left out or missed. Make sure the focus is sharp and clear, and that it speaks directly to your potential client’s needs.

Proofread for Style & Grammar

But don’t neglect the writing. Be sure that you’re not repeating words, falling into cliché or other hallmarks of bad writing. You don’t want to bore the reader to the point that they miss the reason why you’re the organization that can help them succeed.

You’ve checked the content and the prose, but don’t forget the style. You want to write in a way that’s natural and not overly formal, but one that speaks in the manner of your target audience . If they’re a conservative firm, well then, maybe formality is called for. But more and more modern companies have a casual corporate culture, and formal writing could mistakenly cause them to think of you as old and outdated.

The last run should be proofing the copy. That means double-checking to ensure that spelling is correct, and there are no typos or grammatical mistakes. Whoever wrote the executive summary isn’t the best person to edit it, however. They can easily gloss over errors because of their familiarity with the work. Find someone who excels at copy-editing. If you deliver sloppy content, it shows a lack of professionalism that’ll surely color how a reader thinks of your company.

Criticism of Executive Summaries

While we’re advocating for the proper use of an executive summary, it’d be neglectful to avoid mentioning some critiques. The most common is that an executive summary by design is too simple to capture the complexity of a large and complicated project.

It’s true that many executives might only read the summary, and in so doing, miss the nuance of the proposal. That’s a risk. But if the executive summary follows the guidelines stated above, it should give a full picture of the proposal and create interest for the reader to delve deeper into the documents to get the details.

Remember, executive summaries can be written poorly or well. They can fail to focus on results or the solution to the proposal’s problem or do so in a vague, general way that has no impact on the reader. You can do a hundred things wrong, but if you follow the rules, then the onus falls on the reader.

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Because project managers and teams work differently, our software is flexible. We have multiple project views, such as the kanban board, which visualizes workflow. Managers like the transparency it provides in the production cycle, while teams get to focus only on those tasks they have the capacity to complete. Are you more comfortable with tasks lists or Gantt charts? We have those, too.

A screenshot of the Kanban board project view

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You’ve now researched and written a persuasive executive summary to lead your proposal. You’ve put in the work and the potential client sees that and contracts you for the project. However, if you don’t have a reliable set of project management tools like Gantt charts , kanban boards and project calendars at hand to plan, monitor and report on the work, then all that preparation will be for nothing.

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how to write an executive summary of a paper

Writing to the board

How to write an executive summary for your board report – and why you should [with examples]

David cameron.

12 minute read

A man sitting at a desk in his home office. He's holding a report document and looking at his laptop.

Congratulations: you’ve written your board report. That’s the good news. The not-so-good? Well, unless it’s only a page or two long, I have to tell you that you’re not finished yet.

Now you need to write the executive summary: in other words, a condensed version of your full document. This is (arguably) the most important part of your entire report.

Why write an executive summary?

As we’ve said before , your job as board-report writer is to make it as easy as possible for the board to take the decision you want them to take. The executive summary plays a key role here.

A good executive summary will begin to guide your board towards making that decision for you. A really good one will guide their decision from reading the summary alone.

What’s an executive summary for?

Remember that your board members are busy people. They are unlikely to have very much time to devote to your report. They may not have the time (or, forgive them, the inclination) to read the whole thing.

That’s where the executive summary comes in. It’s there for the people who can’t (or won’t) read the whole report. An effective one gives those readers enough information to form an opinion or commit to an action.

What goes in an executive summary?

Most importantly, your executive summary needs to stand alone. Your reader must be able to read it and get all your most important points, your conclusions, and your recommendations.

To make sure you’re including the most significant, relevant points – and only those points – go back to your reader-profiling exercise from writing the full report . You’ll have asked yourself questions like ‘Who will read the paper?’ ‘What do they want from it?’ and most importantly, ‘How interested are they?’

Identify the crucial information

Now go further: where specifically do their interests lie and why? What motivates or drives them, if you know (or can find out)?

Maybe one is a financial director whose priority would be maximising profits or making savings – and specifically how much could be made or saved over what period. Meanwhile, your director of operations will be focused on resources and delivery.

What about any non-executive directors? They may have a remit to question your ideas from a strategic perspective. What will they be looking for? A link to a corporate plan or vision, maybe? Maybe you have a data-driven director who would be moved by a very specific statistic or a well-chosen graph or graphic.

Don’t include too much detail. You should aim to get your summary onto one side of paper – that’s about 400 words. And you should be clear in the summary about what you want the board to do after they’ve read it (or your full paper).

And write your executive summary after you’ve finished your report. It’s easier to whittle down a well-planned and structured report into a succinct summary than to try to summarise up front and then expand into a report.

So, how do you structure the executive summary?

If your board report template comes with a structure for your executive summary too, use it. It may not suit your report, but it’s how your board want it. Or you may have a structured report template but more freedom to write the summary in the order you choose. In this case, you may find one of the following structures works well.

And if you have no template and free rein over how you write both elements, it’s worth mirroring the same structure in the report and summary.

A simple executive summary structure

For a simple summary of a report, you could try this:

  • Purpose: start with the purpose of your report and remind the reader of what the piece of work you are reporting on is. Keep this part very short: three or four sentences at most.
  • Background or methods: Either the background or what you did. Again, three or four sentences at most. Don’t tell the story of your project.
  • Findings and conclusions: What you found out and/or the conclusions you came to. You might need a little more detail here but still keep it short.
  • Recommendations: Your way forward and what you want the board to endorse.

This structure could work like this:

The purpose of this report Our Gidgetty Widget production line cannot keep up with demand. This report updates you on our progress in updating and improving the line. We ask you to endorse the proposal under ‘The way forward’ below. Our investigation We looked at the current production line and where we can improve productivity. We also looked at current and potential demand for Gidgetty Widgets over the next five years. What we discovered There is scope to expand production on our line to meet current demand. However, Gidgetty Widget demand is likely to grow at 10% a year and we will not be able to keep up with demand in 2025. We need to outsource production to a third party or build a new production line. The quickest and most cost-effective method is to outsource. The way forward We believe that we should outsource production to one or more third parties. We ask the board to allow us to share our intellectual property with suitable companies so that we can plan and cost for the future.

However, if you’re feeling bolder, you may find one of the following structures even more effective.

Sad but true: the board may not read the full report you slaved over. So make sure the executive summary can do the job by itself. Here’s how, via @EmphasisWriting Click To Tweet

Persuade with the Four Ps

If you want to make a case for something or persuade the board, you could use the 4Ps. The 4Ps is a persuasive summary structure in four parts, each of which begins with the letter P.

It looks like this:

  • Position: start with where you are now. A simple statement will do. You don’t want anything controversial here. The point is to get the reader nodding from the outset.
  • Problem: give the reason that you can’t stay where you are. This is the reason that you’re writing to the board in the first place. It could be an opportunity rather than a problem.
  • Possibilities: outline all the ways to address the problem or seize the opportunity. This can include the option of doing nothing, which is always a possibility.
  • Proposal: define your suggested way forward with your reasoning.

The summary above would look like this if we used the 4Ps:

The current situation Annual turnover from sales of Gidgetty Widgets is £50 million. We sell them in 44 different countries. Our production line produces 500,000 Gidgetty Widgets every year. We can’t keep up with demand We currently sell every widget we can produce. Our demand forecasts suggest that we need to increase production to 700,000 a year by 2025 or we will lose sales to our competitors. Three ways forward 1. We can expand our production line. We believe that we can increase production to 650,000 a year by 2023. However, we cannot expand production beyond this level. This would be affordable but will limit us to a maximum production of 650,000 widgets a year. By 2024 we will be unable to meet demand. 2. We can outsource extra production to third-party companies. This has the advantage of being cheaper than the cost of upgrading our production line and is also scalable to meet future demand. The disadvantage is that we will have to share our IP with third-party companies, which is a significant risk. 3. We could continue production at current levels and accept that we will not be able to meet demand. This has the advantage of costing nothing. But we will be at risk of our competitors launching products to compete with Gidgetty Widgets. Our recommendations We should outsource production. We will be able to keep up with all future demand and we believe that we can mitigate the risks to our IP.

The 4Ps is a great way to take your readers through your thinking. It works well for summaries, and it’s especially good for presentations and pitches.  

Try putting your request first

If you don’t need to persuade but only to ask for a decision, you could try putting what you’re asking for first, like this:

This paper asks the board to approve sharing our Gidgetty Widget IP with external suppliers so that we can outsource production in the future.

Then follow with a summary of your background , reasoning , and costings .

This structure gets straight to the point when you are asking the board to agree to a decision or course of action. It makes it clear up front what the purpose of your paper is and what you are asking for. It may seem a little blunt and to the point but, remember, your board members are busy. They may appreciate your clarity and directness.

Or start with your update’s purpose

Sometimes we have to write to the board to update them on something we’re working on. This may include asking them to approve some additional work. In this case, try leading with why you’re writing to them and what you want them to do, like this:

This paper updates the board with our progress on updating our Gidgetty Widget production line. It includes budget reports, schedule updates and projected production start dates. We ask the board to approve the schedules and budgets for the next 12 months.

  This tells the board exactly what to expect from the report in the first two sentences. You should then follow it with your summary updates for spending, scheduling and so on. You will have included the detail in the full report that follows.

This structure also works for regular update reports where you are simply reporting on progress and not asking the board to do anything other than note what you are telling them. In this case, you might want to pull out the highlights of what has changed since the last update, and put them up front in your executive summary.

Clarity matters

Never be vague in a board report – or the summary document. If you are unclear about aspects of your project, you should make it clear that there are areas of ambiguity in your report. Or you should resolve the ambiguities before you write anything.

Assume that some of your readers do not share your knowledge and expertise. Be careful with jargon and explain all abbreviations and acronyms when you use them the first time, no matter how familiar they may seem to you. As always, board reports are no place for fashionable business speak. So avoid talk of deep dives , pivoting , circling back , and the new normal , please.

Formatting your executive summary

As with your full report, use formatting to make your document even more readable. Allow white space with decent-sized margins, and don’t use a tiny font to fit more text on the page (don’t go lower than 11-pt).

A few well-placed subheadings will help your reader to navigate your summary. Make sure they’re engaging rather than generic, and that they tell the reader something about what follows. Instead of ‘Background’ try ‘How did we get here?’, for example.

If you have a list, try using bullet points. But only use bullet points once in a one-page summary.

And always take the time to proofread the summary (and report). Your reputation is on the line here. Don’t do yourself the disservice of leaving a howling typo where all the board can see it.

Don’t forget

It’s likely that more people will read your executive summary than will read the whole board report. That makes it one important page of text.

Make sure your executive summary can do the job you need it to by carefully selecting the critical information that your board members need to understand the project or situation or to take a decision. As we’ve said before, this means thinking carefully about your specific reader(s) and what will matter most to them.

Keep it short and keep it as simple as you can make it. Be clear and be precise. As always, make your board’s job easy for them.  

  Looking to train up a team in writing reports the board will want to read? Have a look at our in-house Writing exceptional board reports training , and get in touch if you’d like to talk with us about tailoring it to your needs.

Image credit: fizkes  /  Shutterstock

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David wrote his first organisational policy more than 25 years ago and wishes he’d known then what he knows now about creating them. After over 25 years working in the communications departments of international charities and large organisations, he now trains and develops learning programmes for Emphasis. He has written for and worked with organisations including Amnesty International, the National Trust and the NHS, creating and implementing style and tone-of-voice guides, and developing and delivering business-writing training. These years of experience have given David an understanding of the key role an organisation's culture plays in developing its people – and their business-writing skills.

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How to Write an Executive Summary for a Research Paper

6 December 2023

last updated

When people work on organizing their research papers, they need effective guidelines on how to write an executive summary. This article provides insights students should grasp to create high-standard texts, including defining what is an executive summary, its meaning, and its basic structure. About the structure, the guideline teaches students all the sections of an executive summary (introduction, purpose statement, methods, findings, recommendations, limitations, implementation, and conclusion), the contents of each part, and how to write each element. Other insights include 20 tips for producing a high-standard executive summary, including 10 dos and 10 don’ts. Lastly, the article gives a sample outline template for writing a good executive summary and a practical example of this section of a research paper.

How to Write an Outstanding Executive Summary for a Research Paper & Examples

A habit of reading different types of papers is helpful to students’ mental preparation for course assessments but, more importantly, to their intellectual development. Reading various types of essays , reports, and research papers also induces the mental faculties of intellect, reason, imagination, and intuition, which are essential for academic discourse. Indeed, one can tell a writer who reads habitually by how they construct and defend arguments and ideas in their works. Basically, this guideline for writing an effective executive summary includes essential insights into what students should and should not do when writing this type of academic document. The article also defines what is an executive summary and its meaning, outlines its distinctive features, shows how to write each part of this section of a research paper , explains concepts, and gives helpful tips for producing a high-standard document. In turn, this guideline gives a sample outline of a project paper and an example of an executive summary.

How to Write an Executive Summary for a Research Paper & Examples

Definition of What Is an Executive Summary and Its Meaning

From a simple definition, an executive summary is a text that accounts for the main points of a longer text, mainly a market study report, project report, and business proposal. In this respect, it serves the same purpose as an abstract , the only difference being that it is not used in research papers. Ideally, an abstract is a short and descriptive section of the essential details of a research paper, such as background, methodology, results , and conclusion . In contrast, an executive summary means writing a comprehensive overview of a report, research proposal , or project that explains the main points, including recommendations. Practically, an abstract is between 0.5-1 page, while an executive summary is about 5-10% of the document’s total word count. Since the purpose of an executive summary is to summarize the entire research paper comprehensively, it precedes the introduction of a report, proposal, or business plan.

Distinctive Features of an Executive Summary

An executive summary is identifiable by specific features that distinguish it from other texts, including essays and research papers. Essentially, all scholarly documents require the same level of mental preparation by writers to produce high-quality work. However, students must understand that some papers are demanding because of their contents, which underscore the basic essay outline . The main contents that earmark the distinctive features of an executive summary are an introduction, a purpose statement, methods, findings, recommendations, limitations, an implementation plan, and a conclusion.

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1️⃣ Introduction

The introduction of an executive summary highlights the document’s topic, which emphasizes the type of paper it is, such as a business proposal, project report, or market research report. In this respect, it must be short and precise. Because the focus is the topic, one should use a bridge sentence or short paragraph for the introduction.

2️⃣ Purpose Statement

The purpose statement of an executive summary communicates the document’s primary objective. In this respect, it provides a brief background of the topic to enhance the reader’s understanding of the essence of the document. The language in this part reflects an expected end, while common terms include ‘aim,’ ‘goal,’ ‘purpose,’ or ‘objective.’

3️⃣ Methods

In an executive summary, methods outline the writer’s approach to achieving the primary objective, such as examining official data, conducting a field study, reviewing the literature, or interviewing stakeholders. Students need to understand that this component differs from the research methodology of research papers. In this respect, it does not detail the methods one has used to complete the work. In essence, it outlines the strategies that help writers to better understand critical issues, such as challenges to a sector, stakeholder sentiments, industry insights, or potential barriers.

4️⃣ Findings

Findings in an executive summary are the outcomes of the methods, meaning it is what the writer has discovered about an issue, such as an industry, stakeholders, or a project. This component is crucial to readers because it offers a sneak peek into the outcomes that underscore the primary purpose of the entire document: project report, market research report, or business proposal.

5️⃣ Recommendations

Recommendations in an executive summary underscore the writer’s perspective regarding the issues that a research paper addresses as a challenge or problem. For example, if the paper is a report about healthcare status, the challenges or problems it identifies may be nursing shortages or medical errors. The recommendations should highlight what stakeholders, like the government and health institutions, must do to overcome these challenges or problems. In other words, the recommendations address what must be done to rectify a situation or make it possible to achieve specific outcomes.

6️⃣ Limitations

Like a research paper, an executive summary must point out the limitations that the document’s author encountered in reporting about a project or business plan. For example, these limitations may include a lack of goodwill among stakeholders, sufficient time to investigate a matter, or resources to execute the task. This information is essential to the audience because it indicates the dynamics influencing the primary objective.

7️⃣ Implementation Plan

The implementation plan is the component in an executive summary that provides a framework for adopting and implementing the recommendations. Typically, this information includes claims and activities, people responsible, the timeframe, and budget allocation. Sometimes, an evaluation plan is also part of the implementation plan.

8️⃣ Conclusion

The conclusion part of an executive summary is a call to action about the project report, market research report, or business proposal. Unlike conclusion examples in other academic papers and essays that summarize the paper’s main points, the conclusion of an executive summary gives a direction about the document. Essentially, writers use this component to call to action the audience to adopt the recommendations or compel stakeholders to adopt a particular perspective. In turn, it persuades the audience to adopt a particular stance regarding the report or proposal.

The Length of an Executive Summary

Students should know the length of each of the above sections, except the introduction and conclusion parts, depending on the document’s total length, which determines the word count of an executive summary. For example, a long and robust project report or business proposal requires a long executive summary with an extended purpose statement, methods, findings, recommendations, limitations, and implementation, which means the length of 4-10 double spaced pages, or 2-5 single spaced pages, or 1000–2500 words, depending on the volume of the work. Typically, the introduction and conclusion sections take a statement or short paragraph of 0.5-1 double spaced page or 125-250 words, irrespective of a research paper or executive summary’s length. However, if a research paper is a long work of more than 10 double spaced pages, 5 single spaced page, or 2500 words, the introduction and conclusion parts should not exceed 5-10% of the whole word count. Besides, the body section of an executive summary must take 80-90% of the total word count of a research paper, not less. The word count of a title page, a table of contents , an abstract, a reference page, and appendix is not considered since these parts are technical and do not mean writing itself.

How to Write Each Section of an Executive Summary for a Research Paper

Writing an executive summary requires students to demonstrate an understanding of its purpose. This understanding means students should know when to write it, what to talk about, and how to write each of the sections above. Therefore, writing an executive summary is essential to approach carefully and with the utmost focus.

1️⃣ Writing an Executive Summary as a Last Action

Because an executive summary overviews the entire research paper, students should write this part after finishing their market research reports, project reports, or business proposals. However, one should read and reread the whole research paper to know the most significant points forming part of the summary. By writing an executive summary as a last item, one can have a mental picture of what to address to give the audience a comprehensive sneak peek into a research paper document.

2️⃣ Making Notes of Important Aspects

While reading and rereading a research paper, students should take notes of the most critical aspects of their work that must appear in an executive summary. These aspects must address each section above. Moreover, one should identify crucial information in an introduction, a purpose statement, methods, findings, recommendations, limitations, an implementation plan, and a conclusion.

A. Writing an Introduction Part of an Executive Summary

When writing a college essay introduction , students must refrain from going into details about the purpose of the text because they will have an opportunity to do so later. While one may mention the document’s background, one should make it concise to contextualize the topic. The most crucial detail is that the introduction part of an executive summary should be a sentence or brief paragraph.

B. Writing a Purpose Statement Part of an Executive Summary

When writing the research paper’s purpose, students should communicate the type of document, such as a business proposal, a market research report, or a project report. The next thing is to state the background; provide the reason for writing, like sourcing funds; recommend solutions; or report progress and challenges. However, one should avoid going into detail because they will do so later in an executive summary of a research paper.

C. Writing a Methods Part of an Executive Summary

When writing a methods section, one should focus on giving the audience a sense of the strategy that helps achieve the outcomes. However, writers should approach this part differently than the methodology section of a research paper. Instead, they should mention what they did to execute the work, such as interviewing stakeholders or analyzing official data. The best way to approach this section is to list everything one did to make a research paper.

D. Writing a Findings Part of an Executive Summary

Since the purpose of the findings section in a research paper is to narrate outcomes, students should write it in the past tense. Therefore, when writing this section of an executive summary, authors should see themselves as reporters educating the audience about what they have learned in executing the task. An essential detail students should note when writing the section is to refer to credible sources of information that lead to the findings. These reliable sources can be documents, organizations, individuals in leadership, or industry experts.

E. Writing a Recommendations Part of an Executive Summary

When writing a recommendations section in an executive summary for a research paper, students should focus on giving a clear summary of what should happen after the findings. Essentially, one should address the key decision-makers or stakeholders because they are responsible for creating change through policy. The best approach to writing recommendations is to interrogate each challenge or problem and related findings to understand what must happen to create positive outcomes.

F. Writing a Limitations Part of an Executive Summary

The best approach to writing a limitations section in an executive summary for a research paper is to interrogate the challenges one has faced in the project, such as a lack of goodwill among stakeholders or sufficient time, resources, or support. Ideally, writers aim to inform the audience of the factors that have complicated their work or may complicate the implementation of the recommendations.

G. Writing an Implementation Plan Part of an Executive Summary

When writing an implementation plan in an executive summary, students should focus on telling the audience the procedure for actualizing the recommendations. In this respect, the best approach to writing this section is to interrogate the recommendations to determine what must happen to actualize each. For example, some issues to consider may include people in charge of implementation, such as an organization’s human resource director, the time it would take to actualize (timeline), the budget, and how to measure success (evaluation).

H. Writing a Conclusion Part of an Executive Summary

When writing a conclusion part, students should aim to persuade the audience to adopt a particular stance regarding a research paper or proposal. Although one might reiterate the topic, it is not necessary to mention each of the preceding sections. Instead, writers should focus on sending a strong communication regarding it. The best approach to writing the conclusion section is to influence the audience’s perspective on the topic and the recommendations and implementation.

3️⃣ Explaining Acronyms, Abbreviations, and Key Terms

Since an executive summary is an overview of a market research paper, project report, or business plan, authors should write it clearly and precisely. The best approach is to use simple language and define all acronyms, abbreviations, and key terms. In turn, students should not assume that readers know what each acronym, abbreviation, and key term means when they read research papers.

4️⃣ Proofreading, Revising, and Editing an Executive Summary Section of a Research Paper

After completing writing a research paper, students should proofread it to identify grammatical and formatting mistakes and inconsistent arguments and ideas. For example, the best way to fix these mistakes and flaws is to revise the whole research paper by fixing mistakes, like missing punctuation and wrong citations, and editing it by adding or deleting words and sentences to create a logical order of thoughts and ideas. In turn, writers must be factual, not use word count fillers, and avoid unnecessary repetitions. Besides, students should know that the audience is not interested in stories but in factual communication that makes logical sense.

Sample Paper Template for Writing a Good Executive Summary

Like essays, executive summaries have a specific structure students should demonstrate in their writing. The sections above underscore this outline template, meaning students should know what each section of writing an executive summary for a research paper entails and how to write it. The best way to write a high-quality executive summary is to create a template and populate it with ideas for a project, a business plan, a proposal, or a report. This preparation helps writers to have a mental picture of the kind of document they want to have and the right attitude when writing.

I. Introduction: [Introduce the topic and state the kind of document, such as a market research paper, project report, or business plan].

II. Purpose Statement: [Explain the primary objective of a research paper, such as investigating a problem, souring some funds, or reporting its progress].

III. Methods: [Enumerate how the task is accomplished, such as examining official data, interviewing stakeholders, or reviewing the literature].

IV. Findings: [Provide the outcomes of the methods, such as what official data reveals, stakeholders’ sentiments, or what research says].

V. Recommendations: [State clearly what stakeholders or key decisions must do to address the challenges or problems that the findings reveal].

VI. Limitations: [Discuss the challenges or problems that were encountered in completing the task, such as poor time management, a lack of support, or absent goodwill by stakeholders].

VII. Implementation Plan: [Include what stakeholders or key decision-makers must do to actualize the recommendations, such as identifying a person responsible and establishing a budget and timeline].

VIII. Conclusion: [Persuade the audience to adopt the recommendations and work toward creating change by facilitating an implementation plan].

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Example of an Executive Summary for an 8000-Word Research Paper

Topic: A Need for Proactive Climate Change Initiatives

I. Example of an Introduction Section in an Executive Summary

Stakeholders in the climate change discourse must shift focus from discourse to practical, proactive measures to demonstrate seriousness in tackling the biggest threat of the millennium.

II. Example of a Purpose Statement Section in an Executive Summary

The purpose of writing this executive study is to examine the status of the climate change discourse, interrogate dynamics that make it unpromising as a practical solution to the crisis, and recommend what stakeholders must do to restore hope to millions globally who are afraid that climate change poses the biggest threat to the existence of current and future generations.

III. Example of a Methods Section in an Executive Summary

An executive report employs several data-gathering methods to achieve these objectives, including examining the climate change discourse over the decades to identify key themes: environmental policies, greenhouse gases, industrial pollution, natural disasters, weather forecasts, and others. Another method is interrogating research and official data on climate change by government agencies in the last three decades. The report also considers interviews with environmentalists, social justice advocates, government officials, and leaders of organizations that dedicate their mission to creating awareness about the need for environmental conservation and preservation.

IV. Example of a Findings Section in an Executive Summary

Overall, the methods above reveal worrying findings about the climate change discourse:

  • Human activities, including industries and deforestation, have increased global warming to 1.1 degrees C, triggering unprecedented changes to the Earth’s climate. The lack of consensus on reversing human-induced global warming among the most industrialized countries suggests that the trend will worsen in the coming decades.
  • The impacts of climate change are evident on people and ecosystems. Without urgent practical interventions, these impacts will become more widespread and severe with every additional degree of global warming.
  • Developing and implementing adaptation measures in communities can effectively build and foster the resilience of people and ecosystems. However, stakeholders must interrogate their climate change funding priorities for effective proactive interventions.
  • Communities will continue recording climate-induced losses and damages as long as communities cannot adapt to some impacts of this global problem. An example is 1.1 degrees C of global warming.
  • Projections indicate global greenhouse gas (GHC) emissions will peak at 1.5 degrees C before 2025 in selected at-risk pathways.
  • Burning fossil fuels remains the leading cause of the global climate crisis.
  • Carbon removal is the most effective and practical solution to limiting global warming from peaking at 1.5 degrees C.
  • There is a lack of commitment by key stakeholders to finance climate change mitigation and adaptation.
  • Climate change and the collective efforts to mitigate and adapt to its impacts will exacerbate global inequity if stakeholders do not prioritize just transition.

These findings of a research paper confirm that the climate change discourse is alive to the threat the global problem poses to people and ecosystems and the weaknesses in the current interventions.

V. Example of a Recommendations Section in an Executive Summary

This executive report recommends that key stakeholders, including governments, communities, policy experts, and financiers, must adopt to prioritize practical solutions to the global climate crisis.

  • Stakeholders must target a net-zero climate-resilient future through urgent, systemwide transformations.
  • Adopt policies that enhance access to fresh produce by establishing a relationship between farmers and consumers.
  • Improve awareness about the critical benefits of organic foods.
  • Consider policies that promote regenerative farm practices to eliminate toxins and revitalize soils.
  • Create infrastructures for transforming waste into compost manure for farm use.
  • Develop policies that encourage communities to embrace a green neighborhood.

VI. Example of a Limitations Section in an Executive Summary

This executive report recognizes several limitations that have made the fight against climate change unproductive and threaten current and future endeavors to arrest the crisis. For example, stakeholders need to note that these limitations may undermine the implementation of the recommendations in this report. One limitation is a lack of goodwill among key stakeholders. The four leading industrial powers, namely the United States, China, India, and Brazil, contribute to significant global atmospheric temperature increases. Traditionally, these countries have refused to agree on how to cut back on industries primarily because they are the main drivers of their economies. Another limitation is the mis-prioritization of financing, where much focus is on theoretical interventions, such as agreements and seminars, at the expense of practical solutions like building infrastructures for transforming waste into usable products. While stakeholders agree on the essence of the 3R (reuse, reduce, and recycle) framework, there is little practical implementation at the community level.

VII. Example of an Implementation Plan Section in an Executive Summary

The implementation plan for the recommendations above recognizes government agencies as the most suitable implementers because official bodies are the key stakeholders who finance climate change initiatives. The business plan considers that, to shift the climate change fight from mere discourse to practical evidence, stakeholders must prioritize the following:

  • A budget of at least $50 million annually at the country level;
  • A period of between 2-5 years; and
  • Periodic evaluation of progress through at least one annual seminar or conference.

VIII. Example of a Conclusion Section in an Executive Summary

This executive research paper calls on all stakeholders in the climate change discourse to reconsider the current focus by recognizing its failure to create meaningful change as evidence shows the crisis continues to worsen. Instead, they should focus on practical, proactive interventions focusing on communities because that is where much environmental damage happens. It is also where the adversities of the crisis manifest most powerfully.

4 Easy Steps for Writing an Executive Summary

Writing an executive summary is a technical undertaking requiring writers to consider each section’s basic structure and essential details. When writing a research paper, one must know when to write each section and what to say. In this respect, preparation, stage setup, writing a first draft of an executive section, and wrap-up are essential steps students should follow to produce a research paper document that meets quality standards.

Step 1: Preparation

As the first step in writing an executive summary, preparation helps writers to develop a proper mindset that involves knowing the basic structure and what to write in each section of a research paper. Therefore, the critical task for students in this stage is constructing the basic structure and stating what must happen in each section.

Step 2: Stage Setup

Setting up the stage is the second step in writing an executive summary. It involves reading and rereading the document to identify critical details to address in each section of the basic structure. The best approach to achieve this outcome is to make notes of the most vital data when reading a research paper.

Step 3: Writing a First Draft of an Executive Summary

The third step is to create a first draft of an executive summary by putting all the critical data into relevant sections. Ideally, people must start with a clear introduction where they point out the focal point of a research paper and then move to a study’s purpose statement, methods, findings, recommendations, limitations, implementation plan, and conclusion. Each research section must summarize and not explain the most critical data.

Step 4: Wrap-Up

Wrapping a first draft into a final version of a research paper is the last step in writing an executive summary. This stage involves proofreading, revising, and editing a first version of an executive summary to eliminate grammar mistakes and inconsistent statements. As a result, authors must perfect their executive summaries of research papers by fixing errors and flaws that affect the logical progression of ideas and thoughts and the overall quality of the text.

20 Tips for Writing an Effective Executive Summary

Writing an executive summary can be demanding, particularly for students who do not prepare well or do not know what is most important. The following tips can be helpful: begin an executive summary by explaining why the topic is important; state the purpose of a research paper by outlining the problem and why it is essential or relevant to the audience; explain the methods that help to execute the task; state the findings; enumerate the limitations by addressing dynamics that undermine the implementation of solutions; consider the recommendations and list them using numbers or bullet points; outline an implementation plan that identifies the person or entity that oversee the implementation, the budget allocation, and how to evaluate progress; and write a conclusion that persuades the audience to adopt a particular perspective about the topic. In turn, 10 dos and 10 don’ts that writers should consider when writing their executive summaries in their research papers are:

10 things to do when writing an executive summary include:

  • reading a research paper thoroughly to identify the primary objective, methods for collecting data, key findings, recommendations, significant limitations, and an implementation strategy;
  • considering the audience of an executive summary to determine whether to use simple or technical language;
  • writing formally and avoiding jargon;
  • outlining the structure that considers all the main sections (introduction, purpose statement, methods, key findings, recommendations, limitations, implementation, and conclusion);
  • organizing an executive summary in a summary format;
  • using a short, clear, precise, and captivating opening statement to hook readers;
  • including each section to state the most critical details;
  • focusing on summarizing a research paper rather than explaining its contents;
  • reviewing a research paper for incorrect information;
  • proofreading, revising, and editing an executive summary to eliminate all mistakes.

10 things not to do when writing an executive summary include:

  • using jargon to simplify complex terms and phrases;
  • explaining rather than summarizing a research paper;
  • creating too many grammar mistakes, such as missing punctuation and confusing words with a similar pronunciation;
  • ignoring the basic outline of an executive summary;
  • writing a lengthy introduction;
  • concentrating on some sections more than others;
  • explaining ideas or concepts not discussed in the main research paper;
  • providing a very short or long summary that does not align with the document’s total word count;
  • beginning an executive summary with anecdote or irrelevant information;
  • placing an executive summary at the end of a research paper.

Summing Up on How to Write a Perfect Executive Summary

  • Tell an interesting story. Writers should approach an executive summary as a platform for inducing the reader’s interest in reading a research paper. As such, one should use each section to tell what is most crucial to the audience.
  • Highlight critical data. Writers should focus on what is most critical in each section of an executive summary, emphasizing statistical data because it is visually captivating.
  • Maintain a formal tone from beginning to end. Writers should avoid using jargon to simplify complex concepts or terminologies.
  • Write an executive summary after completing an actual research paper. Writing an executive summary as the last element of a research paper helps one to approach this paper as a final summary of the main points. In turn, the mistake of starting an executive summary before writing an actual research paper is that authors can write about details they fail to address in the final version of a document.

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A guide to writing an executive summary.

A person searches on their computer for information on how to write an executive summary.

Business leaders are busy — they like to know what they’re getting into before diving in. An executive summary is a great way to introduce a larger business document that will keep all parties interested and invested. If you’re looking to convey the most important aspects of a business plan or report, learning how to write an executive summary is crucial.

What is an executive summary?

An executive summary is a brief description (summarization) of a larger business document, and it’s usually the very first section of the paper. It can even be a standalone presentation or document you send to interested parties in hopes of encouraging them to take a more in-depth look at the rest of your document. An executive summary can span anywhere from a few paragraphs to several pages.

The goal is to summarize each section of the document that follows and provide any key information about the company, project, individual, or department. That way, you can share your executive summary with others to entice them to read the rest of your paper.

Why writing an executive summary is important.

An executive summary is an essential component of writing any business document, from business plans to research reports. Here are some of the key reasons why an executive summary is beneficial for different types of reports:

  • Business plans. An executive summary in a business plan provides an overview of the business, including its objectives, target market, competition, financial projections, and growth plans. The summary helps investors and stakeholders quickly understand the business idea, evaluate its potential, and make informed decisions.
  • Research reports. It’s essential to have an executive summary in a research report to summarize the research objectives, methodology, findings, and recommendations. The summary helps readers to quickly understand the research topic and its significance, assess the credibility of the research, and apply the insights to their work.
  • Proposals. In a proposal, an executive summary summarizes the proposal’s key features, benefits, costs, and expected outcomes. The summary helps decision-makers quickly understand the proposal’s value proposition, assess its feasibility, and make informed decisions.
  • White papers. An executive summary in a white paper provides an overview of the problem, the proposed solution, and the benefits of the solution. The summary helps readers quickly understand the issue, assess the credibility of the solution, and determine if the paper is worth reading in full.

Options: How to write an executive summary.

Now that you understand what should be included in an executive summary, let’s explore how to write one effectively. Depending on the type of document you’re creating, there are specific strategies to keep in mind. Below are some tips on how to write an executive summary for a proposal, business or marketing plan, and a research paper or case study.

How to write an executive summary for a proposal.

  • Understand the purpose of your proposal, and tailor your executive summary format to that purpose.
  • Begin with an attention-grabbing statement that summarizes the main idea of your proposal.
  • Summarize the key features of your proposal, including the problem it addresses, the proposed solution, the benefits of the solution, and the costs and timeline involved.
  • Emphasize the unique aspects of your proposal and the advantages it has over competing proposals.
  • Include a call to action that encourages readers to take the next step, whether it’s accepting the proposal or scheduling a meeting to discuss it further.

How to write an executive summary for a business or marketing plan.

  • Understand the purpose of your business or marketing plan, and tailor your executive summary format to that purpose.
  • Begin with an attention-grabbing statement that summarizes the main idea of your plan.
  • Summarize the key points. Your key points could include your target market, marketing objectives, strategies, tactics, budget, competition, financial projections, and growth plans.
  • Emphasize the unique aspects of your business or marketing plan and the advantages it has over competing plans.
  • Include a call to action that encourages readers to take the next step, whether it’s approving the plan, investing in your business, or requesting more information.

How to write an executive summary for a research paper or case study.

  • Understand the purpose of your research paper or case study, and tailor your executive summary format to that purpose.
  • Begin with an attention-grabbing statement that summarizes the main idea of your case study or research paper.
  • Summarize the key points. For a research paper, the key points should include the research objectives, methodology, findings, and recommendations. For a case study, the key points should include the problem, solution, implementation, and results.
  • Emphasize the unique aspects of your case study or the significance of your research and its potential impact on the field.
  • Include a call to action that encourages readers to take the next step, whether it’s applying the lessons learned to their own situation, requesting more information citing your research, or conducting further research on the topic.

What to include in an executive summary.

Since you can use executive summaries in a wide variety of applications, there’s not always a standard format to follow. When writing an executive summary, consider which information to include based on the type of executive summary. For example, resume executive summaries might have different information than a business proposal executive summary.

Despite the variety, most executive summaries should cover at least a few key components:

  • Summary or mission statement
  • Problems and solutions
  • Background information
  • Market research
  • Business model
  • Financial information
  • Recommendations to proceed

Some of these sections might not be relevant to your particular document, so you’re welcome to add or remove sections as needed. Just make sure you do include your paper, proposal, or resume’s essential information.

How long should an executive summary be?

The length of an executive summary can vary depending on the purpose and type of document it is summarizing. As a general rule of thumb, an executive summary should be no longer than 10% of the length of the entire document. For example, if your business plan is 20 pages long, your executive summary should be no more than 2 pages.

However, for certain types of documents, such as research papers or case studies, the executive summary may be shorter, spanning only a paragraph or two. The length of an executive summary for these types of documents will depend on the complexity and length of the original document, as well as the intended audience.

When is writing an executive summary beneficial?

Knowing when to write an executive summary can be as important as knowing how. A variety of business documents use executive summaries. Here are just a few of the most common applications:

  • Business proposals
  • Financial reports
  • Sales reports
  • Marketing proposals
  • Professional resumes

If you have a business document that’s several pages long, it’s not a bad idea to include an executive summary to let interested parties know what the document is about before they take time out of their busy schedules to read it in its entirety. A summary can be especially useful when pitching your ideas to potential investors or clients.

Executive summary template.

Here is an example template for an executive summary:

Introduction Briefly introduce the purpose of the document and provide context for the reader.

Table of contents Write these in the form of a bulleted list.

Problem or opportunity Identify the problem or opportunity the document addresses and why it’s important.

Solution or recommendation Provide an overview of the proposed solution or recommendation.

Benefits Describe the potential benefits of the proposed solution or recommendation.

Market analysis (for business plans and marketing proposals) Discuss the target market, competition, and marketing strategy.

Financial analysis (for business plans and financial reports) Present financial projections and analyses, including cash flow, profit and loss, and balance sheets.

Conclusion Summarize the key points of the document and emphasize why the proposed solution or recommendation is the best option.

Call to action Encourage the reader to take the next step, whether that be to invest in the business, approve the proposal, or continue reading the full document.

This is just an example template. The sections can be modified to fit the specific needs of the document. The key is to ensure that the executive summary provides a clear and concise overview of the larger document, highlighting the most important points and encouraging the reader to continue reading.

Making your document presentable when writing an executive summary.

An executive summary is, in a sense, a kind of presentation. To make it more engaging, many professionals choose to make executive summary presentations in Microsoft PowerPoint. That way, you can formally present the information and increase interest from all parties.

PowerPoint files aren’t always great for sharing. If you need to email or share your executive summary presentation, it’s best to convert your PowerPoint to a PDF first using an online PDF editor like the one found in Adobe Acrobat online services. How? Simply follow these three easy steps:

  • Visit the PDF converter tool.
  • Upload your executive summary PowerPoint.
  • Download your converted PDF file.

Once downloaded, you can send your PDF executive summary to anyone without worrying about formatting or compatibility issues, no matter which device they use.

More resources on business reports and documents.

Now that you’ve learned how to write an executive summary, here are more resources on business reports and documents:

  • Learn how to digitize documents .
  • Learn how to collaborate on documents .
  • Learn how to send secure PDFs.
  • Learn how to redline a document before signing.

Discover what more you can do with Acrobat online services to simplify business document creation and management.

how to write an executive summary of a paper

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The Two Types of White Paper Executive Summary

And when to use each one.

There’s a long-running debate in the content marketing community on the wisdom of using an executive summary at the beginning of a white paper.

The Two Types of Executive Summary and When to Use Each

Others think an executive summary has the opposite effect. This side says an executive summary creates disincentives to read the full white paper: it adds unnecessary length and gives away too much information about the solution. These opponents argue a white paper should begin with a brief introduction rather than an executive summary.

I say the issue is overblown.

It’s a non-issue, really. The controversy, I believe, stems from a misunderstanding between the two camps of what each of them means when they say, “executive summary.”

That’s because there are really two types of executive summary – or "introduction," if you prefer – that can help increase the readership and improve the impact of your white papers. But neither one is right for every white paper. Each works better than the other in specific situations.

We’ll examine the two types – and when you should use each – in a moment. But first, let’s define what we mean by they term “executive summary” and look at why you should use one.

What is an executive summary?

An executive summary, or management summary as its sometimes called, is a single page (or less) summary of the content of your white paper.

A good executive summary will usually start by indicating the audience for which the white paper is intended – by industry and usually by job responsibility – and the business problem to be addressed. It will typically conclude by promising the reader useful, problem-solving information on a new and better way to solve the subject business problem.

Why use an executive summary?

An executive summary is so named, because its purpose is to allow time-pressed executive readers to become rapidly familiar with the content of your white paper without having to read it all.

Corporate decision makers desperately need solid information to solve their business problems and make the right buying decisions. But they’re extremely busy. They don’t have nearly enough time to read everything that comes across their desks. There’s simply too much. They need to make quick, smart decisions on how to most effectively use their valuable and very limited reading time.

That’s why using an executive summary is a smart idea.

An executive summary makes it easy for busy decision makers to determine if your white paper contains the answers they need – answers to their pressing business questions. It supports a quick decision on whether to read your white paper, pass it down the chain of command, or toss it in the recycling bin. If those decision makers can’t quickly find the answers they need, they’ll most likely trash your white paper without reading it.

But besides helping the reader, an executive summary also benefits you, the marketer.

As mentioned, some marketers fear the presence of an executive summary will create a disincentive for readers, make them overconfident they already have the answers they need, and thus dissuade them from reading further.

In reality, the opposite is true.

If your executive summary is well written – if it is succinct and to the point and answers the reader’s basic business questions – it will provide an incentive to read further . If your reader is indeed a prospect for your product or service – if he or she has a strong desire to solve the problem addressed in your white paper – a well-written executive summary will most likely pique his or her interest and create desire to read your white paper and gain more detailed information.

Also bear in mind that your executive summary is the first page of your white paper that your reader will encounter. If it's well written, it will likely be read in its entirety and give your prospect a positive first impression . And a positive first impression greatly increases the chances your white paper will create a positive and lasting overall impression .

The Two Types of Executive Summary

As I mentioned earlier, there are basically two types of executive summary that work well for white papers. Each is effective, but in different circumstances. The two types are the Preview and the Synopsis .

  • The Preview

The Preview executive summary is kind of like a movie trailer. It tries to whet one's appetite – get the audience excited by revealing the conflict and suspense in the story – without giving away the ending.

As such, the Preview is very problem-oriented . By that I mean that this type of executive summary dwells more on the problem than on the solution. It will promise a solution at the end, but like the movie trailer, it doesn't want to give away the ending. It's purpose is to get the target audience to read the white paper, so they'll gain a better understanding of the problem and be better able to appreciate your new and better solution.

Preview executive summaries are typically structured as follows:

  • Market Conditions. The conditions that brought about the problem. The situation the target reader finds himself in today as related to the problem being addressed.
  • Problem Assessment. A summary of the problem or problems that need to be resolved, what is preventing them from being resolved, and why existing solutions are failing.
  • Ramifications and Repercussions. A brief summary of the costs associated with the problem: the costs of persisting with outdated solutions or failing to address the problem.
  • Promise of a Better Solution. A brief, final paragraph that offers the reader hope in the form of an emerging solution that will truly resolve the problem.
  • The Synopsis

If the Preview is like a movie trailer, the Synopsis executive summary is, as the name implies, more like a plot synopsis or movie review, the type you see prefaced with the phrase ***SPOILER ALERT!*** on IMDB.com and Amazon.com. The Synopsis summary strikes a more even balance between problem and solution, compared to the more problem-oriented Preview style.

The Synopsis is the type of executive summary some marketers shy away from. They're afraid if they "give away the ending," their prospects won't read their white paper. But there are some situations where the Synopsis works better, as I'll explain in a moment.

The Synopsis executive summary takes the following form, similar to that of a case study:

  • The Situation. Summarizes what is causing the problem. This is like the Market Conditions portion in the Preview style, but usually more concise.
  • The Problem. Highlights the primary business and/or technical challenges related to the Situation and addresses the inadequacy of existing solutions. Briefer than the Problem portion in the Preview style it leaves more room for the remaining points.
  • The Solution. States the recommended solution in one of two ways, either generically or by the use of a brand name. This portion is usually very brief and segues directly into the Results.
  • The Results. Highlight what's to be gained by implementing the new solution in terms of business benefits: reduced costs, better allocation of resources, labor savings, faster time to market, etc.

When to Use Each Type of Executive Summary

As mentioned earlier, each of the two types of executive summary is superior to the other in different circumstances. And luckily, the difference in those circumstances is pretty clear cut.

In fact, they correspond to the difference between the two styles.

When to use the Preview

The Preview style, as you'll recall, is the more problem-oriented of the two executive summary styles. That makes the Preview style better suited to white papers targeting prospects early in the sales cycle .

Early in the sales cycle, in what some may call the pre-sales or educational portion of the cycle, prospects aren't ready to hone in on specific solutions. They're more interested on learning more about the nature of the problem they face – which they may not yet fully grasp – and the range of possible solutions available.

Classic problem/solution white papers and numbered list white papers tend to work best in this educational phase of the cycle, because they place emphasis on the problem to be solved, and offer more product-agnostic problem-solving information. The Preview exec summary works well for these types of white papers, because it gets prospects' heads nodding, "Yes, this is the problem I have, I need to find out more about it."

When to use the Synopsis

Later in the sales cycle , when prospects fully understand their problem and are evaluating specific solutions, the Synopsis style makes the better choice.

In the evaluation phase of the sales cycle, prospects are more interested in specific product details and the results they bring. Product backgrounder white papers focus on this type of information, and the Synopsis executive summary lets executive readers know right up front that's the kind of information they're going to find in the white paper.

Take-away Points

  • An executive summary is a single-page summary of a white paper's contents designed to aid busy executives in making a decision on whether or not to read your white paper.
  • Supports a quick time-investment decision by the reader.
  • Creates a positive first impression of your white paper.
  • Gives prospects an incentive to read further.
  • Problem/solution and numbered list white papers
  • The early (educational) phases of the sales cycle
  • Backgrounder white papers
  • The later (solution evaluation) phases of the sales cycle

Next Steps...

Need some help putting together a new white paper that executives will really read? Call CopyEngineer at (+39) 011 569 4951 . Or drop me an email at [email protected] .

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How to write an executive summary: Templates and examples

how to write an executive summary of a paper

Imagine you are a CEO or chief product officer (CPO) with a day full of meetings, business agreements, and high-level initiatives to manage.

How To Write An Executive Summary: Templates And Examples

At the same time, you have to review market research and usability testing reports your team has come up with. Not to mention signing off on any big feature initiatives that require significant investments and thus executive approval.

Does that leave you enough time to go through a 100-page report detailing the minutiae of your team’s operations and every bit of data that went into each and every decision? Of course not! This is where an executive summary comes in handy.

What is an executive summary?

An executive summary (ES) is a high-level document or paragraph written as part of a report or a handout that summarizes the critical information of a specific project or feature.

The executive summary, also called the speed read or management summary, is written specifically to provide key stakeholders, such as C-suite executives, senior managers, and investors, with a very abstract and holistic understanding of what is going on.

The executive summary can be a great way for product managers to secure buy-in quickly from upper management and other stakeholders.

Executive summary vs. project overview

Before we delve deeper into executive summaries for product managers, we should note some important differences between an executive summary and a project overview.

Executive summary examples and templates

In product management, you’ll come across various situations that require you to prepare and present an executive summary. Each scenario calls for a different format.

Below are some examples of reports that require executive summaries when presenting to senior stakeholders:

Product updates

Investor pitch, annual or quarterly product review.

After one or more development cycles have been executed and release is imminent, the product manager may need to write an executive summary to communicate any fundamental changes in the product, such as new features, UI/UX enhancements, and fixed bugs.

An executive summary for product updates should be written in straightforward language with minimal jargon. For a clean, succinct format, use the following template:

  • Problem — (Describe the problem you solved) 
  • Change — (Describe the solution you came up with)
  • Problem — (Describe the problem you solved)

In some early-stage startups, product managers represent the voice of the market and customers. As such, they are often tasked with writing investor pitches.

how to write an executive summary of a paper

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how to write an executive summary of a paper

In this case, the product manager should prepare a handout with slides along with an executive summary page. The executive summary should include the following details on a single page:

  • Target user problems
  • Summary of your competitive edge
  • What is your solution?
  • Total addressable market (TAM)
  • Return on investment (ROI)

Product managers in large corporations often need to write an annual or quarterly product review report that details the critical performance of the product, including key objectives, improved or declined product metrics, notable achievements, and obstacles faced during a given time span.

For a periodic product review, you should prepare an executive summary of only one paragraph, stating the improved and declined metrics and linking them with the reasons behind success and failure.

How to write an executive summary

There’s no broad, established template for writing an executive summary because the requirements differ based on your function, role, project, goal, and situation. However, any executive summary should include the following components:

In product management alone, you will be using at least three different executive summaries in multiple situations. However, all of them should include some components. Those components are:

  • State the problem
  • Propose a solution
  • Summarize the impact

1. State the problem

The executive summary should always start by detailing a problem. This problem should be evidenced and supported by either qualitative or quantitative data.

In our recent product analytics report, we discovered that it takes the user at least seven hours to place an order after initiating a search session. This is damaging our monthly conversion rates.

2. Propose a solution

The executive summary should outline a clear solution. It should be focused on persuading the reader that you chose the right solution. As always, the best way to do that is to include hard data as evidence that your solution is viable.

Based on our latest design sprint and our user testing, we believe that building an integrated recommendation system into our search function will decrease the time to place an order from search by 20 percent. This is because we uncovered the highest drop-off rate happens when there are no results available.

3. Summarize the impact

The final section should include the achieved impact (if you are sharing it in a product update) or the expected impact (if it is a feature proposal like in the example above). In this section, you should also restate any significant takeaways from your executive summary.

Finally, based on our extensive research, we believe that building the recommendation with some search enhancements, such as search results filters and sorting, will not only help decrease the time to place an order from a search by 20 percent, but will also increase the basket size by 27 percent. For more information, go through our design sprint, user research synthesis, and product requirement documentation.

Executive summary checklist

Below is a checklist that you can use to evaluate your executive summary and make sure it’s compelling and practical before you present it to stakeholders. If you can answer “yes” to each question, your executive summary is in good shape:

  • Does it have a clear opening statement packed with data? E.g., In recent user interviews we ran, 60 percent of our interviewed users explicitly mentioned the need for new payment methods
  • Does it mention the problem that you want executives to consider?
  • Does it describe the solution you and your product team are proposing?
  • Is it contained to no more than two pages?
  • Does it use clear and simple language?
  • Was it reviewed by another product manager or product associate?

Final thoughts

An executive summary is an essential tool for product managers to communicate various aspects of product development effectively to senior executives at all stages of product development. A well-crafted executive summary can help you gain the buy-in you need from senior executives and product leaders.

By following the checklist above, you can ensure that they are providing you senior stakeholders with the best executive summary possible.

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Ayan Basu talks about what it’s like to work on a product that spans more than 160 countries and differs slightly in various markets.

how to write an executive summary of a paper

How availability heuristics shape product thinking

Availability heuristics refer to your brain preferring to use information that’s readily available at the expense of being comprehensive.

how to write an executive summary of a paper

Leader Spotlight: Meeting customers where they are, with Bernadette Fisher

Bernadette Fishertalks about ButcherBox’s product offerings and how they have changed over time to meet customers where they are.

What To Do If Your Customers Dont Want To Talk To You

What if your customers don’t want to talk to you?

Startups with fewer than 500 customers and startups targeting niche target audiences especially have a hard time.

how to write an executive summary of a paper

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How to Write an Executive Summary in 6 Steps

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When you’re starting a business, one of the first things you need to do is write a business plan. Your business plan is like a roadmap for your business, so you can lay out your goals and a concrete plan for how you’ll reach them.

Not only is a business plan essential for any business owner, but it’s also a requirement if you decide to apply for small business funding or find investors. After all, before a bank or individual hands over any money, they’ll want to be sure your company is on solid ground (so they can get their money back).

A business plan consists of several pieces, from an executive summary and market analysis to a financial plan and projections. The executive summary will be the first part of your business plan.

If wondering how to write an executive summary has kept you from completing your business plan, we’re here to help. In this guide, we’ll explain what an executive summary is and provide tips for writing your own so your business plan can start strong.

how to write an executive summary of a paper

What is an executive summary?

An executive summary is a short, informative, and easy-to-read opening statement to your business plan. Even though it’s just one to two pages, the executive summary is incredibly important.

An executive summary tells the story of what your business does, why an investor might be interested in giving funds to your business, why their investment will be well-spent, and why you do what you do. An executive summary should be informative, but it should also capture a busy reader’s attention.

How much do you need?

with Fundera by NerdWallet

We’ll start with a brief questionnaire to better understand the unique needs of your business.

Once we uncover your personalized matches, our team will consult you on the process moving forward.

Why write an executive summary?

Anyone you’re sending your executive summary and business plan to is likely busy—very busy. An entire business plan is long, involved, and deals with a lot of numbers.

Someone busy wants to get an understanding of your business, and they want to do it quickly, which is to say not by diving into a complicated, 80-page business plan. That’s where your executive summary comes in.

An executive summary provides just the opportunity to hook someone’s interest, tell them about your business, and offer a clear selling point as to why they should consider investing in your business.

Your executive summary is your chance to sell your business to potential investors and show them your business is worth not only their money but also their time.

What to include in an executive summary

By its nature, an executive summary is short. You must be able to clearly communicate the idea of your business, what sets you apart, and how you plan to grow into a successful enterprise.

The subsequent sections of your business plan will go into more detail, but your executive summary should include the most critical pieces of your business plan—enough to stand on its own, as it’s often the only thing a prospective investor will read. Here’s what your executive summary should include—consider it an executive summary template from which you can model your own.

1. The hook

The first sentence and paragraph of your executive summary determine whether or not the entire executive summary gets read. That’s why the hook or introduction is so important.

In general, a hook is considered anything that will get a reader’s attention. While an executive summary is a formal business document, you do want your hook to make you stand out from the crowd—without wasting time.

Your hook can be sharing something creative about your company, an interesting fact, or just a very well-crafted description of your business. It’s crucial to craft your hook with the personality of your reader in mind. Give them something that will make your company stand out and be memorable among a sea of other business plans.

Grab their attention in the first paragraph, and you’re much more likely to get your executive summary read, which could lead to an investment.

2. Company description summary

Now that you’ve hooked your reader, it’s time to get into some general information about your business. If an investor is going to give you money, after all, they first need to understand what your company does or what product you sell and who is managing the company.

Your company description should include information about your business, such as when it was formed and where you’re located; your products or services; the founders or executive team, including names and specific roles; and any additional details about the management team or style.

3. Market analysis

Your market analysis in the executive summary is a brief description of what the market for your business looks like. You want to show that you have done your research and proven that there is a need for your specific product or services. Some questions you should answer:

Who are your competitors?

Is there a demand for your products or services?

What advantages do you have that make your business unique in comparison to others?

To reiterate, stick to the highlights of your market analysis in your executive summary. You’ll provide a complete analysis in a separate section of your business plan, but you should be able to communicate enough in the executive summary that a potential investor can gauge whether your business has potential.

4. Products and services

Now that you’ve established a need in the market, it’s time to show just how your business will fill it. This section of your executive summary is all about highlighting the product or service that your company offers. Talk about your current sales, the growth you’ve seen so far, and any other highlights that are a selling point for your company.

This is also a good time to identify what sets your business apart and gives you a competitive advantage. After all, it’s unlikely that your business is the first of its kind. Highlight what you do better than the competition and why potential customers will choose your product or service over the other options on the market.

5. Financial information and projections

In this section of your executive summary, you want to give the reader an overview of your current business financials. Again, you’ll go more in-depth into this section later in your business plan, so just provide some highlights. Include your current sales and profits (if you have any), as well as what funding you’re hoping to acquire and how this will affect your financials in the next few years.

This is also where you can explain what funding, if any, you’ve received in the past. If you paid back your loan on time, this is an especially bright selling point for potential lenders.

6. Future plans

While asking for what funding you need is essential, you’ve also got to make clear what you’re going to use that funding for. If you’re asking for money, you want the person to know you have a plan to put those funds to good use.

Are you hoping to open another location, expand your product line, invest in your marketing efforts? This final section of your executive summary should detail where you want your business to go in the future, as well as drive home how funding can help you get there.

Tips for writing an executive summary

Even if you include each part of a good executive summary, you might not get noticed. What is written can be just as important as how it’s written. An executive summary has to strike a delicate balance between formal, personable, confident, and humble.

1. Be concise

An executive summary should include everything that’s in your business plan, just in a much shorter format. Writing a concise executive summary is no easy task and will require many revisions to get to the final draft. And while this is the first section of your executive summary, you’ll want to write it last, after you’ve put together all the other elements.

To choose your most important points and what should be included in the executive summary, go through your business plan, and pull out single-line bullet points. Go back through those bullet points and eliminate everything unnecessary to understanding your business.

Once you have your list of bullet points narrowed down, you can start writing your executive summary. Once it’s written, go back in and remove any unnecessary information. Remember, you should only be including the highlights—you have the rest of your business plan to go into more detail. The shorter and clearer your executive summary is, the more likely someone is to read it.

2. Use bullet points

One simple way to make your executive summary more readable is to use bullet points. If someone is reading quickly or skimming your executive summary, extra whitespace can make the content faster and easier to read.

Short paragraphs, short sentences, and bullet points all make an executive summary easier to skim—which is likely what the reader is doing. If important numbers and convincing stats jump out at the reader, they’re more likely to keep reading.

3. Speak to your audience

When writing your executive summary, be sure to think about who will be reading it; that’s who you’re speaking to. If you can personalize your executive summary to the personality and interests of the person who will read it, you’re more likely to capture their attention.

Personalizing might come in the form of a name in the salutation, sharing details in a specific way you know that person likes and the tone of your writing. An executive summary deals with business, so it will generally have a formal tone. But, different industries may be comfortable with some creativity of language or using shorthand to refer to certain ideas.

Know who you’re speaking to and use the right tone to speak to them. That might be formal and deferential, expert and clipped, informal and personable, or any other appropriate tone. This may also involve writing different versions of your executive summary for different audiences.

4. Play to your strengths

One of the best ways to catch the attention of your reader is to share why your business is unique. What makes your business unique is also what makes your business strong, which can capture a reader’s interest and show them why your business is worth investing in. Be sure to highlight these strengths from the start of your executive summary.

5. Get a test reader

Once you’ve written and edited your executive summary, you need a test reader. While someone in your industry or another business owner can be a great resource, you should also consider finding a test reader with limited knowledge of your business and industry. Your executive summary should be so clear that anyone can understand it, so having a variety of test readers can help identify any confusing language.

If you don’t have access to a test reader, consider using tools such as Hemingway App and Grammarly to ensure you’ve written something that’s easy to read and uses proper grammar.

How long should an executive summary be?

There’s no firm rule on how long an executive summary should be, as it depends on the length of your business plan and the depth of understanding needed by the reader to fully grasp your ask.

That being said, it should be as short and concise as you can get it. In general, an executive summary should be one to two pages in length.

You can fudge the length slightly by adjusting the margin and font size, but don’t forget readability is just as important as length. You want to leave plenty of white space and have a large enough font that the reader is comfortable while reading your executive summary. If your executive summary is hard to read, it’s less likely your reader will take the time to read your business plan.

What to avoid in an executive summary

While the rules for writing a stellar executive summary can be fuzzy, there are a few clear rules for what to avoid in your executive summary.

Your executive summary should avoid:

Focusing on investment. Instead, focus on getting the reader to be interested enough to continue and read your business plan or at least schedule a meeting with you.

Clichés, superlatives, and claims that aren’t backed up by fact. Your executive summary isn’t marketing material. It should be straightforward and clear.

Avoiding the executive summary no-nos is just as important as striking the right tone and getting in the necessary information for your reader.

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Start Your Dream Business

The bottom line

While an executive summary is short, it’s challenging to write. Your executive summary condenses your entire introduction, business description, business plan, market analysis, financial projections, and ask into one to two pages. Condensing information down to its most essential form takes time and many drafts. When you’re putting together your business plan’s executive summary, be sure to give yourself plenty of time to write it and to seek the help of friends or colleagues for editing it to perfection.

However, some tools make crafting a business plan, including your executive summary, a simpler process. A business plan template is a great place to start, and business plan software can especially help with the design of your business plan. After all, a well-written executive summary can make all the difference in obtaining funding for your business, so you’ll want all the help you can get.

This article originally appeared on JustBusiness, a subsidiary of NerdWallet.

On a similar note...

How to Write an Executive Summary for a Report: Step By Step Guide with Examples

how to write an executive summary of a paper

Table of contents

So you have finally written a great comprehensive business report that took you weeks to create. You have included all the data from the different departments, compared it, done the analysis, made forecasts, and provided solutions to specific problems.

There is just one problem – the key stakeholders in the company don’t have enough time to go through the whole report.

Since the data and the KPIs that you included in the report are necessary for quality decision-making, you can see why this can become a huge issue.

Luckily, there is a way to present all of your key findings and not take too much of their time. This is done through executive summaries.

An executive summary is exactly what the name suggests – a summary. It is essentially a quick overview of all the most important metrics in the report. The purpose of this summary is to bring the attention of the highest-ranking members in the company to the most important KPIs that they will consider when making decisions.

While an executive summary is a rather short section, it doesn’t mean that it’s easy to write. You will have to pay extra attention to every single sentence in order to avoid unnecessary information.

Do you want to learn how to create an informative executive summary? This guide will show you all you need to know.

What Is an Executive Report?

What is an executive summary in a report, how long should an executive summary be, who is the audience of an executive summary, what should be included in an executive summary report, how to write an executive summary report, common mistakes to avoid when writing executive summaries, executive report examples, executive summary templates, create executive reports in databox.

marketing_overview_hubspot_ga_dashboard_databox

Executive reports are used for keeping senior managers updated on the latest and most significant activities in the company. These reports have to be concise and accurate since they will have a huge impact on the most important business-related decisions.

Working for any sort of company requires writing different types of reports such as financial reports , marketing reports , sales reports , internal reports, and more.

What all of these reports have in common is that they are very comprehensive and typically require a lot of time to go through them –way too much time, if you ask busy managers.

They include a wealthy amount of data and a bunch of different metrics which are more useful for a particular team in the company. However, the highest-ranking members tend to be more focused on only the most essential KPIs that they need for making future decisions and strategies.

This is why executive reports come in handy. They are usually only a few pages long and they include only the most relevant details and data that incurred in a specific period.

An executive summary is the brief overview section included in a long report or document. This part of the report primarily focuses on the key topics and most important data within it. It can include an overall business goal of the company or short-term strategic objectives.

This summary is primarily useful for C-level managers who don’t have time to read the whole report but want to have an insight into the main KPIs and latest business performances.

Bank officials also may use executive summaries since it’s the quickest way for them to estimate whether your company represents a good investment opportunity.

Depending on your company’s practice, executive summaries can either be placed at the beginning of the report or as a formal section in the table of contents. 

The length of the summary depends on the type of report, but it is typically one or two pages long.

To know whether you have written a good executive summary, you can ask yourself, “Are the stakeholders going to have all the information they need to make decisions?”

If the answer is yes, you have done a good job.

There is no strict rule about how long executive summaries should be. Each company is unique which means the length will always vary. In most cases, it will depend on the size of the report/business plan.

However, a universal consensus is that it should be anywhere from one to four pages long or five to ten percent of the length of the report.

This is typically more than enough space to summarize the story behind the data and provide your stakeholders with the most important KPIs for future decision-making.

The people most interested in reading the executive summary are typically the ones who don’t have time to read the whole report and want a quick overview of the most important data and information.

These include:

  • Project stakeholders – The individuals or organizations that are actively involved in a project with your company.
  • Management personnel (decision-makers) – The highest-ranking employees in your company (manager, partner, general partner, etc.)
  • Investors – As we said, this could be bank officials who want a quick recap of your company’s performance so they can make an easier investment decision.
  • Venture capitalists – Investors who provide capital in exchange for equity stakes.
  • C-level executives – The chief executives in your business.

Related : Reporting Strategy for Multiple Audiences: 6 Tips for Getting Started

The components of your executive summary depend on what is included in the overall larger document. Executive summary elements may also vary depending on the type of document (business plan, project, report, etc.), but there are several components that are considered universal.

These are the main elements you should include:

Methods of analyzing the problem

Solutions to the problem, the ‘why now’ segment, well-defined conclusion.

The purpose of the summary should typically be included in the introduction as an opening statement. Explain what you aim to achieve with the document and communicate the value of your desired objective.

This part is supposed to grab your reader’s attention, so make sure they pay extra attention when writing it.

Problems are an unavoidable element in modern-day businesses, even in the most successful companies.

The second thing your executive summary needs to outline is what specific problem you are dealing with. It could be anything from product plans and customer feedback to sales revenue and marketing strategies.

Define the problems clearly so all the members know which areas need fixing.

Problem analysis methods are key for identifying the causes of the issue.

While figuring out the problems and the methods to solve them is immensely important, you shouldn’t overlook the things that caused them. This will help you from avoiding similar issues in the future.

Now that you’ve introduced the stakeholders to the problems, it’s time to move on to your solutions. Think of a few different ways that could solve the issue and include as many details as you can.

This is one of the most important parts of your executive summary.

The ‘Why Now’ segment showcases why the problem needs to be solved in a timely manner. You don’t want the readers to get the impression that there is plenty of time to fix the issue.

By displaying urgency in your summary, your report will have a much bigger impact.

One of the ways to display urgency visually is by adding performance benchmarks to your report. In case your business is not performing well as other companies within your industry, only one image showcasing which metrics are below the median could make a compelling case for the reader.

High churn example

For example, if you have discovered that your churn rate is much higher than for an average SaaS company, this may be a good indication that you have issues with poor customer service, poor marketing, pricing issues, potentially outdated product features, etc.

Benchmark Your Performance Against Hundreds of Companies Just Like Yours

Viewing benchmark data can be enlightening, but seeing where your company’s efforts rank against those benchmarks can be game-changing. 

Browse Databox’s open Benchmark Groups and join ones relevant to your business to get free and instant performance benchmarks. 

Lastly, you should end your executive summary with a well-defined conclusion.

Make sure to include a recap of the problems, solutions, and the overall most important KPIs from the document.

Okay, so you understand the basics of executive summaries and why they are so important. However, you still aren’t sure how to write one.

Don’t worry.

Here are some of the best practices you can use to create amazing executive summaries that will impress your key stakeholders and high-ranking members.

Write it Last

Grab their attention, use appropriate language, talk strategy, include forecasts, highlight funding needs, make it short.

The most natural way to write your executive summary is by writing it at the end of your report/business plan.

This is because you will already have gone through all the most important information and data that should later be included.

A good suggestion is to take notes of all the significant KPIs that you think should be incorporated in the summary, it will make it easier for you to later categorize the data and you will have a clearer overview of the key parts of the report.

You may think that you already know which data you are going to include, but once you wrap up your report, you will probably run into certain things that you forgot to implement. It’s much easier to create an executive summary with all the data segmented in one place, than to rewrite it later.

While your primary goal when creating the executive summary is to make it informative, you also have to grab the attention of your readers so that you can motivate them to read the rest of the document.

Once they finish reading the last few sentences of the summary, the audience should be looking forward to checking out the remanding parts to get the full story.

If you are having trouble with finding ways to capture the reader’s attention, you can ask some of your colleagues from the sales department to lend a hand. After all, that’s their specialty.

One more important element is the type of language you use in the summary. Keep in mind who will be reading the summary, your language should be adjusted to a group of executives.

Make the summary understandable and avoid using complicated terms that may cause confusion, your goal is to feed the stakeholders with important information that will affect their decision-making.

This doesn’t only refer to the words that you use, the way in which you provide explanation should also be taken into consideration. People reading the report should be able to easily and quickly understand the main pain points that you highlighted.

You should have a specific part in your executive summary where you will focus on future strategies. This part should include information regarding your project, target market, program, and the problems that you think should be solved as soon as possible.

Also, you should provide some useful insights into the overall industry or field that your business operates in. Showcase some of the competitive advantages of your company and specific marketing insights that you think the readers would find interesting.

Related : What Is Strategic Reporting? 4 Report Examples to Get Inspiration From

Make one of the sections revolve around financial and sales forecasts for the next 1-3 years. Provide details of your breakeven points, such as where the expenses/revenues are equal and when you expect certain profits from your strategies.

This practice is mainly useful for business plans, but the same principle can be applied to reports. You can include predictions on how your overall objectives and goals will bring profit to the company.

Related : How Lone Fir Creative Uses Databox to Forecast, Set, & Achieve Agency & Client Goals

Don’t forget to talk about the funding needs for your projects since there is a high chance that investors will find their way to the executive summary as well.

You can even use a quotation from an influential figure that supports your upcoming projects. Include the costs that will incur but also provide profitability predictions that will persuade the investors to fund your projects.

While your report should include all of the most important metrics and data, aim for maximum conciseness.

Don’t include any information that may be abundant and try to keep the executive summary as short as possible. Creating a summary that takes up dozens of pages will lose its original purpose.

With a concise summary and clear communication of your messages, your readers will have an easy time understanding your thoughts and then take them into consideration.

Also, one last tip is to use a positive tone throughout the summary. You want your report to exude confidence and reassure the readers.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

  • Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
  • New Contacts from Sessions . How well is your campaign driving new contacts and customers?
  • Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
  • Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
  • Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

marketing_overview_hubspot_ga_dashboard_preview

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

No one expects you to become an expert executive summary writer overnight. Learning how to create great and meaningful summaries will inevitably take some time.

With the above-mentioned best practices in mind, you should also pay attention to avoiding certain mistakes that could reduce the value of your summaries.

Here are some examples.

Don’t use jargon

Avoid going into details, the summary should be able to stand alone, don’t forget to proofread.

From project stakeholders to C-level executives, everyone should be able to easily understand and read the information you gather in your summary.

Keep in mind, you are probably much more familiar with some of the technical terms that your departments use since you are closer to the daily work and individual tasks than your stakeholders.

Read your summary once again after you finish it to make sure there are no jargons you forgot to elaborate on.

Remember, your summary should be as short as possible, but still include all the key metrics and KPIs. There is no reason to go into details of specific projects, due dates, department performances, etc.

When creating the summary, ask yourself twice whether the information you included truly needs to be there.

Of course, there are certain details that bring value to the summary, but learn how to categorize the useful ones from the unnecessary ones.

While you will know your way around the project, that doesn’t apply to the readers.

After wrapping up the summary, go over it once again to see whether it can stand on its own. This means checking out if there is any sort of context that the readers will need in order to understand the summary.

If the answer is yes, you will have to redo the parts that can’t be understood by first-time readers.

Your executive summary is prone to changes, so making a typo isn’t the end of the world, you can always go back and fix it.

However, it’s not a bad idea to ask one of your colleagues to proofread it as well, just so you have an additional set of eyes.

Using reporting tools such as dashboards for executive reports can provide you with a birds-eye view of your company’s most important KPIs and data.

These dashboards work as visualization tools that will make all the important metrics much more understandable to your internal stakeholders.

Since executive reports on their own don’t include any visual elements such as graphs or charts, these dashboards basically grant them superpowers.

Executive reporting dashboards also make the decision-making process easier since there won’t be any misunderstandings regarding the meaning of the data.

Not only will you be able to gather the data in real-time, but you can also connect different sources onto the dashboard can use the visuals for performance comparisons.

Interested in giving executive report dashboards a try? Let’s check out some of the best examples.

Marketing Performance Dashboard

Customer support performance dashboard, financial overview dashboard, saas management dashboard, sales kpi dashboard.

To stay on top of your key user acquisition metrics, such as visit to leads conversion rates, email traffic, blog traffic, and more, you can use this Marketing Performance Dashboard .

You can pull in data from advanced tools such as HubSpot Marketing and Google Analytics to get a full overview of how your website generates leads.

Some of the things you will learn through this dashboard are:

  • Which traffic sources are generating the most amount of leads
  • How to track which number of users are new to your website
  • How to compare the traffic you are getting from your email with blog traffic
  • How to stay on top of lead generation goals each month
  • How to be sure that your marketing activities are paying off

The key metrics included are bounce rate, new users, page/session, pageview, and average session duration.

Marketing Performance Dashboard

You can use the Customer Support Performance Dashboard to track the overall performance of your customer service and check out how efficient individual agents are.

This simple and customizable dashboard will help you stay in touch with new conversation numbers, open/closed conversations by teammates, number of leads, and much more.

Also, you will get the answers to questions such as:

  • How many new conversations did my customer support agents deal with yesterday/last week/last month?
  • How many conversations are currently in progress?
  • In which way are customer conversations tagged on Intercom?
  • How to track the number of leads that the support team is generating?
  • What is the best way to measure the performance of my customer support team?

Some of the key metrics are leads, open conversations, new conversations, tags by tag name, closed conversations, and more.

Customer Support Performance Dashboard

Want to know how much income your business generated last month? How to measure the financial health of your business? How about figuring out the best way to track credit card purchases?

You can track all of these things and more by using the Financial Overview Dashboard .

This free customizable dashboard will help you gain an insight into all of your business’s financial operations, cash flow, bank accounts, sales, expenses, and plenty more.

Understanding your company from a financial standpoint is one of the most important ingredients of good decision-making.

With key metrics such as gross profit, net income, open invoices, total expenses, and dozens more – all gathered in one financial reporting software , you will have no problems staying on top of your financial activities.

Financial Overview Dashboard

Use this SaaS Management Dashboard to have a clear overview of your business’s KPIs in real-time. This customizable dashboard will help you stay competitive in the SaaS industry by providing you with comprehensive data that can you can visualize, making it more understandable.

You will be able to:

  • See how your company is growing on an annual basis
  • Have a detailed outline of your weakest and strongest months
  • Determine which strategies are most efficient in driving revenue

The key metrics included in this dashboard are recurring revenue, churn by type, MRR changes, and customer changes.

SaaS Management Dashboard

Do you want to monitor your sales team’s output and outcomes? Interested in tracking average deal sizes, number of won deals, new deals created, and more?

This Sales KPI Dashboard can help you do just that.

It serves as a perfect tool for sales managers that are looking for the best way to create detailed overviews of their performances. It also helps achieve sales manager goals for the pre-set time periods.

By connecting your HubSpot account to this customizable dashboard, you can learn:

  • What’s the average deal size
  • The number of open, closed, and lost deals each month
  • How much revenue you can expect from the new deals
  • How your business is progressing towards the overall sales goals

Sales KPI Dashboard

Although you probably understand what your executive summary should include by now, you may still need a bit of help with creating a clear outline to follow.

We thought about that too. Here are some template examples that will help you create executive summaries for different kinds of business needs.

Here is an executive summary template for a business plan:

  • [Company profile (with relevant history)]
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  • Business and industry
  • Science and innovation
  • Artificial intelligence
  • AI regulation: a pro-innovation approach – policy proposals
  • Department for Science, Innovation & Technology

A pro-innovation approach to AI regulation: government response

Updated 6 February 2024

how to write an executive summary of a paper

© Crown copyright 2024

This publication is licensed under the terms of the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] .

Where we have identified any third party copyright information you will need to obtain permission from the copyright holders concerned.

This publication is available at https://www.gov.uk/government/consultations/ai-regulation-a-pro-innovation-approach-policy-proposals/outcome/a-pro-innovation-approach-to-ai-regulation-government-response

Command Paper: CP 1019

ISBN: 978-1-5286-4565-2

Unique Reference: E03019481 02/24

Presented to Parliament by the Secretary of State for Science, Innovation and Technology by Command of His Majesty on 6 February 2024.

1. Ministerial foreword

how to write an executive summary of a paper

The Rt Hon Michelle Donelan MP, Secretary of State for Science, Innovation and Technology.

The world is on the cusp of an extraordinary new era driven by advances in Artificial Intelligence ( AI ). I see the rapid improvements in AI capabilities as a once-in-a-generation opportunity for the British people to revolutionise our public services for the better and to deliver real, tangible, long-term results for our country.

The UK AI market is predicted to grow to over $1 trillion ( USD ) by 2035 [footnote 1] – unlocking everything from new skills and jobs to once unimaginable life saving treatments for cruel diseases like cancer and dementia. My ambition is for us to revolutionise the way we deliver public services by becoming a global leader in safe AI development and deployment.

We have done more than any government in history to make that a reality, and our plan is working. Last year, we hosted the world’s first AI Safety Summit, bringing industry, academia, and civil society together with 28 leading AI nations and the EU to agree the Bletchley Declaration – a landmark commitment to share responsibility on mitigating the risks of frontier AI , collaborate on safety and research, and to promote its potential as a force for good in this world.

We were the first government in the world to formally publish our assessment of the capabilities and risks presented by advanced AI . Research-driven reports produced by DSIT and the Government Office for Science [footnote 2] laid the groundwork for an international agreement on evaluating the scientific basis for AI safety.

We brought together a powerful consortium of experts in our AI Safety Institute, the first government-backed organisation of its kind anywhere in the world, committed to advancing AI safety in the public interest.

With the publication of our AI regulation white paper in March 2023, I wanted to take a bold and considered approach that is strongly pro-innovation and pro-safety. I knew that our approach had to remain agile enough to deal with the unprecedented speed of development, while also remaining robust enough in each sector to address the key concerns around potential societal harms, misuse risks, and autonomy risks that our thought leadership exercises have revealed.

This agile, sector-based approach has empowered regulators to create bespoke measures that are tailored to the various needs and risks posed by different sections of our economy. The white paper proposed five clear principles for existing UK regulators to follow, and set out our expectations for responsible AI innovation.

This common sense, pragmatic approach has been welcomed and endorsed both by the companies at the frontier of AI development and leading AI safety experts. Google DeepMind, Microsoft, OpenAI and Anthropic all supported the UK’s approach, as did Britain’s budding AI start-up scene, and many leading voices in academia and civil society.

In considering our response to the consultation, I have sought to double-down on this success and drive forward our plans to make Britain the safest and most innovative place to develop and deploy AI in the world, backed by over £100 million to support AI innovation and regulation. Building on feedback from the consultation, we have set up a central function to drive coherence in our regulatory approach across government, including by recruiting a new multidisciplinary team to conduct cross-sector risk assessment and monitoring to guard against existing and emerging risks in AI .

With the Digital Regulation Cooperation Forum ( DRCF ), we have launched the AI and Digital Hub, a pilot scheme for a brand-new advisory service to support innovation run by expert regulators including Ofcom , the CMA , the FCA and the ICO [footnote 3] . We are also investing in new support for regulators to build their practical, technical expertise and backing the launch of nine new research hubs across the UK to harness the power of AI in everything from mathematics to healthcare.

Advancing our thought-leadership on safety, we also lay out the case for a set of targeted, binding requirements on developers of highly capable general-purpose AI models in the future to ensure that powerful, sophisticated AI develops in a way which is safe. And our targeted consultations on our cross-economy AI risk register and monitoring and evaluation framework will engage with leading voices from regulators, academia, civil society, and industry.

The AI Safety Institute’s technical experts will have a crucial role to play here as we develop our approach on the regulation of highly capable general-purpose systems. We will work closely with AI developers, with academics and civil society members who can provide independent expert perspectives, and also with our international partners ahead of the next AI Safety Summits in the Republic of Korea and France.

Finally, my thinking on the UK’s AI leadership role goes well beyond the immediate horizon. We will need to lead fields of research that will help us build a more resilient society ready for a world where advanced AI technology and the means to develop it are widely accessible. That means improving our defensive capabilities against bad actors seeking to use AI to do harm, it means designing new internet infrastructure for a digital world full of agentic AI systems, and it also means leveraging AI to improve critical aspects of our society such as democratic deliberation and consensus. AI can and must remain a force for the public good, and we will ensure that is the case as we develop our policy approach in this area.

This response paper is another clear, decisive step forward for the UK’s ambitions to lead in safe AI and to be a Science and Technology Superpower by the end of the decade. Whether you are an AI developer, user, safety researcher or you represent civil society, we all have a shared interest in realising the opportunities of safe AI development. I am personally driven by a mission to improve the lives of the British people through technology and innovation, and our response paper sets out exactly how that mission will become a reality.

2. Executive summary

The pace of progress in Artificial Intelligence ( AI ) has been unlike any previous technology and the benefits are already being realised across the UK: AI is helping to make our jobs safer and more satisfying, conserve our wildlife and fight climate change, and make our public services more efficient. Not only do we need to plan for the capabilities and uses of the AI systems we have today, but we must also prepare for a near future where the most powerful systems are broadly accessible and significantly more capable [footnote 4] .

The UK is leading the world in how to respond to this challenge. Our approach to preparing for such a future is firmly pro-innovation. To realise the immense benefits of these technologies, we must ensure AI ’s trustworthiness and public adoption through a strong pro-safety approach. As the Prime Minister set out in a landmark speech in October 2023, “the future of AI is safe AI .  And by making the UK a global leader in safe AI , we will attract even more of the new jobs and investment that will come from this new wave of technology” [footnote 5] . To achieve this, the UK is investing more in AI safety than any other country in the world. Today we are announcing over £100 million to help realise new AI innovations and support regulators’ technical capabilities.

Our regulatory framework builds on the existing strengths of both our thriving AI industry and expert regulatory ecosystem. We are focused on ensuring that regulators are prepared to face the new challenges and opportunities that AI can bring to their domains. By working closely with regulators to ensure cohesion across the landscape, we are ensuring that innovators can bring new products to market safely and quickly. Today we are announcing several new initiatives to make the UK an even better place to build and use AI including £10 million to jumpstart regulator’s AI capabilities; a new commitment by UK Research and Innovation ( UKRI ) that future investments in AI research will be leveraged to support regulator skills and expertise; and a £9 million partnership with the US on responsible AI as part of our International Science Partnerships Fund [footnote 6] . Through this and other work on AI across government, the UK will continue to respond to risks proportionately and effectively, striving to lead thinking on AI in the years to come. Through this and other work on AI across government, the UK will continue to respond to risks proportionately and effectively, striving to lead thinking on AI in the years to come.

In March 2023, we published our AI regulation white paper, setting out initial proposals to develop a pro-innovation regulatory framework for AI . The proposed framework outlined five cross-sectoral principles for the UK’s existing regulators to interpret and apply within their remits. We also proposed a new central function to bring coherence to the regime and address regulatory gaps. This flexible and adaptive regulatory approach has enabled us to act decisively and respond to technological progress.

Our context-based framework received strong support from stakeholders across society and we have acted quickly to implement it. We are pleased that a number of regulators are already taking action in line with our proposed approach, from the Competition and Market Authority’s ( CMA ) review of foundation models to the updated guidance on data protection and AI by the Information Commissioner’s Office ( ICO ). We are asking a number of regulators to publish an update outlining their strategic approach to AI by 30 April 2024.

We have already started developing the central function to support effective risk monitoring, regulator coordination, and knowledge exchange. Our new £10 million package to boost regulators’ AI capabilities, mentioned above, will help our regulators develop cutting-edge research and practical tools to build the foundations of their AI expertise and everyday ability to address AI risks in their domains. Today, we are also publishing new guidance to support regulators to implement the principles effectively and the Digital Regulation Cooperation Forum ( DRCF ) is sharing details on the eligibility criteria for the support to be offered by the AI and Digital Hub pilot.

We are backing this approach with wider support for the AI ecosystem, including committing over £1.5 billion in 2023 to build the next generation of supercomputers in the public sector and today announcing an £80 million boost in AI research through the launch of nine new research hubs across the UK to propel transformative innovations. In November 2023, the Prime Minister brought together leading global actors in AI for the first AI Safety Summit where they discussed and agreed actions to address emerging risks posed by the development and deployment of the most powerful AI systems. Leading AI developers set out the steps they are already taking to make models safe and committed to sharing the most powerful AI models with governments for testing so that we can ensure safety today and prepare for the risks of tomorrow.

Our initial technical contribution to this international effort is through the creation of an AI Safety Institute to lead evaluations and safety research in the UK government, in collaboration with partners across the world including in the US. The AI Safety Summit underscored the global nature of AI development and deployment, demonstrating the need for further work towards a coherent and collaborative approach to international governance.

Our overall approach – combining cross-sectoral principles and a context-specific framework, international leadership and collaboration, and voluntary measures on developers – is right today as it allows us to keep pace with rapid and uncertain advances in AI . However, the challenges posed by AI technologies will ultimately require legislative action in every country once understanding of risk has matured. In this document, we build on our pro-innovation framework and pro-safety actions by setting out our early thinking and the questions that we will need to consider for the next stage of our regulatory approach.

As AI systems advance in capability and societal impact, it is clear that some mandatory measures will ultimately be required across all jurisdictions to address potential AI -related harms, ensure public safety, and let us realise the transformative opportunities that the technology offers. However, acting before we properly understand the risks and appropriate mitigations would harm our ability to benefit from technological progress while leaving us unable to adapt quickly to emerging risks. We are going to take our time to get this right – we will legislate when we are confident that it is the right thing to do.

We have placed a particular emphasis on the challenges that highly capable general-purpose AI systems pose to a context-based framework. Here we lay out a pro-innovation case for further targeted binding requirements on the small number of organisations developing highly capable general-purpose AI systems to ensure that they are accountable for making these technologies sufficiently safe. This can be done while allowing our expert regulators to provide effective rules for the use of AI within their remits.

In the coming months, we will formally establish our activities to support regulator capabilities and coordination, including a new steering committee with government and regulator representatives to support coordination across the AI governance landscape. We will conduct targeted consultations on our cross-economy AI risk register and plan to assess the regulatory framework. We will continue our work to address the key issues of today, from electoral interference to discrimination to intellectual property law, and the most pressing risks of tomorrow, such as biosecurity and AI alignment. We will also continue to lead international conversations on AI governance across a range of fora and initiatives in the lead up to the next AI Safety Summits in the Republic of Korea and France.

3. Glossary

Adaptivity : The ability to see patterns and make decisions in ways not directly envisioned by human programmers.

Artificial General Intelligence ( AGI ) : A theoretical form of advanced AI that would have capabilities that compare to or exceed humans across most economically valuable work [footnote 7] . A number of AI companies have publicly stated their aim to build AGI and believe it may be achievable within the next twenty years. Other experts believe we may not build AGI for many decades, if ever.

AI agents : Autonomous AI systems that perform multiple sequential steps – sometimes including actions like browsing the internet, sending emails, or sending instructions to physical equipment – to try and complete a high-level task or goal.

AI deployers : Any individual or organisation that supplies or uses an AI application to provide a product or service to an end user.

AI developers : Organisations or individuals who design, build, train, adapt, or combine AI models and applications.

AI end user : Any intended or actual individual or organisation that uses or consumes an AI -based product or service as it is deployed.

AI life cycle : All events and processes that relate to an AI system’s lifespan, from inception to decommissioning, including its design, research, training, development, deployment, integration, operation, maintenance, sale, use, and governance.

AI risks : The potential negative or harmful outcomes arising from the development or deployment of AI systems.

Alignment : The process of ensuring an AI system’s goals and behaviours are in line with human values and intentions.

Application Programming Interface (API) : A set of rules and protocols that enables integration and communication between AI systems and other software applications.

Autonomous : Capable of operating, taking actions, or making decisions without the express intent or oversight of a human.

Capabilities : The range of tasks or functions that an AI system can perform and the proficiency with which it can perform them.

Compute : Computational processing power, including Central Processing Units ( CPUs ), Graphics Processing Units ( GPUs ), and other hardware, used to run AI models and algorithms.

Developers of highly capable general-purpose systems : A subsection of AI developers, these organisations invest large amounts of resource into designing, building, and pre-training the most capable AI foundation models. These models can underpin a wide range of AI applications and may be deployed directly or adapted by downstream AI developers.

Disinformation : Deliberately false information spread with the intent to deceive or mislead.

Foundation models : Machine learning models trained on very large amounts of data that can be adapted to a wide range of tasks.

Frontier AI : For the AI Safety Summit, we defined frontier AI as models that can perform a wide variety of tasks and match or exceed the capabilities present in today’s most advanced models. In this paper, we focus on highly capable general-purpose AI model developers to target our proposals for new responsibilities.

Misinformation : Incorrect or misleading information spread without harmful intent.

Safety and security : The protection, wellbeing, and autonomy of civil society and the population [footnote 8] . In this publication, safety is often used to describe prevention of or protection against AI -related harms. AI security refers to protecting AI systems from technical interference such as cyber-attacks [footnote 9] .

Superhuman performance : When an AI model demonstrates capabilities that exceed human ability benchmarking for a specific task or activity.

Box 1: Different types of AI systems

In our discussion paper on frontier AI capabilities and risks [footnote 10] , we noted that definitions of AI are often challenging due to the quick advancements in the technology.

For the purposes of developing a proportionate regulatory approach that effectively addresses the risks posed by the most powerful AI systems, we currently distinguish between:

Highly capable general-purpose AI : Foundation models that can perform a wide variety of tasks and match or exceed the capabilities present in today’s most advanced models. Generally, such models will span from novice through to expert capabilities with some even showing superhuman performance across a range of tasks.

Highly capable narrow AI : Foundation models that can perform a narrow set of tasks, normally within a specific field such as biology, with capabilities that match or exceed those present in today’s most advanced models. Generally, such models will demonstrate superhuman abilities on these narrow tasks or domains.

Agentic AI or AI agents : An emerging subset of AI technologies that can competently complete tasks over long timeframes and with multiple steps. These systems can use tools such as coding environments, the internet, and narrow AI models to complete tasks.

4. Introduction

1. The UK’s AI sector is thriving. The AI industry in the UK employs over 50,000 people and contributes £3.7 billion to economy [footnote 11] . Our universities produce some of the best AI research and talent, and the UK is home to the third largest number of AI unicorns and start-ups in the world [footnote 12] .

2. Our goal is to make the UK a great place to build and use AI that changes our lives for the better. AI is the defining technology of our time and the UK is leading the world with our response.

3. In March 2023, we published a white paper setting out our proposals to establish a regulatory framework for AI to drive safe, responsible innovation [footnote 13] . We set five principles for regulators to interpret and apply within their domains. We also included proposals for a central function within government to conduct a range of activities such as risk assessment and regulatory coordination to support the adaptability and coherence of our approach.

4. We held a 12-week public consultation on our proposals [footnote 14] . We have now analysed the evidence (see Annex A for details) which has informed our approach. We thank everyone for their submissions. We have also built into our response the key achievements from the AI Safety Summit in November 2023, as well as themes from our engagement ahead of the Summit.

AI White Paper consultation and AI Summit activities

AI White Paper consultation and AI Summit activities.

5. The pace of AI development continues to accelerate. In the run up to the AI Safety Summit, we published a discussion paper on AI risks and capabilities that showed these trends are likely to continue in line with companies building these technologies using more compute, more data, and increasingly efficient algorithms [footnote 15] . Some frontier AI labs have stated their goal to build AI systems that are more capable than humans at a range of tasks [footnote 16] .

6. Enhanced capabilities bring new opportunities. AI is already changing the way that we live and work. Workers using AI in sectors ranging from manufacturing to finance have reported improvements to their job enjoyment, performance, and health [footnote 17] . AI will change the tasks we do at work and the skills we need to do them well [footnote 18] . Recent AI developments are also changing how we spend our leisure time, with powerful AI systems underpinning the chatbots and image generators that have become some of the fastest growing consumer applications in history [footnote 19] . Highly capable AI is already transforming sectors, from helping us to conserve our wildlife [footnote 20] to changing the ways that we identify and treat disease [footnote 21] .

7. However, more powerful AI also poses new and amplified risks. For example, AI chatbots may make false information more prominent [footnote 22] or a highly capable AI system may be misused to enable crime. For instance, a model designed for drug discovery could potentially be accessed maliciously to create harmful compounds [footnote 23] .

8. AI may also fundamentally transform life in ways that are hard to predict. For instance, future agentic AI systems may be able to pursue complex goals with limited human supervision, raising questions around how AI agents remain attributable, ask for approval before taking action, and can be interrupted.

9. AI technologies present significant uncertainties that require an agile regulatory approach that supports innovation whilst adapting to address new risks. In this consultation response, we show how our flexible approach is already addressing key AI -related risks and how we are further strengthening this framework (section 5.1). We also set out initial thinking on potential new responsibilities on the developers of highly capable general-purpose AI systems alongside the voluntary commitments secured at the AI Safety Summit (section 5.2). In section 6, we provide a summary of the evidence we received to our consultation along with our formal response.

5. A regulatory framework to keep pace with a rapidly advancing technology

10. In the AI regulation white paper, we proposed five cross-sectoral principles for existing regulators to interpret and apply within their remits in order to drive safe, responsible AI innovation [footnote 24] . These are:

  • Safety, security and robustness.
  • Appropriate transparency and explainability.
  • Accountability and governance.
  • Contestability and redress.

11. We welcome the strong support for these principles through the consultation. They are the foundation of our approach. We remain committed to a context-based approach that avoids unnecessary blanket rules that apply to all AI technologies, regardless of how they are used. This is the best way to ensure an agile approach that stands the test of time.

12. We are pleased to see how regulators are already independently implementing our principles. In the white paper we highlighted the importance of a central function to support regulator capabilities and coordination. We have made good progress establishing this function within the government. We set out below how we are further strengthening it, including new funding, in section 5.1. We also show how regulators and the government are addressing some of the most important issues facing us today.

13. In section 5.2, we set out some of the regulatory challenges posed by the rapid development of highly capable general-purpose systems; how we are currently tackling these through voluntary measures, including those agreed at the AI Safety Summit; and which additional responsibilities may be required in the future to address risks effectively.

5.1. Delivering a proportionate, context-based approach to regulate the use of AI

5.1.1. regulators are taking active steps in line with the framework.

14. Since the publication of the AI regulation white paper, a number of regulators have set out work in line with our principles-based approach. For example, the Competition and Markets Authority ( CMA ) published a review of foundation models to understand the opportunities and risks for competition and consumer protection [footnote 25] . The Information Commissioner’s Office ( ICO ) updated guidance on how data protection laws apply to AI systems to include fairness [footnote 26] . To ensure the safety of AI , regulators such as the Office of Gas and Electricity Markets ( Ofgem ) and Civil Aviation Authority ( CAA ) are working on AI strategies to be published later this year. This builds on regulator work that led the way on clarifying how existing frameworks apply to AI risks in their domain, such as the Medicines and Healthcare products Regulatory Agency ( MHRA ) Software and AI as a Medical Device Change Programme 2021 on requirements for software and AI used in medical devices [footnote 27] .

15. It is important that the public have full visibility of how regulators are incorporating the principles into their work. The government has written to a number of regulators impacted by AI to ask them to publish an update outlining their strategic approach to AI by 30 April [footnote 28] . We are encouraging regulators to include:

  • An outline of the steps they are taking in line with the expectations set out in the white paper.
  • Analysis of AI -related risks in the sectors and activities they regulate and the actions they are taking to address these.
  • An explanation of their current capability to address AI as compared with their assessment of requirements, and the actions they are taking to ensure they have the right structures and skills in place.
  • A forward look of plans and activities over the coming 12 months.

16. When we published the AI regulation white paper, we proposed that the principles would be established on a non-statutory basis. Many consultation respondents noted the potential benefits of a statutory duty on regulators, but some acknowledged that implementing the regime on a non-statutory basis in the first instance would allow for important flexibilities. We think a non-statutory approach currently offers critical adaptability – especially while we are still establishing our approach – but we will keep this under review. Our decision will be informed in part by our review of the plans published by regulators, as set out above; our review of regulator powers, as set out below; and in line with our wider approach to AI legislation, such as the introduction of targeted binding measures (see section 5.2).

5.1.2 Supporting regulatory capability and coordination

17. The systemic changes driven by AI demand a system-wide response – our individual regulators cannot successfully address the opportunities and risks presented by AI technologies within their remits by acting in isolation. In the AI regulation white paper, we proposed a new central function, established within government, to monitor and assess risks across the whole economy and support regulator coordination and clarity.

18. The proposal for a central function was widely welcomed by stakeholders who noted it is critical to the effective delivery of the AI regulation framework. Many stressed that, without such a function, there is a risk of regulatory overlaps, gaps, and poor coordination as multiple regulators consider the impact of AI in their domains.

19. We have already started to establish this function in a range of ways:

i. Risk assessment : We have recruited a new multidisciplinary team to undertake cross-sectoral risk monitoring within the Department for Science, Innovation and Technology ( DSIT ), bringing together expertise in risk, regulation, and AI with backgrounds in data science, engineering, economics, and law. This team will provide continuous examination of cross-cutting AI risks, including evaluating the effectiveness of interventions by both the government and regulators. In 2024, we will launch a targeted consultation on a cross-economy AI risk register to ensure it comprehensively captures the range of risks. It will provide a single source of truth on AI risks which regulators, government departments, and external groups can use. It will also support government work to identify any risks that fall across or in between the remits of regulators so we can identify where there are gaps or existing regulation is ineffective and prioritise further action. In addition to the risk register, we are considering the added value of developing a risk management framework, similar to the one developed in the US by the National Institute of Standards and Technology ( NIST ).

ii. Regulator capabilities : Effective regulation relies on regulators having the right skills, tools, and expertise. While some regulators have been able to put the right expertise in place to address AI , others are less prepared. We are announcing £10 million for regulators to develop the capabilities and tools they need to adapt and respond to AI . We are investing in regulators today to future-proof their capabilities for tomorrow. The funding will enable regulators to collaborate to create, adapt, and improve practical tools to address AI risks and opportunities within and across their remits. It will enable regulators to carry out research and development to produce novel, actionable insights that will set the foundation of their approaches for years to come. We will work closely with regulators in the coming months to identify the most promising opportunities to leverage this funding. This builds on the recent announcement that the government will explore how to further support regulators to develop the specialist skills necessary to regulate emerging technologies, including options for increased flexibility on pay and conditions [footnote 29] .

iii. Regulator powers : We recognise the need to assess the existing powers and remits of the UK’s regulators to ensure they are equipped to address AI risks and opportunities in their domains and implement the principles in a consistent and comprehensive way. We will, therefore, work with government departments and regulators to analyse and review potential gaps in existing regulatory powers and remits.

iv. Coordination : In the coming months we will formalise our regulator coordination activities. To support and guide this work, we will establish a steering committee with government representatives and key regulators to support knowledge exchange and coordination on AI governance by spring 2024. We continue to support regulatory coordination more widely, including working with bodies such as the Digital Regulation Cooperation Forum ( DRCF ). Today we have published new guidance for regulators to support them to interpret and apply our principles.

v. Research and innovation : We are working closely with UK Research and Innovation  ( UKRI ) to ensure the government’s wider investments in AI R&D can support the government’s safety agenda. This includes a new commitment by UKRI to improve links between regulators and the skills, expertise, and activities supported by UKRI investments in AI research such as Responsible AI UK, the Trustworthy Autonomous Systems hub, the UKRI AI Centres for Doctoral Training, and the Alan Turing Institute. This will ensure the UK’s strength in AI research is fully utilised in our regulatory framework. This work builds on our previous commitment of £250 million through the UKRI Technology Missions Fund to secure the UK’s global leadership in critical technologies [footnote 30] . UKRI is today announcing that £19 million of the Technology Missions Fund will support Phase 2 of the Accelerating Trustworthy AI competition, supporting 21 projects delivered through the Innovate UK BridgeAI programme, to accelerate the adoption of trusted and responsible AI and machine learning.

vi. Ease of compliance : Regulation must work for innovators. We are supporting innovators and businesses to get new products to market safely and efficiently by funding a pilot multi-agency advisory service delivered by the DRCF [footnote 31] . This will particularly help innovators navigate the legal and regulatory requirements they need to meet before launch. The online portal for the pilot 1. DRCF AI and Digital Hub and the application window are due to launch in the spring. Insights from the pilot will inform the implementation of our regulatory approach. Further details on the eligibility criteria for the support to be offered by the pilot have been published by the DRCF today alongside this consultation response.

vii. Public trust : We want businesses, consumers, and the public to have confidence in AI technologies. We will build trust by continuing to support work on assurance techniques and technical standards. The UK AI Standards Hub, launched in 2022, provides practical tools and guides for businesses, organisations, and individuals to effectively use digital technical standards and participate in their development [footnote 32] In 2023, the government collaborated with techUK to launch the Portfolio of AI Assurance Techniques announced in the AI regulation white paper [footnote 33] . In spring 2024, we will publish an “Introduction to AI assurance” to further promote the value of AI assurance and help businesses and organisations build their understanding of the techniques for safe and trustworthy systems. Alongside this, we undertake regular research with the public to ensure the government’s approach to AI is aligned with our wider values [footnote 34] .

viii. Monitoring and Evaluation : We are developing a monitoring and evaluation plan that allows us to continuously assess the effectiveness of our regime as AI technologies change. We will conduct a targeted consultation with a range of stakeholders on our proposed plan to assess the regulatory framework in spring 2024. As part of this, we will seek detailed views on our proposed metrics and data sources.

20. AI regulation will only work within a wider ecosystem that champions the industry. In 2023, the government committed over £1.5 billion to build public sector supercomputers, including the AI Research Resource and an exascale computer. We are also working closely with the private sector to support investment, such as Microsoft’s announcement of £2.5 billion for AI -related data centres in November 2023. The £80 million investment in AI hubs that we are announcing today will enable AI to evolve and tackle complex problems across applications from healthcare treatments to power-efficient electronics. The government is also conducting a wider review of the UK AI supply chain to ensure we maintain our strategic advantage as a world leader in these technologies.

21. Finally, to drive coordinated action across government we have established lead AI Ministers across all departments to bring together work on risks and opportunities driven by AI in their sectors and to oversee implementation of frameworks and guidelines for public sector usage of AI . We are also establishing a new Inter-Ministerial Group to drive effective coordination across government on AI issues. Further to this, we are strengthening the team working on AI within the DSIT . In February 2023, we had a team of around 20 people working on AI issues. This had grown to over 160 across the newly established AI Policy Directorate and the AI Safety Institute by the end of 2023, with plans to expand to more than 270 people in 2024. In recognition of the fact that AI is a top priority for the Secretary of State and has become central to the wider work of the department and government, we will no longer maintain the branding of a separate Office for AI . Similarly, the Centre for Data Ethics and Innovation ( CDEI ) is changing its name to the Responsible Technology Adoption Unit to more accurately reflect its mission. The name highlights the directorate’s role in developing tools and techniques that enable responsible adoption of AI in the private and public sectors, in support of the department’s central mission.

AI governance landscape

AI regulation landscape.

DSIT - the government department responsible for overall responsibility for AI policy, including regulation.

Image 1 : A diagram of the AI regulation landscape showing the relationships between the government, regulators, industry, and the wider ecosystem.

5.1.3 Tackling specific risks

22. There are three broad categories of AI risk: societal harms; misuse risks; and autonomy risks [footnote 35] . Below we outline examples of how the government and regulators are responding to specific risks in line with our principles. This summary illustrates the wide range of work already happening to ensure the benefits of AI innovation can be realised safely and responsibly. It is not intended to be exhaustive or prioritise certain risks over others.

23. In addition to the work to address specific risks outlined below, we are today announcing £2 million of Arts and Humanities Research Council ( AHRC ) funding to support translational research that will help to define responsible AI across sectors such as education, policing, and creative industries. These projects, part of the AHRC ’s Bridging Responsible AI Divides ( BRAID ) work [footnote 36] , will produce recommendations to inform future work in this area and demonstrate how the UK is at the forefront of embedding AI across key sectors. In addition to the scoping projects, AHRC are confirming a further £7.6 million to fund a second phase of the BRAID programme, extending activities to 2027/28. The next phase will include a new cohort of large-scale demonstrator projects, further rounds of BRAID Fellowships, and new professional AI skills provisions, co-developed with industry and other partners.

Societal harms

Preparing UK workers for an AI enabled economy

24. AI is revolutionising the workplace. While the adoption of these technologies can bring new, higher quality jobs, it can also create and amplify a range of risks, such as workplace surveillance and discrimination in recruitment, that the government and regulators are already working to address. We want to harness the growth potential of AI but this must not be at the expense of employment rights and protections for workers. The UK’s robust system of legislation and enforcement for employment protections, including specialist labour tribunals, sets a strong foundation for workers. To ensure the use of AI in HR and recruitment is safe, responsible, and fair, the Department for Science, Innovation and Technology ( DSIT )  will provide updated guidance in spring 2024.

25. Since 2018 we have funded a £290 million package of AI skills and talent initiatives to make sure that AI education and awareness is accessible across the UK. This includes funding 24 AI Centres for Doctoral Training which will train over 1,500 PhD students. We are also working with Innovate UK and the Alan Turing Institute to develop guidance that sets out the core AI skills people need, from ‘ AI citizens’ to ‘ AI professionals’. We published draft guidance for public comment in November 2023 and we intend to publish a final version and a full skills framework in spring 2024 [footnote 37] .

26. It is hard to predict, at this stage, exactly how the labour market will change due to AI . Some sectors are concerned that AI will displace jobs through automation [footnote 38] . The Department for Education ( DfE ) has published initial work on the impact of AI on UK jobs, sectors, qualifications, and training pathways [footnote 39] . We can be confident that we will need new AI -related skills through national qualifications and training provision. The government has invested £3.8 billion in higher and further education in this parliament to make the skills system employer-led and responsive to future needs. Along with DfE ’s Apprenticeships [footnote 40] and Skills Bootcamps [footnote 41] , the new Lifelong Learning Entitlement reforms [footnote 42] and Advanced British Standard [footnote 43] will put academic and technical education in England on an equal footing and ensure our skills and education system is fit for the future.

Enabling AI innovation and protecting intellectual property

27. The AI technology and creative sectors, as well as our media, are strongest when they work together in partnership. This government is committed to supporting these sectors so that they continue to flourish and are able to compete internationally. The Department for Culture, Media and Sport ( DCMS ) is working closely with publishers, the music industry, and other creative businesses to understand the impact of AI on these sectors, with a view to mitigating risks and capitalising on opportunities. Significant funding highlighted in the Creative Industries Sector Vision [footnote 44] will help enable AI -based R&D and innovation in the creative industries.

28. Creative industries and media organisations have particular concerns regarding copyright protections in the era of generative AI . Creative industries and rights holders are concerned at the large-scale use of copyright protected content for training AI models and have called for assurance that their ability to retain autonomy and control over their valuable work will be protected. At the same time, AI developers have emphasised that they need to be able to easily access a wide range of high-quality datasets to develop and train cutting-edge AI systems in the UK.

29. The Intellectual Property Office ( IPO ) convened a working group made up of rights holders and AI developers on the interaction between copyright and AI . The working group has provided a valuable forum for stakeholders to share their views. Unfortunately, it is now clear that the working group will not be able to agree an effective voluntary code.

30. DSIT and DCMS ministers will now lead a period of engagement with the AI and rights holder sectors, seeking to ensure the workability and effectiveness of an approach that allows the AI and creative sectors to grow together in partnership. The government is committed to the growth of our world-leading creative industries and we recognise the importance of ensuring AI development supports, rather than undermines, human creativity, innovation, and the provision of trustworthy information.

31. Our approach will need to be underpinned by trust and transparency between parties, with greater transparency from AI developers in relation to data inputs and the attribution of outputs having an important role to play. Our work will therefore also include exploring mechanisms for providing greater transparency so that rights holders can better understand whether content they produce is used as an input into AI models. The government wants to work closely with rights holders and AI developers to deliver this. Critical to all of this work will also be close engagement with international counterparts who are also working to address these issues. We will soon set out further proposals on the way forward.

Protecting UK citizens from AI -related bias and discrimination

32. AI has the potential to entrench bias and discrimination [footnote 45] , possibly leading to unfairly negative outcomes for different populations across a range of sectors. For example, unaccounted for bias in an AI -enabled automated decision making process could result in discriminatory outcomes against specific demographic characteristics in areas such as credit applications [footnote 46] or recruitment [footnote 47] . In line with our fairness principle, the department is working closely with the Equality and Human Rights Commission ( EHRC ) and ICO to develop new solutions to address bias and discrimination in AI systems [footnote 48] .

33. Both regulators and public sector bodies are acting to address AI -related bias and discrimination in their domains. The ICO has updated guidance on how our strong data protection laws apply to AI systems that process personal data to include fairness and has continued to hold organisations to account, for example through the issuing of enforcement notices [footnote 49] . The Office of the Police Chief Scientific Adviser published a Covenant for Using AI in Policing [footnote 50] which has been endorsed by the National Police Chiefs’ Council and should be given due regard by all developers and users of the technology in the sector.

Reforming data protection law to support innovation and privacy

34. Data is the foundation for modelling, training, and developing AI systems. But it is critical to respect relevant individual rights and data protection principles should be complied with when processing personal data in AI systems. The ICO has demonstrated how they can use data protection law to hold organisations to account through regulatory action and public communications where AI systems are processing personal data. The UK’s data protection framework, which is being reformed through the Data Protection and Digital Information Bill ( DPDI ), will complement our pro-innovation, proportionate, and context-based approach to regulating AI .

35. Current rules on automated decision-making are confusing and complex, undermining confidence to develop and use innovative technologies. The DPDI Bill will expand the lawful bases on which solely automated decisions that have significant effects on individuals can take place and provide a boost in confidence to organisations looking to use the technologies responsibly. It will continue to ensure that data subject rights are protected with safeguards in place. For example, data subjects will be provided with information on such decisions, have the opportunity to make representations, and can request human intervention or contest the decision. This will support innovation and reduce burdens on people and businesses, while maintaining data protection safeguards in line with the UK’s high standards of data protection.

Ensuring AI generated online content is trusted and safe

36. The government is committed to ensuring that people have access to accurate information and is supporting all efforts to promote verifiable sources to tackle the spread of false or misleading information. AI technologies are increasingly able to provide individuals with cheap ways to generate realistic content that can falsely portray people and events. Similarly, AI may increase volumes of unintentionally false, biased, or harmful content [footnote 51] . This may drive negative public perceptions of information quality and lower overall trust in information sources [footnote 52] .

37. We have published emerging practices to protect trust in online information including watermarking and output databases [footnote 53] . We will shortly launch a call for evidence on AI -related risks to trust in information to develop our understanding of this fast moving and nascent area of technological development, including possible mitigations. This will be aimed at researchers, academics, and civil society organisations with relevant expertise. We will also explore research into the wider and systemic impacts on the information ecosystem and potential solutions. We also continue to engage with news publishers and broadcasters, as vital channels for trustworthy and verifiable information, on the risks of AI to journalism.

Ensuring AI driven digital markets are competitive

38. AI is creating huge opportunities for innovation that benefits businesses and consumers across the economy. The markets for both the underlying AI technologies, such as foundation models, and products that use AI in new and innovative ways, are growing quickly.

39. Where these markets are competitive they will drive innovation and better outcomes for businesses and consumers. Successful firms will rightly grow and increase their market share, but it will be important that market power does not become entrenched by only a small number of firms.

40. The CMA will take steps to ensure that AI markets work well for all. In September 2023, the regulator published an initial review into the market for foundation models [footnote 54] . The report found that, while there will be many benefits to consumers from AI , these technologies could enable firms to gain or entrench market power. The Digital Markets, Competition and Consumers Bill, which is currently progressing through Parliament, will give the CMA additional tools to identify and address any competition issues in AI markets and other digital markets affected by recent developments in AI .

Ensuring AI best practice in the public sector

41. AI poses enormous opportunities for transforming productivity in the public sector. The UK is already leading the way, ranked third in the Government AI Readiness Index. [footnote 55] In November 2023, we announced that we are tripling the number of technical AI engineers and developers within the Cabinet Office to create a new AI Incubator for the government. These experts will design and implement AI solutions across government departments to drive improvements in public service delivery. This potential productivity improvement could, for example, save police up to 38 million hours per year and 750,000 hours every week [footnote 56] .

42. We are seizing the opportunities presented by AI to deliver better public services including health, education, and transport. For example, last year the Department of Health and Social Care ( DHSC ) and NHS launched the £21 million AI Diagnostic Fund to deploy these technologies in key, high demand areas such as chest X-rays and CT scans [footnote 57] . DfE has been examining how to maximise the benefits of AI in the education sector, including publishing a policy paper and a call for evidence on generative AI in education [footnote 58] , as well as running a hackathons project to further understand possible use cases. The findings of the hackathons will be published in spring of this year. The Department for Transport (DfT) is focused on the new Automated Vehicles Bill, designed to put the UK at the forefront of regulation of self-driving technology and in a strong position to realise an estimated £42 billion share of the global self-driving market. DfT also plans to publish its first Transport AI Strategy in 2024, to help both the department and the wider sector to grasp the opportunities and risks presented by new AI capabilities. Alongside this, the department continues to fund innovative Small and Medium sized Enterprises ( SMEs ) through its Transport Research and Innovation Grants scheme to support the next generation of AI tools and applications as well as trialling AI to support fraud identification in its grant-making processes.

43. Cabinet Office ( CO ) is leading on establishing the necessary underpinnings to drive AI adoption across the public sector, by improving digital infrastructure and access to data sets, and developing centralised standards. The government is also using the procurement power of the public sector to drive responsible and safe AI innovation. The Central Digital and Data Office ( CDDO ) has published guidance on the procurement and use of generative AI for the UK government [footnote 59] . Later this year, DSIT will launch the AI Management Essentials scheme, setting a minimum good practice standard for companies selling AI products and services. We will consult on introducing this as a mandatory requirement for public sector procurement, using purchasing power to drive responsible innovation in the broader economy.

44. This builds on the Algorithmic Transparency Recording Standard ( ATRS ), which established a standardised way for public sector organisations to proactively publish information about how and why they are using algorithmic methods in decision-making. Following a successful pilot of the standard, and publication of an approved cross-government version last year, we will now be making use of the ATRS a requirement for all government departments and plan to expand this across the broader public sector over time.

45. To inform the secure use of AI across government, the public sector, and beyond, the National Cyber Security Centre ( NCSC ) has published a range of guidance products on the cyber security considerations around using and developing AI [footnote 60] .

Misuse risks

Safeguarding democracy from electoral interference

46. The government is committed to strengthening the integrity of elections to ensure that our democracy remains secure, modern, transparent, and fair. AI has the potential to increase the reach of actors spreading disinformation online, target new audiences more effectively, and generate new types of content that are more difficult to detect [footnote 61] . Our Defending Democracy Taskforce is helping to reduce the threat of foreign interference in our democracy by bringing together a wide range of expertise across government, the intelligence community, and industry. In 2024, the Taskforce will be increasing its engagement with partners, collaborating with devolved governments, the police, local authorities, tech companies, and international partners.

47. We will always respond firmly to any threats to the UK’s democracy. The Elections Act 2022 introduced the new digital imprints regime, which will increase the transparency of digital political advertising (including AI -generated material), by requiring those promoting eligible digital campaigning material targeted at the UK electorate to include an imprint with their name and address. This will empower voters to know who is promoting political material online and on whose behalf. The Elections Act 2022 also revised the offence of undue influence. This will better protect voters from improper influences to vote in a particular way, or to not vote at all, and includes activities that deceive a person in relation to the administration of an election (such as the date of an electoral event or the location of a polling station).

48. The Online Safety Act 2023 will capture specific activity aimed at disrupting elections where it is a criminal offence in scope of the regulatory framework. This includes content that contains incitement to violence against electoral candidates and public figures, and the offence of undue influence. The foreign interference offence from the National Security Act 2023 has been added to the Online Safety Act as a “priority offence”, putting new responsibilities on online service providers and capturing attempts by foreign state actors to manipulate our information environment and undermine our democratic, political, and legal processes (including elections). The Online Safety Act has also updated Ofcom ’s statutory media literacy duty, requiring the regulator to heighten the public’s awareness of, and resilience to, misinformation and disinformation online.

49. We will consider the tools available to verify election-related content. This could include using watermarks to give people confidence in the content they are viewing. It is not just the government that needs to act. We will continue to work with tech companies to ensure that it is possible to report and remove fakes quickly. Building on discussions at the AI Safety Summit, we are collaborating with international and industry partners to address the shared risk of election interference.

Preventing the misuse of AI technologies

50. AI capabilities may be used maliciously, for example, to perform cyberattacks or design weapons [footnote 62] . Developments in AI can amplify existing risks by enabling less sophisticated threat actors to carry out more substantial attacks at a larger scale [footnote 63] . We are working with industry, academia, and international partners to find proportionate, practical mitigations to these risks. The 2023 refreshed Biological Security Strategy will ensure that by 2030 the UK is resilient to a spectrum of biological risks and a world leader in responsible innovation [footnote 64] . As set out in the National Vision for Engineering Biology, the government has identified screening of synthetic DNA as a responsible innovation policy priority for 2024 [footnote 65] . Prioritising this will allow us to continue reaping the economic rewards of engineering biology in the UK whilst improving the safety of the supply chain.

51. Some of the risks presented by AI systems are manifesting today as these technologies are misused to increase the scale, speed, and success of criminal offences. As discussed above, AI can provide users with increasing capability to produce false or misleading content. This can include material that constitutes a criminal offence such as fraud, online child sexual abuse, and intimate image abuse. The government has already moved to address some of these issues in the Online Safety Act 2023. Some AI technologies could be misused to commit identity-related fraud, such as producing false documentation used for immigration purposes. These capabilities present potential risks related to fraudulent access to public funds.

52. In order to address the potential criminal use of AI , we are reviewing the extent to which existing criminal law provides coverage of AI -enabled offending and harmful behaviour. AI may also present systemic risks to police capacity, institutional trust, and the evidential process. The government will make amendments to existing legal frameworks as required in order to protect law and order. AI also poses more opportunities for law enforcement to become more efficient at detecting and preventing crime. As such, these technologies may help mitigate some of the risks of AI -enabled criminal offences. For example, we are investing in AI models that allow police to detect and categorise the severity of child abuse images more effectively. We are also exploring how AI might enable officers to redact large amounts of text evidence more quickly.

53. To help organisations develop and use AI securely, the NCSC published guidelines for secure AI system development in November 2023. The government is now looking to build on this and other important publications by releasing a call for views in spring 2024 to obtain further input on our next steps in securing AI models, including a potential Code of Practice for cyber security of AI , based on NCSC ’s guidelines. International collaboration in this area is vital if we are to see meaningful change to the security of AI models, and we will be exploring ways to promote international alignment, such as via international standards.

54. This builds on our work to secure personal devices and critical infrastructure. The security regime in the Product Security and Telecommunications Infrastructure (“ PSTI ”) Act, scheduled to come into effect in 2024, will require manufacturers of consumer connectable products, such as AI -enabled smart speakers, to comply with minimum security requirements underpinned by the secure by design principle. This means no consumer connectable products in scope of the regime can be made available to UK customers unless the manufacturer has minimum security measures in place covering the product’s hardware and software, and, where appropriate, associated AI solutions. Beyond this, the National Protective Security Authority ( NPSA ) conducts research to understand how AI can, and will, enhance physical and personnel security. NPSA advises a wide range of organisations, including critical national infrastructure companies, on how to address AI -related threats and delivers campaigns to help protect valuable AI -related intellectual property for emerging technology companies.

Autonomy risks

55. In our discussion paper on frontier AI capabilities and risks [footnote 66] , we outlined potential future risks linked to the increasing autonomy of advanced AI systems. Some experts are concerned that, as AI systems become more capable across a wider range of tasks, humans will increasingly rely on AI to make important decisions. Some also believe that, in the future, agentic AI systems may have the capabilities to actively reduce human control and increase their own influence. New research on the advancing capabilities of agentic AI demonstrates that we may need to consider potential new measures to address emerging risks as the foundational AI technologies that underpin a range of applications continue to develop [footnote 67] .

56. In section 5.2, we set out proposals for new future responsibilities on developers of highly capable general-purpose AI . While the likelihood of autonomy risks is debated, we believe that our proposals introduce accountability, governance, and oversight for these developers as well as testing and benchmarking powerful AI systems to address these risks now and in the future. In particular, the testing conducted by the AI Safety Institute will identify systems with potentially hazardous capabilities (see sections 5.2 and 5.3 for more details on the role of the Institute). Testing has already begun and will increase in pace over the following months. These initial steps build the UK’s technical capability to assess and respond to emerging AI risks, ensuring our resilience to future technological developments.

5.2. Examining the case for new responsibilities for developers of highly capable general-purpose AI systems

57. As noted above, we are seeing rapid progress in the performance of highly capable general-purpose AI systems. We expect this to continue as organisations develop them with more compute, more data, and more efficient algorithms. Developers do not always know which capabilities a model may exhibit before testing [footnote 68] . Some companies have publicly stated their goal to build AI systems that are more capable than humans at a range of tasks [footnote 69] . With agentic AI capabilities on the horizon, we expect further transformative changes to our societies [footnote 70] .

58. The Prime Minister set out the government’s approach to managing risk at the frontier of AI development in October 2023. He stated: “My vision, and our ultimate goal, should be to work towards a more international approach to safety, where we collaborate with partners to ensure AI systems are safe before they are released [footnote 71] .”

59. We set out below how the UK has led the way with a technical approach, securing voluntary agreements on AI safety with key countries and companies. The new AI Safety Institute will work with its partners to test the most powerful new AI systems pre- and post- deployment. As the Prime Minister set out, we will not “rush to regulate” and potentially implement the wrong measures that may insufficiently balance addressing risks and supporting innovation.

60. Clearly, if the exponential growth of AI capabilities continues, and if – as we think could be the case – voluntary measures are deemed incommensurate to the risk, countries will want some binding measures to keep the public safe. Some countries, such as the United States are beginning to explore this through mandatory reporting requirements for the most powerful systems. We have seen significant interventions from leading figures in industry, science, and civil society, highlighting how governments should consider responding to the development [footnote 72] and we welcome continued close collaboration with these expert voices.

61. The UK will continue to lead the conversation on effective AI governance. In the section below, we set out some of the key questions that countries will have to grapple with when deciding how best to manage the risks of highly capable general-purpose AI systems, such as how to allocate liability across the supply chain and negotiate the open release of the most powerful systems. We will continue to discuss these questions with civil society, industry, and international partners to prepare for the future.

Box 2: What do we mean by highly capable general-purpose AI systems?

In the AI regulation white paper, we defined “foundation models” as “a type of AI model that is trained on a vast quantity of data and is adaptable for use on a wide range of tasks. Foundation models can be used as a base for building more specific AI models [footnote 73] .”

For the purposes of the AI Safety Summit, the UK defined “frontier AI ” as highly capable general-purpose AI models that can perform a wide variety of tasks and match or exceed the capabilities present in today’s most advanced models.

Today, this can include the cutting-edge foundation models that underpin consumer facing applications. However, it is important to note that, both today and in the future, highly capable AI systems could be underpinned by another technology.

In this consultation response, we focus our discussion on future responsibilities for the developers of highly capable general-purpose AI systems. Developers of these systems currently face the least clear legal responsibilities. The systems have the least coverage by existing regulation while presenting some of the greatest potential risk. This means some of those risks may not be addressed effectively. In the future, our regulatory approach might need to also allocate new responsibilities to developers of highly capable narrow systems as the framework continues to adapt to reflect new technological developments, different risks, or further analysis of accountability across the AI life cycle.

5.2.1. The regulatory challenges of highly capable general-purpose AI

62. The AI regulation white paper outlined a regulatory approach designed to adapt and keep pace with the rapid developments in AI technology. For the large majority of AI systems, our view is still that it is more effective to focus on how AI is used within a specific context than to regulate specific technologies. This is because the level of risk will be determined by where and how AI is used.

63. However, some highly capable AI systems can present substantial risks. Risk may increase when a highly capable system is general-purpose and can be used in a wide range of applications across different sectors. If a general-purpose AI system presents a risk of harm, this could mean that multiple sectors or applications could be at risk. This means that a single feature or flaw in one model might result in multiple harms across the whole economy. For example, if an AI system is used to underpin complex automated processes in both healthcare and recruitment, but the model’s outputs demonstrate bias in a way that is not sufficiently transparent or with impacts that are not adequately mitigated, this could result in discriminatory practices in these different services.

64. Highly capable general-purpose AI systems challenge a context-based approach to regulation as some of the risks that they contribute to may not be effectively mitigated by existing regulation. For example, the cross-sectoral impact of these systems may prevent harms from being sufficiently addressed. Even though some regulators can enforce existing laws against the developers of the most capable general-purpose systems within their current remits [footnote 74] , the wide range of potential uses means that general-purpose systems do not currently fit neatly within the remit of any one regulator, potentially leaving risks without effective mitigations [footnote 75] .

65. While some regulators demonstrate advanced approaches to addressing AI within their remits, many of our current legal frameworks and regulator remits may not effectively mitigate the risks posed by highly capable general-purpose AI systems. Many regulators in the UK can struggle to enforce existing rules on those actors designing, training, and developing the most powerful general-purpose AI systems. Similarly, it is not always clear how existing rules can be applied to effectively address the risks that highly capable general-purpose models can present. Existing rules and laws are frequently applied to the deployment or application level of AI , but the organisations deploying or using these systems may not be well placed to identify, assess, or mitigate the risks they can present. If this is the case, new responsibilities on the developers of highly capable general-purpose models may more effectively address risks.

66. Our ongoing work analysing life cycle accountability for AI , outlined in the white paper, may eventually need to consider the role of other actors across the value chain, such as data or cloud hosting providers, to determine how legal responsibility for AI may be distributed most fairly and effectively. This analysis will also consider how the unpredictable way future capabilities and risks may emerge could also expose further gaps in the regulatory landscape.

Case study 1: Liability as a barrier to AI adoption in the UK

“Count Your Pennies Ltd”, a fictional accountancy firm, purchases an “off the shelf” AI recruitment tool developed by a fictional UK company called “Quantum Talent Technologies”. The tool automatically shortlists candidates based on their application forms.

One fictional candidate, Ms Smith, queries why her application was rejected for a certain position given her clear suitability for the role. After receiving an unsatisfactory response from the recruiting manager, she files a discrimination claim. Through the investigation, it becomes clear that the AI tool is discriminatory. It was built using a powerful foundation model that was developed by a non-UK company and trained on biased historic employment data.

It’s common for the law to allocate liability to the last actor in the chain (in this case, “Count Your Pennies Ltd”). In limited circumstances, the law may also allocate liability to the actor immediately above in the supply chain (in this case, “Quantum Talent Technologies”) [footnote 76] .

For example, it can be difficult for equality law – which is the statutory framework designed to legally protect people against discrimination in the workplace and in wider society [footnote 77] – to allocate liability to anyone other than the end deployer. This could ultimately lead to harmful outcomes (if the actors most able to address risks and harms are not incentivised or held accountable) and undermine AI adoption and dampen innovation across the UK economy. We will continue to analyse challenges such as these as part of our ongoing policy work on life cycle accountability for AI .

67. While highly capable narrow AI systems are in scope of the regulatory framework for AI , these systems may require a different set of interventions if they present potentially dangerous capabilities. Narrow systems are more likely than general-purpose systems to be subject to effective regulation within the remit of an existing regulator. We will continue to gather evidence on whether the specialised nature of highly capable narrow systems demands a different approach to general-purpose systems

5.2.2. The role of voluntary measures in initially building an effective and targeted regulatory approach

68. We have already started to make the world safer today by securing commitments from leading AI companies on voluntary measures. Building on voluntary commitments brokered by the White House, the Secretary of State for Science, Innovation and Technology wrote to seven frontier AI companies prior to the AI Safety Summit requesting that they publish their safety policies. All seven companies published their policies before the AI Safety Summit, increasing transparency within the AI community and encouraging safe industry practice [footnote 78] . We also published a report on emerging processes for frontier AI safety to inform the future development of safety policies (see Box 3) [footnote 79] . In 2024, we will encourage AI companies to develop their AI safety and responsible capability scaling policies [footnote 80] . As part of this work, we will update our emerging processes guide by the end of the year.

Box 3: Emerging Processes for Frontier AI Safety

Ahead of the AI Safety Summit, the UK government outlined a set of emerging safety processes to provide information to companies on how they can ensure and maintain the safety of AI technologies.

The document covers nine emerging processes:

1. Responsible Capability Scaling - a framework for managing risk as organisations scale the capability of frontier AI systems, enabling companies to prepare for potential future, more dangerous AI risks before they occur.

2. Model Evaluations and Red Teaming - methods to assess the risks AI systems pose and inform better decisions about training, securing, and deploying them.

3. Model Reporting and Information Sharing - practices that increase government visibility of frontier AI development and deployment and enable users to make well-informed choices about whether and how to use AI systems.

4. Security Controls including Securing Model Weights - measures such as cyber security and other security controls that underpin AI system security.

5. Reporting Structure for Vulnerabilities - a process to enable outsiders to identify safety and security issues in an AI system.

6. Identifiers of AI -generated Material - tools to mitigate the creation and distribution of deceptive AI -generated content by providing information about whether content has been AI generated or modified.

7. Prioritising Research on Risks Posed by AI - research processes to identify and address the emerging risks posed by frontier AI .

8. Preventing and Monitoring Model Misuse - practices to identify and prevent intentional misuse of AI systems.

9. Data Input Controls and Audits - measures to identify and manage training data that is likely to increase the dangerous capabilities their frontier AI systems possess, and the risks they pose.

The document consolidated emerging thinking in AI safety from research institutes and academia, companies, and civil society, who the UK government collaborated and engaged with throughout its development. AI safety is an ongoing project and the processes and practices will continue to evolve through research and dialogue between governments and the broader AI ecosystem. The document provides a useful starting point for future frameworks for action both in the UK and globally.

69. Alongside these voluntary measures, at the AI Safety Summit, governments and AI companies agreed that both parties have a crucial role to play in testing the next generation of AI models, to ensure AI safety – both before and after models are deployed. In the UK, the newly established AI Safety Institute (see Box 4) leads this work. Leading AI tech companies have pledged to provide the Institute with priority access to their systems. The Institute has already begun testing, and is committed to doing so in partnership with other countries and their respective safety institutes. We will shortly provide an update on the AI Safety Institute’s approach to evaluations. Our assessment of the capabilities and risks of AI will also be underpinned by a new International Report on the Science of AI Safety [footnote 81] , chaired by leading AI pioneer Yoshua Bengio (see paragraph 87).

Box 4: The AI Safety Institute (AISI)

At present, frontier AI developers are building powerful systems that outpace the ability of government and regulators to make them safe. As such, the government’s first challenge is one of knowledge: we do not fully understand what the most powerful systems are capable of and we urgently need to plug that gap. This will be the task of the new AI Safety Institute. It will advance the world’s knowledge of AI safety by carefully examining, evaluating, and testing new frontier AI systems. In addition, it will research new techniques for understanding and mitigating AI risk, and conduct fundamental research on how to keep people safe in the face of fast and unpredictable progress in AI .

The AI Safety Institute’s work will be fundamental to informing the UK’s regulatory framework. It will provide foundational insights to our governance regime and help ensure that the UK takes an evidence-based, proportionate approach to regulating the risks of AI . It will initially perform three core functions:

  • Develop and conduct evaluations on advanced AI systems , aiming to characterise safety-relevant capabilities, understand the safety and security of systems, and assess their societal impacts.
  • Drive foundational AI safety research . The Institute’s research will support short and long-term AI governance. It will ensure the UK’s iterative regulatory framework for AI is informed by the latest expertise and lay the foundation for technically grounded international governance of advanced AI . Projects will range from rapid development of tools to inform governance, to exploratory AI safety research which may be underexplored by industry.
  • Facilitate information exchange , including by establishing – on a voluntary basis and subject to existing privacy and data regulation – clear information-sharing channels between the Institute and other national and international actors, such as policymakers, international partners, private companies, academia, civil society, and the broader public.

The goal of the Institute’s evaluations will not be to designate any particular AI system as “safe”; it is not clear that available techniques could justify such a definitive determination. The AI Safety Institute is not a regulator; its role is to develop the technical expertise to understand the capabilities and risks of AI systems, informing the government’s broader actions. Nevertheless, we expect progress in system evaluations to enable better informed decision making by governments and companies and act as an early warning system for some of the most concerning risks. If the AI Safety Institute identifies a potentially dangerous capability through its evaluation of advanced AI systems, the Institute will, where appropriate, address risks by engaging the developer on suitable safety mitigations and collaborating with the government’s established AI risk management and regulatory architecture.

The Institute is focused on the most advanced current AI capabilities and any future developments. It will consider open source systems as well as those deployed with various forms of access controls.

70. These voluntary actions allow us to test and learn what works in order to adapt our regulatory approach. We will strengthen our technical understanding to build wider consensus on key interventions, such as whether there should be conditions in which it would be right to pause the development of specific systems, as some have proposed.

71. While voluntary measures help us make AI safer now, the intense competition between companies to release ever-more-capable systems means we will need to remain highly vigilant to meaningful compliance, accountability, and effective risk mitigation. It may be the case that commercial incentives are not always aligned with the public good. If the market evolves such that there are a larger number of firms that are building highly capable systems, the governance of voluntary approaches will be much harder [footnote 82] . It will also be increasingly important to ensure the right accountability mechanisms and corporate governance frameworks are in place for companies building the most powerful systems.

5.2.3. The case for future binding measures

72. The section above highlights how the context-based approach may miss significant risks posed by highly capable general-purpose systems and leave the developers of those systems unaccountable. Whilst voluntary measures are a useful tool to address risks today, we anticipate that all jurisdictions will, in time, want to place targeted mandatory interventions on the design, development, and deployment of such systems to ensure risks are adequately addressed.

Foundation model supply chain

Foundation model supply chain.

Note: This is one possible model (there will not always be a separate or single company at each layer).

Image 2 : A diagram of the foundation model supply chain taken from the Ada Lovelace Institute’s ‘ Foundation Models Explainer ’.

While there are many different ways to understand and describe the often complex life cycles of AI technologies, this diagram illustrates that our proposed future measures would be clearly targeted at the small number of companies that work in the foundation model developer layer building highly capable general-purpose AI .

73. Predicting which systems are capable enough to lead to significant risk is not straightforward. In line with our proportionate approach, any future regulation would be targeted at the small number of developers of the most powerful general-purpose systems. We propose to do this by establishing dynamic thresholds that can quickly respond to advances in AI development. Our preliminary analysis indicates that initial thresholds could be based on forecasts of capabilities using a combination of two proxies: compute (i.e. the amount of compute used to train the model) and capability benchmarking (i.e. assessing capabilities in certain risk areas to identify where we think high capabilities result in high risk). At least for the time being, the combination of these proxies can predict AI capabilities reasonably well, however there might need to be a range of thresholds.

74. Any new obligations would ensure that the developers of the in-scope systems adhere to the principles set out in the AI regulation white paper including safety, security, transparency, fairness, and accountability. This could include transparency measures (for example, relating to the data that systems are trained on); risk management, accountability, and corporate governance related obligations; or actions to address potential harms, such as those caused by misuse or unfair bias before or after training.

75. The open release of AI has, overall, been beneficial for innovation, transparency, and accountability. A degree of openness in AI is, and will continue to be, critical to scientific progress, and we recognise that openness is core to our society and culture. However, while we are committed to defending the value of openness, we note that there is a balance to strike as we seek to mitigate potential risks. In this regard, we see an emerging consensus on the need to explore pre-deployment capability testing and risk assessment for the most powerful AI systems, including where systems might be released openly. Pre-deployment testing could inform the deployment options available for a model and change the risk prevention steps required of organisations prior to the model’s release. Recognising the complexity of the debate, we are working closely with the open source community and AI developers to understand their needs. Our engagement with those developing and using AI models that are highly capable, general-purpose, and open access will allow us to explore the need for nuanced and targeted policy options that minimise any negative impacts on valuable open source activity, whilst mitigating risks.

76. The challenges posed by AI will ultimately require legislative action in every country once understanding of risk has matured. Introducing binding measures too soon, even if highly targeted, could fail to effectively address risks, quickly become out of date, or stifle innovation and prevent people from across the UK from benefiting from AI in line with the adaptable approach set out in the AI regulation white paper, the government would consider introducing binding measures if we determined that existing mitigations were no longer adequate and we had identified interventions that would mitigate risks in a targeted way. As with any decision to legislate, the government would only consider introducing legislation if we were not sufficiently confident that voluntary measures would be implemented effectively by all relevant parties and if we assessed that risks could not be effectively mitigated using existing legal powers. Finally, prior to legislating, the government would need to be confident that we could mandate measures in a way that would significantly mitigate risk without unduly dampening innovation and competition.

77. We know there is more work to do to refine our approach to regulating the most capable AI systems and the actors that design, develop, and deploy them. We look forward to developing our proposals by working closely with industry, academia, civil society, and the wider public. In Box 5, below, we set out the key questions that will guide our policy development.

Box 5: Key questions for policy development on the future regulation of highly capable general-purpose systems

Building on the evidence we received to our AI regulation white paper consultation on the topic of life cycle accountability and foundation models, over the coming months we will work closely with a range of experts and international partners to examine the questions below. We will publish findings from this engagement in a series of expert discussion papers. We will also publish the next iteration of our thinking and the steps we are taking in relation to the most capable AI systems.

Which specific risks should be addressed through future regulatory interventions targeted at highly capable AI systems? How do we ensure the regime is resilient to future developments?

When should the government and regulators intervene? Which systems should we be targeting? What would a compound threshold for intervention look like? Is compute a useful proxy for now, if thresholds remain dynamic? What about capability benchmarking?

Which obligations should be imposed on developers? Should the obligations be linked to our AI regulation principles? How do we ensure that the obligations are flexible but clear? At what stage could it be necessary to pause model development?

What, if any, new regulatory powers are required? How would this work alongside the existing regulatory landscape?

What should enforcement of any new regulation look like? What legal responsibilities should developers of in-scope systems have? Are updates to civil or criminal liability frameworks needed?

How do we provide regulatory certainty to drive responsible AI innovation while retaining an adaptable regime that can accommodate fast technical developments? How do we avoid creating barriers to market entry and scale-up?

Should certain capabilities trigger controls on open release? What would the negative consequences be? How should thresholds be set? What controls could be imposed?

What are the roles of existing transparency and accountability frameworks? How can strong transparency and good accountability be encouraged or assured to support responsible development of the most capable AI systems?

Should developers of highly capable AI systems be subject to specific  corporate governance requirements? Is there a role for requirements on developers of highly capable AI systems to consider and mitigate risks to society or humanity at large?

How do potential new measures on highly capable AI systems link to wider life cycle accountability for AI ? Are other actors in the AI value chain also hard for regulators to reach in a way that hampers our ability to address risk and support AI innovation and adoption?

78. As we set out in the AI regulation white paper, our intention is for our regulatory framework to apply to the whole of the UK subject to existing exemptions and derogations for unique operating requirements, such as defence and national security. However, we recognise that AI is used across a wide variety of sectors, some of which are reserved and some of which are devolved. As our policy develops and we consider the introduction of binding requirements on the developers of the most capable general-purpose systems, we will continue to assess any devolution impacts and need for extraterritorial reach.

79. We are committed to engaging the territorial offices and devolved administrations on both the design and delivery of the regulatory framework, so that businesses and citizens across the UK benefit from our regulatory approach.

5.3. Working with international partners to promote effective collaboration on AI governance

80. AI knows no borders and its impact will shape societies and economies in all corners of the world: AI developed in one nation will increasingly affect the lives of citizens living in others. Effective governance of AI will therefore require equally impactful international cooperation, which must build on the work of existing multilateral and multi-stakeholder fora and initiatives.

81. The UK is an established global leader in AI with a history of driving forward the international conversation and taking clear, decisive action to build bilateral and multilateral agreement. Our focus to date has been on collaborative action to support the development of AI in line with the context-based framework and principles set out in the AI regulation white paper [footnote 83] . This involves working alongside different groups of countries in accordance with need and acting in a targeted and proportionate manner. Our goal remains to work with others to build an international community that is able to realise the opportunities of AI on a global scale. We promote our values and collaborate where suitable to address the most pressing current and future AI -related risks. We carefully balance safety and innovation, acting alongside our partners to promote the international design, development, deployment, and use of the highest potential AI systems.

82. We will continue to act through bilateral partnerships and multilateral initiatives – including future AI Safety Summits – to promote safe, secure, and trustworthy AI , underpinned by effective international AI governance. Throughout this we will adopt a multistakeholder approach: We will collaborate with our international partners by working with representatives from industry, academia, civil society, and government to ensure we can reap the extraordinary benefits afforded by these technologies [footnote 84] .

83. Working with these networks, we will unlock the opportunities presented by AI while addressing potential risks. In support of this, we maintain close relationships with our international partners across the full range of issues detailed in section 5.1, as well as on our respective emerging domestic approaches.

84. Domestic and international approaches must develop in tandem. In developing our own approach to AI regulation we will, therefore, both influence and respond to international developments. We will continue to proactively engage with the international landscape to ensure the appropriate degree of cooperation required for effective AI governance. We will achieve appropriate levels of coherence with other regulatory regimes, promote safety, and minimise potential barriers to trade – maximising opportunities for individuals and businesses across the UK and beyond. We will continue to work with our international partners to drive the development and adoption of tools for trustworthy AI , such as assurance techniques and global technical standards, in order to promote interoperability and avoid fragmentation.

85. We will continue to recognise the critical nature of safety in underpinning, but not supplanting, all other aspects of international AI collaboration. As the Prime Minister Rishi Sunak set out, our “vision, and our ultimate goal, should be to work towards a more international approach to safety” [footnote 85] . As noted above, the UK hosted the first ever AI Safety Summit in November 2023 and secured the Bletchley Declaration, a landmark agreement between 29 parties, including 28 countries from across the globe and the European Union [footnote 86] . The Declaration builds a shared understanding of the opportunities and risks that AI presents and the need for collaborative action to ensure the safety of the most powerful AI systems now and in the future. A number of countries and companies developing frontier AI also agreed to state-led testing of the next generation of systems, including through partnerships with newly announced AI Safety Institutes (see Box 4 for more detail) [footnote 87] .

86. The pace of AI development shows no sign of slowing down, so the UK is committed to establishing enduring international collaboration on AI safety, building on the foundations of the AI Safety Summit agreements. To maintain this momentum and ensure that action is taken to secure AI safety, the Republic of Korea has agreed to co-host the next AI Safety Summit with the UK. France has agreed to host the following summit.

87. The UK’s AI Safety Institute represents one of our key contributions to international collaboration on AI . The Institute will partner with other countries to facilitate collaboration between governments on AI safety testing and governance, and develop their own capability. The Institute will facilitate international collaboration in three key ways:

Partnerships : the AI Safety Institute has agreed a partnership with the US AI Safety Institute and with the government of Singapore to collaborate on AI safety testing and is in regular dialogue on AI safety issues with international partners.

International Report on the Science of AI Safety [footnote 88] : The report was first unveiled as the State of the Science Report at the UK AI Safety Summit in November, where represented countries agreed to the development of an internationally authored report on the capabilities and risks of advanced AI . Rather than producing new material, it will summarise the best of existing research and identify priority research areas, providing a synthesis of the existing knowledge of risks from advanced AI .

Information Exchange : the AI Safety Institute’s evaluations and research are the first step in addressing the insight gaps between industry, governments, academia, and the public. This will ensure relevant parties, including international partners, receive the information they need to inform the development of shared protocols.

88. The UK also plays a proactive role through a range of multilateral initiatives to drive forward our ambition to promote the safe and responsible design, development, deployment, and use of AI . This includes:

G7 : Working in cooperation with our partners in this forum, the UK has made significant progress to quickly respond to new technological developments and drive work on effective international AI governance. In December 2023, under Japan’s Presidency, G7 Leaders welcomed the Hiroshima AI Process Comprehensive Policy Framework that includes international guiding principles for all AI actors and a Code of Conduct for organisations developing advanced AI systems, as well as a work plan to further advance these outcomes [footnote 89] . We encourage AI actors, and especially AI developers, to further engage and support these outcomes. We look forward to collaborating further on AI under Italy’s G7 Presidency in 2024.

G20 : In September 2023, as part of India’s G20 Presidency, the UK Prime Minister agreed to and endorsed the New Delhi Leaders’ Declaration alongside all other G20 Members [footnote 90] . The Declaration reaffirmed the UK’s commitment to the 2019 G20 AI Principles and emphasised the importance of a governance approach that balances the benefits and risks of AI and promotes responsible AI for achieving the UN Sustainable Development Goals [footnote 91] . The UK will work closely with Brazil on their AI ambitions as part of their 2024 G20 Presidency, which will centre on AI for inclusive sustainable development.

Global Partnership on AI ( GPAI ) : The UK continues to actively shape GPAI ’s multi-stakeholder project-based activities to guide the responsible development and use of AI grounded in human rights, inclusion, diversity, innovation, and economic growth. The UK was pleased to attend the December 2023 GPAI Summit in New Delhi, represented by the Minister for AI , Viscount Camrose, and to both endorse the GPAI New Delhi Ministerial Declaration [footnote 92] and host a side-event on outcomes and next steps following the AI Safety Summit. The UK has also begun a two-year mandate as a Steering Committee member and will work with India’s Chairmanship to ensure GPAI is reaching its full potential.

Council of Europe : The UK is continuing to work closely with like-minded nations on the proposed Council of Europe Convention on AI to help protect human rights, democracy, and rule of law. The Convention offers an opportunity to ensure these important values are codified internationally as one part of a wider approach to effective international governance.

Organisation for Economic Co-operation and Development ( OECD ) : The UK is an active member of the Working Party on AI Governance ( AIGO ) and recognises the forum’s role in supporting the implementation of the OECD AI Principles and enabling the exchange of experience and best practice across member countries. In 2024, the UK will support the revision of the OECD AI Principles [footnote 93] and continue to provide case studies from the UK’s Portfolio of AI Assurance Techniques [footnote 94] to the OECD ’s Catalogue of Tools and Metrics of Tools for Trustworthy AI [footnote 95] .

United Nations ( UN ) and its associated agencies : Given the organisation’s unique role in convening a wide range of nations, the UK recognises the value of the UN -led discussions on AI and engages regularly to shape global norms on AI . In July 2023, the UK initiated and chaired the first UN Security Council briefing session on AI , and the Deputy Prime Minister chaired a session on frontier AI risks at UN High Level Week in September 2023. The UK continues to collaborate with a range of partners across UN AI initiatives, including negotiations for the Global Digital Compact, which aims to facilitate the Sustainable Development Goals through technologies such as AI , monitoring the implementation of the UNESCO Recommendation on the Ethics of AI [footnote 96] , and engaging constructively at the International Telecommunication Union, which hosted the ‘ AI for Good’ Summit in July 2023. The UK will also continue to work closely with the UN AI Advisory Body and is closely reviewing its interim report: Governing AI for Humanity [footnote 97] .

Global Standards Development Organisations ( SDOs ) : The UK is engaging directly with SDOs , such as the ISO and IEC , and is supporting developments in technical AI standards. The UK champions a global digital standards ecosystem that is open, transparent, and consensus-based. The UK also aims to support innovation and strengthen a multi-stakeholder, industry-led model for the development of technical AI standards, including through initiatives such as the UK’s AI Standards Hub [footnote 98] . We support UK stakeholders to participate in SDOs to both leverage the benefits of global technical standards here in the UK and deliver global digital technical standards shaped by democratic values.

89. Additionally, the UK is committed to ensuring that the benefits of AI are widely accessible. This includes working with international partners to fund safe and responsible AI projects for development around the world. As announced at the AI Safety Summit, the UK is contributing £38 million through its new AI for Development programme to support safe, responsible and inclusive AI innovation to accelerate progress on development challenges, focused initially in Africa [footnote 99] . This is part of an £80 million boost in AI programming to combat inequality and boost prosperity in Africa, with the UK working alongside Canada, the Bill and Melinda Gates Foundation, the USA, Google, Microsoft, and African partners, including Kenya, Nigeria, and Rwanda among others.

90. AI is now also fundamental to our bilateral relationships and, in some cases, it is suitable to build deeper and more committed bilateral partnerships alongside multilateral engagement to further our shared interests. We have therefore pursued bilateral agreements on areas including responsibly developing and deploying AI with key international partners, to build the foundation for further collaboration on AI governance. For example, as part of the DSIT International Science Partnerships Fund [footnote 100] , UKRI will invest £9 million to bring together researchers and innovators in bilateral research partnerships with the US. These partnerships will focus on developing safer, responsible, and trustworthy AI as well as AI for scientific uses. Since the publication of the AI regulation white paper in March 2023 we have signed:

The Atlantic Declaration with the US [footnote 101] : which develops our strong partnership on AI , underpinned by our shared democratic values and our ambition to promote safe and responsible AI innovation across the world. Work under the 2023 Atlantic Declaration will ensure that our unique alliance is reinforced for the challenges of new technological developments.

The Hiroshima Accord with Japan [footnote 102] : which commits to focus on promoting human-centric and trustworthy AI and interoperability between our AI governance frameworks.

The Downing Street Accord with the Republic of Korea [footnote 103] : which builds on the progress achieved on safe, responsible AI development, including at the AI Safety Summit – the next edition of which will be co-hosted by the Republic of Korea and the UK.

The Joint Declaration on a Strategic Partnership with Singapore [footnote 104] : which harnesses expertise in new technologies such as AI from the UK and Singapore. DSIT also signed a Memorandum of Understanding ( MoU ) on Emerging Technologies in June 2023 with Singapore’s Infocomm Media Development Authority ( IMDA ). In this MoU , both parties agreed to collaborate on AI governance and to facilitate the development of effective and interoperable AI assurance mechanisms.

91. We have a number of other important bilateral relationships on AI with countries across the world and we intend, where suitable, to further build such agreements to strengthen these partnerships, such as through bilateral MoUs and Free Trade Agreements.

92. Only through effective global collaboration will the UK and our partners worldwide unlock the opportunities and mitigate the associated risks of AI . We will continue to engage our international partners to support responsible AI innovation that effectively and proportionately addresses potential AI harms and aligns with the principles established in the AI regulation white paper. We will also work together to promote coherence between our AI governance frameworks to ensure that businesses can operate effectively in both the UK and wider global markets and to ensure that AI developments benefit people around the world.

5.4. An AI regulation roadmap of our next steps

93. In 2024, we will:

Continue to develop our domestic policy position on AI regulation by:

Engaging with a range of experts on interventions for highly capable AI systems, including questions on open release, in the summer.

Publishing an update on our work on new responsibilities for developers of highly capable general-purpose AI systems by the end of the year.

Collaborating across government and with regulators to analyse and review potential gaps in existing regulatory powers and remits on an ongoing basis.

Working closely with the AI Safety Institute, which will provide foundational insights to our central AI risk assessment activities and inform our approach to AI regulation, on an ongoing basis. The AI Safety Institute will ensure that the UK takes an evidence-based, proportionate response to regulating the risks of AI .

Progress action to promote AI opportunities and tackle AI risks by:

Conducting targeted engagement on our cross-economy AI risk register and plan to assess the regulatory framework from the spring onwards.

Releasing a call for views in spring to obtain further input on our next steps in securing AI models, including a potential Code of Practice for cyber security of AI , based on NCSC ’s guidelines.

Establishing a new international dialogue to defend democracy and address shared risks related to electoral interference ahead of the next AI Safety Summit.

Launching a call for evidence on AI -related risks to trust in information and related issues such as deepfakes.

Exploring mechanisms for providing greater transparency, including measures so that rights holders can better understand whether content they produce is used as an input into AI models.

Phasing in the mandatory requirement for central government departments to use the Algorithmic Transparency Recording Standard ( ATRS ) over the course of the year.

Build out the central function and support regulators by:

Launching a new £10 million programme to support regulators to identify and understand risks in their domain and to develop their skills and approaches to AI .

Establishing a steering committee to support and guide the activities of a formal regulator coordination structure within government in the spring.

Asking key regulators to publish updates on their strategic approach to AI by 30 April.

Collaborating with regulators to iterate and expand our initial cross-sectoral guidance on implementing the principles, with further updates planned by summer.

Encourage effective AI adoption and provide support for industry, innovators, and employees by:

Launching the pilot AI and Digital Hub with the DRCF in the spring.

Publishing an Introduction to AI Assurance in spring.

Publishing updated guidance on the use of AI within HR and recruitment in spring.

Publishing a full AI skills framework that incorporates feedback to our consultation and supports employers, employees, and training providers to identify upskilling routes for AI in spring.

Launching the AI Management Essentials scheme to set a minimum good practice standard for companies selling AI products and services by the end of the year.

Publishing an update on our emerging processes guide by the end of the year.

Support international collaboration on AI governance by:

Actioning our newly announced £9 million partnership with the US on responsible AI as part of the DSIT International Science Partnerships Fund.

Publishing the first iteration of the International Report on the Science of AI Safety in spring.

Sharing new knowledge with international partners through the AI Safety Institute on an ongoing basis.

Supporting the Republic of Korea and France on the next AI Safety Summits on an ongoing basis, and considering the possible role of AI Safety Summits beyond these.

Continuing bilateral and multilateral partnerships on AI , including the G7 , G20 , Council of Europe, OECD , United Nations, and GPAI , on an ongoing basis.

6. Summary of consultation evidence and government response

94. This chapter provides a summary of the written evidence we received in response to our consultation followed by the government response. This chapter is structured by the 10 categories that we used to group our 33 consultation questions:

  • The revised cross-sectoral AI principles.
  • A statutory duty to regard.
  • New central functions to support the framework.
  • Monitoring and evaluation of the framework.
  • Regulator capabilities.
  • Tools for trustworthy AI .
  • Final thoughts.
  • Legal responsibility for AI .
  • Foundation models and the regulatory framework.
  • AI sandboxes and testbeds.

95. In total, we received 409 written consultation responses from organisations and individuals. Annex A provides an overview of who we received responses from and outlines our method of analysis. We also proactively engaged with 364 individuals through roundtables, technical workshops, bilaterals, and a programme of ongoing regulator engagement. While we weave insights from this engagement throughout our analysis, Annex A provides a detailed overview of our engagement findings.

6.1. The revised cross-sectoral AI principles

1. Do you agree that requiring organisations to make it clear when they are using AI would improve transparency?

2. Are there other measures we could require of organisations to improve transparency for AI ?

3. Do you agree that current routes to contest or get redress for AI -related harms are adequate?

4. How could current routes to contest or seek redress for AI -related harms be improved, if at all?

5. Do you agree that, when implemented effectively, the revised cross-sectoral principles will cover the risks posed by AI technologies?

6. What, if anything, is missing from the revised principles?

Summary of questions 1-6:

96. Over half of respondents agreed that, when implemented effectively, the revised principles would cover the key risks posed by AI technologies. The revised principles included safety, security and robustness; appropriate transparency and explainability; fairness; accountability and governance; and contestability and redress. However, respondents also advocated for the explicit inclusion of human rights, operational resilience, data quality, international alignment, systemic risks and wider societal impacts, sustainability, and education and literacy.

97. Respondents wanted to see further detail on the implementation of the principles, regulator capability, and interactions with existing law. Respondents consistently stressed the fast pace of technological change and reflected that the framework should be adaptable and supported by monitoring and evaluation. Some respondents were concerned that the principles would not be sufficiently enforceable, citing a lack of statutory backing.

98. There was strong support for a range of transparency measures from respondents. Respondents emphasised that transparency was key to building public trust, accountability, and an effective and verifiable regulatory framework. A majority of respondents agreed that a requirement for organisations to make it clear when they are using AI would improve transparency. Those who disagreed felt that labelling AI use would be either insufficient or disproportionately burdensome. Respondents suggested a range of transparency measures including the public disclosure of inputs like compute and data; labelling AI use and outputs; opt-ins and human alternatives to automated processing; explanations for AI outcomes, impacts and limitations; public or organisational AI registers; disclosure of model details to regulators; and independent assurance tools including audits and technical standards.

99. Most respondents reported that current routes to contest or seek redress for AI -related harms through existing legal frameworks are not adequate. Respondents noted that it can be difficult to identify AI -related harms and the high costs of litigation often prevents individuals from seeking redress. Many respondents wanted to see the government clarify the legal rights and responsibilities relating to AI , with many suggesting doing so through regulatory guidance. Some endorsed the introduction of statutory requirements. Respondents recommended establishing accessible redress routes, with some advocating for a central, cross-sector redress mechanism such as a dedicated AI ombudsman. Respondents also noted that international agreements would be needed to ensure effective routes to contest or seek redress for AI -related harms across borders. Respondents emphasised that better AI transparency would help make redress more accessible across a broad range of potential harms, including intellectual property infringement.

100. The government wants to ensure that the UK maintains its position as a global leader in AI . This means promoting safe, responsible innovation to ensure that we maximise the benefits AI can bring across the country. Our cross-sectoral principles set out our expectations for the responsible design, development, and application of AI to help guide businesses and organisations building and using these technologies. We are encouraged to see that most respondents agree that the revised cross-sectoral principles will cover the risks posed by AI when implemented effectively.

101. We expect regulators to apply the principles within their existing remits and in line with our existing laws and values, respecting the UK’s long history of democracy, strong rule of law, and commitments to human rights and environmental sustainability. As aspects of these values and rules are enshrined in the law that regulators are bound to follow, we do not think it is necessary to include democracy, human rights, the rule of law, or sustainability specifically within the principles themselves. The guidance we are publishing alongside this consultation response will support regulators to implement the principles within their respective domains.

102. The principles already cover issues raised by respondents linked to both operational resilience (safety, security, and robustness) and data protection (transparency, fairness, and accountability). We expect all actors across the AI life cycle to adhere to existing legal frameworks, including data protection law. The UK’s existing data protection legislation (UK GDPR and the Data Protection Act 2018) regulates the development of AI systems and other technologies where personal data is involved. The Data Protection and Digital Information Bill will clarify the rights of data subjects to specific safeguards when subject to solely automated decisions that have significant effects on them. Furthermore, the Information Commissioner’s Office ( ICO ) has created specific guidance on how to use data for AI in compliance with data protection law. Beyond the scope of data protection law [footnote 105] . Beyond the scope of data protection law, the government is assessing a range of possible interventions aligned with the principles as part of our work to encourage the responsible and safe development of highly capable AI . For example, we are exploring if and how to introduce targeted measures on developers of highly capable general-purpose AI systems related to transparency requirements (for example, on training data), risk management, and accountability and corporate governance related obligations. Similarly, our central risk assessment activities will identify and monitor a range of risks, providing cross-economy oversight that will capture systemic risks and wider societal impacts.

103. We acknowledge the broad support for transparency and we will continue our work assessing whether and which measures provide the most meaningful transparency for AI end users and actors across the AI life cycle. It is important that we take an evidence-based approach to transparency. The Algorithmic Transparency Recording Standard ( ATRS ) is a practical mechanism for transparency that was developed through public engagement and has been piloted across the UK [footnote 106] . The ATRS helps public sector organisations provide clear information about algorithmic tools they use in decision-making. As mentioned in section 5.1, we will now be making use of the ATRS a requirement for all government departments and plan to expand this across the broader public sector over time. While measures like watermarking can help users identify AI generated content, we need to ensure that proposed interventions are robust, cannot be easily overridden, and achieve positive outcomes. To establish greater transparency on AI outputs, we published an “Emerging processes for frontier AI safety” document that outlines three areas of practice related to identifying AI generated content, including research techniques, watermarking, and AI output databases [footnote 107] . As mentioned in section 5.2.2, we will update this guide by the end of the year and continue to encourage AI companies to develop best practices.

104. Our expert regulators are already using their existing remits to implement the AI principles, including the contestability and redress principle which includes expectations about clarifying existing routes to redress. We recognise the link between the fair and effective allocation of liability throughout the AI life cycle and the availability and clarity of routes to redress. Our work to explore existing liability frameworks and accountability through the value chain is ongoing and includes analysis of the existence of redress mechanisms. As a first step towards ensuring fair and effective allocation of accountability and liability, the government is considering introducing targeted binding requirements on developers of highly capable general-purpose AI systems which may involve creating or allocating new regulatory powers.

6.2. A statutory duty to regard

7. Do you agree that introducing a statutory duty on regulators to have due regard to the principles would clarify and strengthen regulators’ mandates to implement our principles, while retaining a flexible approach to implementation?

8. Is there an alternative statutory intervention that would be more effective?

Summary of questions 7-8:

105. Most respondents somewhat or strongly agreed that introducing a statutory duty on regulators to have due regard to the principles set out in the AI regulation white paper would clarify and strengthen regulators’ mandates to implement the principles while retaining a flexible approach to implementation. However, nearly a quarter noted that regulators would need enhanced resources and capabilities in order to enact a statutory duty effectively.

106. Around a third of respondents argued that additional, targeted statutory measures would be necessary to effectively implement the regulatory framework. Many suggested expanding regulator powers, noting that the existing statutory remits of some regulators would limit their ability to implement the framework. In particular, respondents raised the need to review and potentially expand the investigatory powers and capabilities of regulators in regard to AI .

107. Some advocated for wider, horizontal statutory measures such as specific AI legislation, a new AI regulator, and strict rules about the use of AI in certain contexts.

108. Other respondents felt that, if rushed, the implementation of a duty to regard could disrupt regulation, innovation, and trust. These respondents recommended that the duty should be reviewed after a period of non-statutory implementation, particularly to observe interactions with existing law and regulatory remits. Some respondents 1. noted that the end goal and timeframes for the AI regulatory framework were not clear, causing uncertainty.

109. We are encouraged that respondents to this question are enthusiastic about the proper and effective implementation of our cross-sectoral AI principles. We welcome the broad support for a statutory duty on regulators, recognising that respondents also gave conditions and alternatives that could be used to implement the framework effectively. As set out in the AI regulation white paper, we anticipate introducing a statutory duty on regulators requiring them to have due regard to the principles after reviewing an initial period of non-statutory implementation.

110. We acknowledge concerns from respondents that rushing the implementation of a duty to regard could cause disruption to responsible AI innovation. We will not rush to legislate but will evaluate whether it is necessary and effective to introduce a statutory duty to have due regard to the principles on regulators. We currently think that a non-statutory approach offers critical adaptability but we will keep this under review, for example by assessing the updates on strategic approaches to AI that the government has asked a number of regulators to publish by 30 April 2024. We will also work with government departments and regulators to analyse and review potential gaps in existing regulatory powers and remits.

111. We are pleased to see that many regulators are taking proactive steps to address AI and implement the principles within their remits. This includes work by the Competition and Markets Authority ( CMA ), Advertising Standards Authority (ASA), and Office of Communications ( Ofcom ) [footnote 108] . Others are progressing their existing plans in ways that align with these principles, such as the ICO and Medicines and Healthcare products Regulatory Agency ( MHRA ) [footnote 109] .

112. We continue to work closely with regulators to develop the framework, ensure coherent implementation, and build regulator capability. To support a coherent approach across sectors, we are publishing initial guidance to regulators alongside this response on how to apply the cross-sectoral AI principles within their existing remits. We will update this guidance over time to ensure that it reflects developments in our regime and technological advances in AI . We will establish a steering committee by spring 2024 to support and guide the activity of the central regulator coordination function (see section 5.1.2 for details).

113. We note respondents’ concerns across the consultation that any new rules for AI should not contradict or duplicate existing laws. We will continue to evaluate any potential gaps or frictions within the existing statutory remits of regulators and current legislative frameworks. In the white paper, we said that we would keep the wider AI landscape under review in order to inform future iterations of the regulatory framework, including whether further interventions on foundation models may be required. We will consult on our plan for monitoring and evaluating the regulatory framework in 2024 (see our response to questions on monitoring and evaluation in section 6.4 for more detail).

6.3. New central functions to support the framework

9. Do you agree that the functions outlined in section 3.3.1 would benefit our AI regulation framework if delivered centrally?

10. What, if anything, is missing from the central functions?

11. Do you know of any existing organisations who should deliver one or more of our proposed central functions?

12. Are there additional activities that would help businesses confidently innovate and use AI technologies?

12.1. If so, should these activities be delivered by government, regulators, or a different organisation?

13. Are there additional activities that would help individuals and consumers confidently use AI technologies?

13.1. If so, should these activities be delivered by government, regulators, or a different organisation?

14. How can we avoid overlapping, duplicative, or contradictory guidance on AI issued by different regulators?

Summary of questions 9-14:

114. Nearly all respondents agreed that delivering the proposed functions centrally would benefit the AI regulation framework, with many praising the approach for ensuring that the government can monitor and iterate the framework.

115. While respondents widely supported the proposed central functions, many wanted more detail on each function and its activities. Some respondents felt there should be a greater emphasis on partnerships and collaboration to deliver the activities. Respondents also wanted more detail on international collaboration. Some suggested that the government should prioritise building the central risk function. Of these responses, a few noted that more consideration should be given to ethical and societal risks.

116. Respondents emphasised that the regulatory functions should build from the existing strengths of the UK’s regulatory landscape, with approximately a third identifying regulators as organisations who should deliver one or more central functions. Overall, respondents emphasised that effective delivery would require collaboration between government, regulators, industry, civil society, academia, and the general public. Over a quarter of respondents felt that technology-focused research institutes and think tanks could help deliver the central functions.

117. Respondents suggested a range of additional activities that government and regulators could offer to support industry. Around a third of respondents felt that training products and educational resources would help organisations to apply the principles to everyday business practices. Nearly a quarter suggested that regulators should produce guidance to allow businesses to innovate confidently. Some noted the importance of internationally interoperable frameworks for AI regulation to ensure a low compliance burden on organisations building, selling, and using AI technologies. Respondents also argued that more work is needed to ensure that businesses have access to high-quality, diverse, and ethically-sourced data to support their AI innovation efforts.

118. When thinking about additional activities for individuals and consumers, respondents prioritised transparency from the cross-sectoral principles, with nearly half arguing that individuals and consumers should be able to identify when and how AI is being used by a service or organisation. More than a third of respondents felt that education and training would enable consumers to use AI products and services safely and more effectively.

119. Around a third suggested that the proposed central functions would be the most effective mechanism to avoid overlapping, duplicative, or contradictory guidance.

120. We welcome the strong support for the central functions proposed in the AI regulation white paper to coordinate, monitor, and adapt the AI framework. Together, these functions will provide clarity, ensure the framework works as intended, and future-proof the UK’s regulatory approach. That is why we have already started to establish the central function within government to undertake the activities proposed in the white paper (see section 5.1.2 for details).

121. We note respondents’ concerns around the potential risks posed by the rapid developments in AI technology. We have already established the risk monitoring and assessment activities of the central function within DSIT , reflecting the strong recommendation from respondents to operationalise cross-economy AI risk management as a priority. Our centralised risk assessment activities will identify, measure, and monitor existing and emerging AI risks using expertise from across government, industry, and academia, including the AI Safety Institute. This will allow us to monitor risks holistically and identify any potential gaps in our approach. Horizon scanning will extend our central risk assessment activities, monitoring emerging AI trends and opportunities to maximise benefits while taking a proportionate approach to AI risks. This year, we will conduct targeted engagement on our cross-economy AI risk register.

122. Reflecting respondents’ views that the proposed central function will help regulators avoid producing overlapping, duplicative, or contradictory guidance, we are developing a coordination function to support regulators to interpret and apply the principles within their remits (see section 5.1.2 for detail). As part of this, we will establish a steering committee in the spring with government representatives and key regulators to support knowledge exchange and coordination on AI governance. To further support regulators and ensure that the UK’s strength in AI research is fully utilised in our regulatory framework, we have also announced a £10 million package to support regulator AI capabilities and a new commitment by UK Research and Innovation ( UKRI ) to improve links between regulators and the skills, expertise, and activities supported by their future investments in AI research.

123. To ensure appropriate levels of cohesion with emerging approaches to AI regulation in other jurisdictions, we will continue to work with international partners on regulatory interoperability, including technical standards and assurance techniques, to make it easier for UK companies to attract overseas investment and trade internationally. For more detail, see section 5.3 and our response to questions on tools for trustworthy AI in 6.6.

124. Alongside this, we have announced a new pilot regulatory service to be hosted by the Digital Regulation Cooperation Forum ( DRCF ) to make it easier for AI and digital innovators to navigate the regulatory landscape (see our response to questions on AI sandboxes for more detail: section 6.10).

125. We remain committed to the iterative approach set out in the white paper, anticipating that our framework will need to evolve as new risks or regulatory gaps emerge. Our monitoring and evaluation activities will assess if, when, and how we make changes to our framework, gathering evidence from a wide range of sources. We provide more detail in our response to questions on monitoring and evaluation in section 6.4.

126. We are encouraged that respondents endorsed a wide range of organisations in the UK as useful partners to deliver the proposed centralised activities. As we said in the white paper, the government will deliver the central function initially, working in partnership with regulators and other key actors in the AI ecosystem. The government’s primary role will be to leverage existing activities where possible and ensure that all the necessary activities to promote responsible AI innovation are taking place.

6.4. Monitoring and evaluation of the framework

15. Do you agree with our overall approach to monitoring and evaluation?

16. What is the best way to measure the impact of our framework?

17. Do you agree that our approach strikes the right balance between supporting AI innovation; addressing known, prioritised risks; and future-proofing the AI regulation framework?

Summary of questions 15-17:

127. A majority of respondents agreed with the overall approach to monitoring and evaluation, commending the proposed feedback loop with industry and civil society as a means to gain insights about the effectiveness of the framework.

128. Just over a quarter of respondents emphasised that engaging with a diverse range of stakeholders would create the most valuable insights. Many advocated for the inclusion of wider civil society and consumer representatives to ensure that voices outside of the tech industry are heard, as well as regular engagement with industry and research experts. Respondents also stressed that international engagement would be key to effectively harmonise approaches across jurisdictions.

129. Respondents wanted to see more detail on the practicalities of the monitoring and evaluation framework, including how data will be collected and used to measure success. Nearly a third of respondents suggested the impact of the framework should be measured through a range of data sources, and recommended collecting data on key indicators as well as using impact assessments.

130. Half of respondents agreed that the approach appears to strike the right balance between supporting AI innovation; addressing known, prioritised risks; and future-proofing the AI regulation framework. However, some respondents disagreed and argued that the approach prioritised AI innovation and economic growth over safety and the mitigation of AI -related risks.

131. We are pleased to note the positive feedback on our proposed approach to the monitoring and evaluation of the framework. Monitoring and evaluation activities will allow us to review the implementation of the AI regulation framework across the economy and is at the heart of our iterative approach. It will ensure that the regime is working as intended: actively responding to prioritised risks, supporting innovation, and maximising the benefits of AI across the UK. We agree with respondents that, as we implement the framework set out in the AI regulation white paper, monitoring and evaluation will allow the government to spot potential issues and adapt the framework in response if needed.

132. We acknowledge growing concerns that we may face more safety risks related to AI as these technologies are increasingly used. We recognise that many of these concerns focus on the advanced capabilities of the most powerful AI systems. That is why we remain committed to an adaptable approach that will evolve as new risks or regulatory gaps emerge. Our initial thinking on potential new measures targeted at the developers of highly capable general-purpose AI models is presented in section 5.2. The AI Safety Institute will advance AI safety capabilities for the public interest, allowing the government to respond to the cutting-edge of technological development. Our monitoring and evaluation will build on work by the Institute, our cross-sectoral risk assessment, and feedback from stakeholders to understand how the regulatory framework is performing. Our evaluation will consider whether the framework is effectively achieving the objectives set out in the white paper, including building public trust by addressing potential risks appropriately.

133. We note the emphasis from respondents on using the right data, metrics, and sources to evaluate how well the regulatory framework is performing. We agree that it is key to the effectiveness of the framework to get the measures of success right, and we are actively working on this as we develop our monitoring and evaluation framework for publication. We will conduct a targeted consultation on our proposed plan to assess the framework with a range of stakeholders in spring. As part of this, we will seek detailed views on our proposed metrics and data sources.

6.5. Regulator capabilities

18. Do you agree that regulators are best placed to apply the principles and government is best placed to provide oversight and deliver central functions?

19. As a regulator, what support would you need in order to apply the principles in a proportionate and pro-innovation way?

20. Do you agree that a pooled team of AI experts would be the most effective way to address capability gaps and help regulators apply the principles?

Summary of questions 18-20:

134. Nearly all respondents agreed that regulators are best placed to lead the implementation of the principles, and that the government is best placed to provide oversight and delivery of the central functions. However, respondents argued that the government would need to improve regulator capability in order for this approach to be effective. Some respondents were concerned at the lack of a specific body to support the implementation and oversight of the proposed framework, with some asking for AI legislation and a new AI regulator.

135. While regulators are broadly supportive of the proposed approach, over a quarter of those that responded to Q19 suggested that increased AI expertise would help them effectively apply the principles within their existing remits. Overall, regulators reported different levels of technical expertise and AI capability. Some felt that greater organisational capacity and additional resources would help them undertake new responsibilities related to AI and understand where and how AI is used in their domains.

136. Regulators also noted that AI presents coordination challenges across domains and sectors, with some emerging risks related to AI not falling clearly within a specific existing remit. Just over a quarter of regulators that responded to Q19 emphasised that close collaboration between regulators and the proposed central functions would help build meaningful sector-specific requirements and prevent duplication.

137. A majority of respondents agreed that a pooled team of AI experts would be the most effective way to address the different levels of capability across the regulatory landscape. Respondents advocated for a diverse and multi-disciplinary pool to bring together technical AI expertise with sector-specific regulatory knowledge, industry specialists, and civil society. Respondents argued that this would ensure that regulators are considering a broad range of perspectives in their application of the cross-sectoral AI principles.

138. We are encouraged that respondents broadly agree with the proposed regulator-led approach for the implementation of the principles, with the government providing oversight and delivering the central function. As outlined in the AI regulation white paper, our existing expert regulators are best placed to conduct detailed risk analysis and enforcement activities within their areas of expertise. We will continue to work closely with regulators to ensure that potential risks posed by AI are sufficiently covered by our rule of law. In keeping with our iterative approach, we will seek to adapt the framework, including the regulatory architecture, if analysis proves this is necessary and effective.

139. As pointed out by respondents across the consultation, to regulate AI effectively our regulators must have the right skills, tools, and expertise. To support regulator’s ability to adapt and respond to the risks and opportunities that AI presents in their domains, we are today announcing a £10 million investment to build technical upskilling. We will work closely with regulators to identify the most promising opportunities to leverage this funding, including designing a delivery model that can achieve the intended objectives more effectively than the central pool of expertise proposed in the AI regulation white paper. In particular, regulator feedback has shown that we need to support them to develop tools and skills within their specific domains – albeit working collaboratively where appropriate – and deliver support that aligns with and supports their independence. As capability and resource varies across regulators, our intention is that this fund will particularly enable those regulators with less mature AI expertise to conduct research and uncover foundational insights to develop or adapt practical tools to ensure compliance in an AI -enabled future.

140. Further, as set out in the response to Professor Dame Angela McLean’s cross-cutting review of pro-innovation regulation of technologies [footnote 110] , the government is also exploring how to further support regulators to develop the specialist skills necessary to regulate emerging technologies, including increased flexibility on pay and conditions. This builds on schemes already in place to support secondments between government departments, regulators, academia, and industry.

141. We acknowledge regulator’s concerns that AI can pose coordination challenges. In the white paper we proposed a number of centralised activities to support regulators and ensure that the regulatory landscape for AI is consistent and cohesive. To facilitate cross-cutting collaboration and ensure that the overall regulatory framework functions as intended, we are developing our regulatory coordination activities. These coordination activities will sit in our central function in government alongside our AI risk assessment activities (see more detail in section 5.1.2). To support a coherent approach across sectors, we are also publishing initial guidance to regulators alongside this response on how to apply the cross-sectoral AI principles within their existing remits.

142. We note respondents’ emphasis on transparency and the need for industry and civil society to have visibility of the AI regulation framework. We agree that establishing feedback loops with industry, academia and civil society will be key to measuring the effectiveness of the framework. Our central function will engage stakeholders to ensure that a wide range of voices are heard and considered: providing clarity, building trust, ensuring interoperability, and informing the government of the need to adapt the framework.

6.6. Tools for trustworthy AI

21. Which non-regulatory tools for trustworthy AI would most help organisations to embed the AI regulation principles into existing business processes?

Summary of question 21:

143. There was strong support for the use of technical standards and assurance techniques, with some respondents agreeing that both would help organisations to embed the AI principles into existing business processes. Many respondents praised the UK AI Standards Hub and the Centre for Data Ethics and Innovation’s ( CDEI ) work on AI assurance. While some respondents noted that businesses would have a smaller compliance burden if tools and processes were consistent across sectors, others noted the importance of additional sector-specific tools and processes. Respondents also suggested supplementing technical standards with case studies and examples of good practice.

144. Respondents argued that standardised tools and techniques for identifying and mitigating potential risks related to AI would also support organisations to embed the AI principles. Some identified assurance techniques such as impact and risk assessments, model performance monitoring, model uncertainty evaluations, and red teaming as particularly helpful for identifying AI risks. A few respondents recommended assurance techniques that can be used to detect and prevent issues such as drift to mitigate risks related to data. While commending the role of tools for trustworthy AI , a small number of respondents also expressed a desire for more stringent regulatory measures, such as statutory requirements for high risk applications of AI or a watchdog for foundation models.

145. Respondents felt that tools and techniques such as fairness metrics, transparency reports, and organisational AI ethics guidelines can support the responsible use of AI while growing public trust in the technology. Respondents expressed the desire for third-party verification of AI models through bias audits, consumer labelling schemes, and external certification against technical standards.

146. A few respondents noted the benefits of international harmonisation across AI governance approaches for both organisations and consumers. Some endorsed interoperable technical standards for AI , commending global standards development organisations ( SDOs ) such as the International Organization for Standardization ( ISO ) and Institute of Electrical and Electronics Engineers ( IEEE ). Others noted the strength of a range of international work on AI including that by individual countries, such as the USA’s National Institute of Standards and Technology ( NIST ) AI Risk Management Framework ( RMF ) and Singapore’s AI Verify Foundation, along with work on international governance by multilateral bodies such as the Organisation for Economic Co-operation and Development ( OECD ), United Nations ( UN ), and G7 .

147. We are pleased to see such strong support for the continued development and adoption of technical standards and assurance techniques for AI . These tools will help organisations put our proposed regulatory principles into practice, innovate responsibly, and build public confidence. We recognise that, in some instances, it will be important to have assurance techniques and technical standards that are specific to a particular context, application, or sector. That is why, in the AI regulation white paper, we set out a layered approach to technical standards, encouraging regulators to build on widely applicable sector-agnostic tools where appropriate [footnote 111] .

148. We welcome praise for the UK AI Standards Hub and CDEI . Launched in October 2022, the Hub brings together the UK’s technical expertise on AI standards, including the Alan Turing Institute, British Standards Institution, and National Physical Laboratory, to provide training and information on the complex international AI standards landscape. The CDEI published a Portfolio of AI Assurance Techniques in June 2023 with examples from the real world to support the development of trustworthy AI , which respondents indicated would be helpful [footnote 112] . The Portfolio is also part of the OECD ’s Catalogue of Tools and Metrics for Trustworthy AI , which shares the CDEI case-studies to an international audience. The CDEI also launched the “Fairness Innovation Challenge” in October to support the development of new socio-technical solutions to address bias and discrimination in AI systems [footnote 113] . Today we are announcing that the Centre for Data Ethics and Innovation ( CDEI ) is changing its name to the Responsible Technology Adoption Unit to more accurately reflect its role within the Department for Science, Innovation and Technology ( DSIT ) to develop tools and techniques that enable responsible adoption of AI in the private and public sectors. This year, DSIT will publish an “Introduction to AI assurance” to further promote the value of AI assurance.

149. We note that respondents would like to see more standardised tools and techniques to identify and manage AI risk. Ahead of the AI Safety Summit in November 2023, we published “Emerging processes for frontier AI safety” to help prompt a debate about good safety processes for advanced AI systems look like [footnote 114] . The document provides a snapshot of promising ideas, emerging processes, and associated practices in AI safety. It is intended as a point of reference to inform the development of frontier AI organisations’ safety policies as well as a companion for readers of these policies. It outlines early thinking on practices for innovation in frontier AI development, including model evaluations and red teaming, responsible capability scaling, and model reporting and information sharing. In 2024, we will encourage AI companies to develop their AI safety and responsible capability scaling policies. As part of this work, we will update our emerging processes guide by the end of the year. More widely, we note the development of relevant global technical standards which provide guidance on risk management related to AI . For example, standard ISO 42001 will help organisations manage their AI systems in a trustworthy way.

150. In the white paper, we note that responding to risk and building public trust are key drivers for regulation. We therefore understand respondents’ emphasis on tools for building public trust as a key way to ensure responsible AI innovation. The Responsible Technology Adoption Unit (formerly CDEI ) within DSIT has a specialist Public Insights team that regularly engages with the general public and affected communities to build a deep understanding of public attitudes towards AI [footnote 115] . These insights are used by DSIT and wider government to align our regulatory approaches to AI with public values and foster trust in these technologies. DSIT and the Central Digital and Data Office ( CDDO ) have also developed the ATRS to help public sector organisations provide clear information about algorithmic tools they use to support decisions [footnote 116] . Following a successful pilot of the standard, and publication of an approved cross-government version last year, we will now be making use of the ATRS a requirement for all government departments and plan to expand this across the broader public sector over time.

151. We agree with respondents that international cooperation on AI governance will be key to successfully mitigating AI -related risks and building public trust in AI . The first ever AI Safety Summit convened a group of representatives from around the globe to set a new path for collective international action to navigate the opportunities and risks of frontier AI . We also continue to collaborate internationally on AI governance, both bilaterally and through several multilateral fora. For example, the UK plays an important role in AI discussions at the UN , Council of Europe, OECD , G7 , Global Partnership on AI ( GPAI ), and G20 . Notably, the UK worked closely with G7 partners in negotiating the Codes of Conduct and Guiding Principles for the development of advanced AI systems, as part of the Hiroshima AI Process. The UK fully supports developing AI policy and technical standards in a globally inclusive, multi-stakeholder, open, and consensus-based way. We support UK stakeholders to participate in Standards Development Organisations ( SDOs ) to both leverage the benefits of global technical standards here in the UK and deliver global digital technical standards shaped by democratic values.

6.7. Final thoughts

22. Do you have any other thoughts on our overall approach? Please include any missed opportunities, flaws, and gaps in our framework.

Summary of question 22:

152. Some respondents felt that the AI regulation framework set out in the white paper would benefit from more detailed guidance on AI -related risks. Some wanted to see more stringent measures for severe risks, particularly related to the use of AI in safety-critical contexts. Respondents suggested that the framework would be clearer if the government provided risk categories for certain uses of AI such as law enforcement and places of work. Other respondents stressed that AI can pose or accelerate significant risks related to privacy and data protection breaches, cyberattacks, electoral interference, misinformation, human rights infringements, environmental sustainability, and competition issues. A few respondents were concerned about the potential existential risk posed by AI . Many respondents felt that AI technologies are developing faster than regulatory processes.

153. Some respondents argued that the success of the framework relies on sufficient coordination between regulators in order to provide a clear and consistent approach to AI across sectors and markets. Respondents also noted that different sectors face particular AI -related benefits and risks, suggesting that the framework would need to balance the consistency provided by cross-sector requirements with the accuracy of sector-specific approaches. In particular, respondents flagged that any new rules or bodies to regulate AI should build from the existing statutory remits of regulators and relevant regulatory standards. Respondents also noted that regulators would need to be adequately resourced with technical expertise and skills to implement the framework effectively.

154. Respondents consistently emphasised the importance of international harmonisation to effective AI regulation. Some respondents suggested that the UK should work towards an internationally aligned regulatory ecosystem for AI by developing a gold standard framework and promoting best practice through key multilateral channels such as the OECD , UN , GPAI , G7 , G20 , and the Council of Europe. Respondents noted that divergent or overlapping approaches to regulating AI would cause significant compliance burdens. Respondents argued that international cooperation can support responsible AI innovation in the UK by creating clear and certain rules that allow investments to move across multiple markets. Respondents also suggested establishing bilateral working groups with key strategic partners to share expertise. Some respondents stressed that the UK’s pro-innovation approach should be delivered at pace to remain competitive with a fast-moving international landscape.

155. We acknowledge that many respondents would like more detail on the implementation of the framework set out in the AI regulation white paper, particularly regarding AI -related risks. We have already started to deliver the proposals set out in the white paper, working quickly to establish centralised, cross-economy risk assessment activities within the government to identify, measure, and mitigate risks. Building from this work, we published research on frontier AI capabilities and risks for discussion at the AI Safety Summit [footnote 117] . It outlined initial evidence on the most advanced AI systems and how their capabilities and risks may continue to develop. The significant uncertainty in the evidence highlights the need for further research.

156. This year, we will consult on a cross-economy risk register for AI , seeking expert views on our risk assessment methodology and whether we have comprehensively captured AI -related risks. The AI Safety Institute will advance the world’s knowledge of AI safety by carefully examining, evaluating, and testing advanced AI systems. It will conduct fundamental research on how to keep people safe in the face of fast and unpredictable technological progress.

157. In the white paper, we proposed an adaptable, principles-based approach to regulating AI in order to keep pace with rapid technological change. We will use our risk assessment and monitoring and evaluation activities to continue to assess measures for the targeted, proportionate, and effective prevention and mitigation of any new and accelerated risks related to AI , including those potentially posed by the development of the most powerful systems.

158. We agree that an effective framework for regulating AI will need to carefully balance cross-sector consistency with sector specific needs in order to support responsible innovation. Our context-focused framework builds from the domain expertise of the UK’s regulators, ensuring that different industries benefit from existing regulatory knowledge. While this approach streamlines compliance within specific sectors, we recognise the need for consistency and coordination between regulators to create an easily navigable regulatory landscape for businesses and consumers. That is why, as we note in detail in our responses to questions on regulator capability and AI sandboxes and testbeds (sections 6.5 and 6.10), we have been focusing on building from the existing strengths of UK regulators by establishing a pilot advisory service for AI innovators through the DRCF , sharing guidance on implementation, and building common regulator capability.

159. Alongside our work to quickly deliver on the centralised risk assessment and regulatory capability and coordination activities, the UK has led the way in convening world leaders at the first ever AI Safety Summit in order to establish an aligned approach to the most pressing risks related to the cutting-edge of AI technology. Countries agreed to the Bletchley Declaration at the AI Safety Summit, recognising the need for international collaboration in understanding the risks and opportunities of frontier AI [footnote 118] . We will deliver a groundbreaking International Report on the Science of AI Safety to promote an evidence-based understanding of advanced AI [footnote 119] . Additionally, the UK, through the AI Safety Institute, will collaborate with other nations, including the US, to enhance our capability to research and evaluate AI risks, underscoring our ability to drive change through international coordination on this critical topic. 

160. Our work at the AI Safety Summit is complemented by multilateral engagement in other AI -focused forums, such as the G7 Hiroshima process, G20 , UN , GPAI , and Council of Europe. In multilateral engagements, we are working to leverage each forum’s strengths, expertise, and membership to prevent overlap or divergences with other regulatory systems, ensuring they are adding maximum value to global AI governance discussions and the UK’s values and economic priorities. The UK is also pursuing bilateral cooperation with many partners, reflecting our commitment to interoperability and establishing international norms for responsible AI innovation.

6.8. Legal responsibility for AI

L1. What challenges might arise when regulators apply the principles across different AI applications and systems? How could we address these challenges through our proposed AI regulatory framework?

L2.i. Do you agree that the implementation of our principles through existing legal frameworks will fairly and effectively allocate legal responsibility for AI across the life cycle?

L.2.ii. How could it be improved, if at all?

L3. If you work for a business that develops, uses, or sells AI , how do you currently manage AI risk including through the wider supply chain? How could government support effective AI -related risk management?

Summary of questions L1-L3:

161. While respondents praised the benefits of a principles-based approach, nearly half were concerned about potential coordination issues between regulators and consistency across sectors. Some were concerned about confusing interdependencies between the AI regulation framework and existing legislation. Respondents asked for sector-based guidance from regulators, compliance tools, and regulator engagement with industry. Some respondents also pointed to the importance of international alignment and collaboration.

162. A majority of respondents disagreed that the implementation of the principles through existing legal frameworks would fairly and effectively allocate legal responsibility for AI across the life cycle. Just under a third of respondents felt that the government should clarify AI -related liability. However, there was not clear agreement about where liability should sit, with respondents noting a range of potential responsibilities for different actors across the AI life cycle. There was repeated acknowledgement of the complexity of AI value chains and the potential variations in use-cases. Some voiced concerns about gaps in existing legislation, including intellectual property, legal services, and employment law.

163. Around a quarter of respondents to L2.ii stated that new legislation and regulatory powers would be necessary to effectively allocate liability across the life cycle. Respondents stressed the importance of a legally responsible person for AI within organisations, with a few suggestions of an AI equivalent to Data Protection Officers. Some respondents wanted more detail on how the principles will be implemented through existing law, with a few recommending that regulatory guidance would clarify the landscape. A small number of respondents noted that the proposed central functions, including risk assessment, horizon scanning, and monitoring and evaluation, would help assess and adapt the framework to ensure that legal responsibility for new AI -related risks is adequately distributed. A couple of respondents also suggested pre-deployment measures such as licensing and pre-market approvals.

164. Nearly half of organisations that responded to L3 told us that they used risk assessment processes for AI , with many building from sectoral best practice or trade body guidance. Respondents pointed to existing legal frameworks that capture AI -related risks, such as product safety and data protection laws, and stressed that any future AI measures should avoid duplicating or contradicting existing rules. Respondents suggested that it would be useful for businesses to understand the government’s view on AI -related best practices, with some recommending a central guide on using AI safely. Some smaller businesses asked for targeted support to implement the AI principles.

165. Respondents consistently stressed the importance of transparency as a tool for education, awareness, consent, and contestability. Echoing answers to questions Q2 and F1, many respondents mentioned that organisations should be transparent about AI use, outputs, and training data.

166. We are pleased to note respondents’ broad support for a principles-based approach to AI regulation that can provide proportionate oversight across the many potential applications and uses of AI technologies. We agree with respondents that, as we implement the framework set out in the white paper, it is important to coordinate between regulators, sectors, existing legal frameworks, and the fast-moving international regulatory landscape. That is why we have been working at pace to establish the activities of the central function outlined in the white paper (for a detailed overview see section 5.1.2).

167. We note that there are still questions regarding how to fairly and effectively allocate legal responsibility for AI across the life cycle. We also recognise that many responses endorsed further government intervention to ensure the fair and effective allocation of liability across the AI value chain. Responses stressed the complexity and variability of AI supply chains, with use-cases highlighting expansive ethical and technical questions. We agree that there is no easy answer to the allocation of legal responsibility for AI and we also agree that it is important to get liability and accountability for AI right in order to support innovation and public trust. Building on the commitment to examine foundation models in the white paper, we have focused our initial life cycle accountability work on highly capable general-purpose systems (for details see section 5.2).

168. We are also continuing to analyse how existing legal frameworks allocate accountability and legal responsibility for AI across the life cycle. Our initial analysis suggests that a context-based approach to regulating AI may not adequately address risks arising from highly capable general-purpose systems since a context-based approach does not effectively and fairly allocate accountability to developers of those systems. We are exploring a range of potential obligations targeted at the developers of these systems including those suggested by respondents such as pre-market permits, model licensing, accountability and governance frameworks, transparency measures, and changes to existing legal frameworks. As we continue to iterate the AI regulation framework, we will consider introducing measures to effectively allocate accountability and fairly distribute legal responsibility to those in the life cycle best able to mitigate AI -related risks.

169. We are encouraged by the wide range of risk assessment and management processes that respondents told us they are already using. Our “Emerging processes for frontier AI safety” paper outlines a set of practices to inform the development of organisational AI safety policies [footnote 120] . It provides a snapshot of promising ideas and associated practices in AI safety today. As discussed in response to questions on the cross-sectoral principles (section 6.1), we acknowledge the broad support for measures on transparency and we will continue our work assessing whether and which measures provide the most meaningful transparency for AI end users and actors across the AI life cycle.

6.9. Foundation models and the regulatory framework

F1. What specific challenges will foundation models such as large language models ( LLMs ) or open-source models pose for regulators trying to determine legal responsibility for AI outcomes?

F2. Do you agree that measuring compute provides a potential tool that could be considered as part of the governance of foundation models?

F3. Are there other approaches to governing foundation models that would be more effective?

Summary of questions F1-F3:

170. While respondents supported the AI regulation framework set out in the white paper, many were concerned that foundation models may warrant a bespoke regulatory approach. Some respondents noted that foundation models are characterised by their technical complexity and stressed their potential to underpin many different applications across multiple sectors. Nearly a quarter of respondents emphasised that foundation models make it difficult to determine legal responsibility for AI outcomes and shared hypothetical use-cases where both upstream and downstream actors are at fault. Respondents stressed that technical opacity, complex supply chains, and information asymmetries prevent sufficient explainability, accountability, and risk assessment for foundation models.

171. Around a fifth of respondents expressed concerns about how foundation models use data, including whether data is of adequate quality, appropriate for downstream applications, compliant with existing law, and sourced ethically. Some stated that it is not clear who is responsible for deciding whether or not data is appropriate to a given application. Respondents stressed that training data currently lacks a clear definition, technical standards, and benchmark measurements.

172. Some respondents noted concerns regarding wider access to AI , including open source, leaking, or malicious use of models. However, a similar number of respondents noted the importance of open source to AI innovation, transparency, and trust.

173. Half of respondents felt compute was an inadequate proxy for governance requirements, with some recommending assessing models by their capabilities and applications instead. Respondents felt that model verification measures, such as audits and evaluations, would be effective, with some suggesting these should be mandatory requirements. A few noted the importance of downstream monitoring or post-market surveillance.

174. About a third of respondents supported governance measures including tools for trustworthy AI such as technical standards and assurance. One respondent suggested a pre-deployment sandbox. A few supported moratoriums, bans, or limits. A small number of respondents suggested that contracts, licences, user agreements, and (cyber) security measures could be used to govern foundation models.

175. We acknowledge the range of challenges that respondents have raised in regard to foundation models and note the particular attention given to the core characteristics or features of foundation models such as technical opacity and complexity. We also recognise that challenges arise from the fact that foundation models can be broad in their potential applications and, as such, can cut across sectors and impact upon a range of risks. Our analysis shows that many regulators can struggle to enforce existing rules and laws on the developers of highly capable general-purpose AI systems within their current statutory remits in a way that effectively mitigates risk.

176. In response to repeated calls for specific regulatory interventions targeted at foundation models, we have been exploring the impact of foundation models on life cycle accountability for AI . In the AI regulation white paper, we stated that legal responsibility for AI should sit with the actor best able to mitigate any potential risks it poses. Our assessment suggests that, despite their ability to mitigate risks when designing and developing AI , the organisations building highly capable general-purpose systems are currently unlikely to be impacted by existing rules and laws in a way that sufficiently mitigates risk. That is why we are exploring options for targeted, proportionate interventions focusing on these systems and the risks that they present. We have been assessing measures to mitigate risk during the design, training, and development of highly capable general-purpose systems. We have also been exploring options for ensuring effective accountability, including legally mandated obligations, while avoiding cumbersome red-tape.

177. We note respondent views that compute is an imperfect proxy for foundation model capability. As part of our work exploring the right guardrails for highly capable general-purpose systems, we are examining how best to scope any regulatory requirements based on model capabilities, and the risks associated with these, wherever possible. But we recognise that, in some cases, controls might need to be in place before a model’s capability is known. In these cases, limited and careful use of proxies may be necessary to target regulatory requirements to only those systems that pose the most significant potential risks. Our early analysis indicates that initial thresholds could be based on forecasts of capabilities using a combination of two proxies: compute and capability benchmarking. However there might need to be a range of thresholds. For more detail, see section 5.2.

178. To provide greater clarity on best practices for responsible AI innovation – including using data – we published a set of emerging safety processes for frontier AI companies for the AI Safety Summit in 2023 [footnote 121] . The document consolidates emerging thinking in AI safety and has been written for AI organisations and those who want to better understand their safety policies. We will update this guide by the end of the year and continue to encourage AI companies to develop best practices (see section 5.2.2 for detail).

179. We acknowledge respondents’ views on both the value and risks of open source AI . Open access can provide wide benefits, including helping to mitigate some of the risks caused by highly capable general-purpose systems. However, open release can also exacerbate the risk of misuse. We believe that all powerful and potentially dangerous systems should be thoroughly risk-assessed before being released. We will continue to monitor and assess the impacts of open model access on risk. We will also carefully consider the impact of any potential measures to regulate open source systems on competition, innovation, and wider risk mitigation.

180. As set out in section 5.2, we will continue our technical policy analysis to refine our thinking on highly capable general-purpose systems in the context of AI regulation and life cycle accountability. We will continue to engage with external experts on a range of challenging topics such as how effective voluntary measures could be at mitigating risks and the right scope of any additional regulatory interventions including proxies and capability thresholds. We will also continue to examine questions related to accountability and liability, including the extent to which existing laws and regulators can “reach” through the value chain to target the developers of highly capable general-purpose systems and the potential impact of open release. We will also engage with regulators to learn from their existing work on this topic. For example, we will continue to engage with the CMA on their work on foundation models.

6.10. AI sandboxes and testbeds

S1. To what extent would the sandbox models described in section 3.3.4 support innovation?

S2. What could government do to maximise the benefit of sandboxes to AI innovators?

S3. What could government do to facilitate participation in an AI regulatory sandbox?

S4. Which industry sectors or classes of product would most benefit from an AI sandbox?

Summary of questions S1-S4:

181. Overall, respondents were strongly supportive of a regulatory sandbox for AI . The highest proportion of respondents agreed that the “multiple sector, multiple regulator” and “single sector, multiple regulator” sandbox models would be most likely to support innovation, stating that the cross-sectoral or cross-regulator basis would help develop effective guidance in response to live issues, harmonise rules, and coordinate implementation of the AI regulation framework. While there was no majority consensus on a specific sector that would most benefit from a sandbox, the largest proportion of question respondents stated that healthcare and medical devices would most benefit from an AI sandbox, followed by financial services and transport.

182. Some respondents suggested collaborating with the wider AI ecosystem to maximise the benefit of sandboxes to AI innovators. Many recommended building on the existing strengths of the UK regulatory landscape, such as the DRCF . Linked to this, a few respondents noted that an AI regulatory sandbox presents an opportunity for the UK to demonstrate global leadership in AI regulation and technical standards by sharing findings and best practice internationally.

183. Some respondents recommended making information accessible to maximise the benefit of the sandbox to participants and the wider AI ecosystem. Respondents wanted participation pathways, training, tools, and other resources to be technically and financially accessible. Many respondents noted that accessible guidance and tools would allow organisations to engage with the sandbox. In particular, respondents emphasised the benefits of accessible information for smaller businesses and start-ups who are new to the regulatory process. Respondents advocated for regular reporting on sandbox processes, evidence, findings, and outcomes to encourage “business-as-usual” best practices for AI across the wider ecosystem.

184. Respondents noted the importance of reducing the administrative burden on smaller businesses and start-ups to lower the barrier to entry for those with less organisational resources. Some noted that financial support would help ensure that smaller businesses and start-ups could participate in resource-intensive research and development focused AI sandboxes. Respondents felt that sharing evidence, guidance, and tools would ensure the wider AI ecosystem benefitted from the sandbox. Some suggested access to datasets or product accreditation schemes would incentivise participation in supervised test environment sandboxes.

185. The response to the consultation – which aligns with independent research commissioned through the Regulators’ Pioneer Fund – has helped to inform the government’s decision to fund a pilot multi-regulator advisory service offered by the DRCF : the AI and Digital Hub. In particular, it has helped to clarify that a new regulatory service is likely to add most value supporting AI innovators from a range of sectors to navigate the multiple regulatory regimes that govern the use of cross-cutting AI products and services, rather than through targeting one specific regulatory remit or regulated sector.

186. The DRCF AI and Digital Hub brings together four of the most critical regulators of AI and digital technologies, including the CMA , ICO , Ofcom , and the Financial Conduct Authority ( FCA ). Together these regulators are responsible for overseeing some of the most significant regulatory regimes that govern AI products, whether cross-economy (data protection, competition and consumer regulation) or sectoral (financial services, telecommunications and broadcasting).

187. Respondents to the consultation also emphasised the importance of making information and resources relating to the sandbox accessible in order to maximise its benefits. Respondents noted the need to reduce the compliance burden for smaller businesses and start-ups in particular. Again, these considerations are central to the design and operation of the DRCF AI and Digital Hub. In addition to providing tailored support to participating innovators that will be accessed via a simple online application process, the Hub will also publish anonymised case-studies and guidance to support a broader pool of innovators facing similar compliance challenges. Our research has indicated that a repository of use cases such as this will be a particularly effective means of amplifying the outreach and impact of such a pilot.

188. We note that some respondents suggested that additional incentives such as product accreditation or access to data would encourage participation in a sandbox for AI . These additional incentives would best suit a supervised test environment sandbox model. As the DRCF ’s AI and Digital Hub pilot phase will focus on providing compliance support, these additional incentives will not be included. However, we are committed to reviewing how the service needs to develop – and what further measures are necessary to support AI and digital innovators – in the light of the pilot findings and further feedback from stakeholders.

Annex A: method and engagement

Consultation method and engagement summary.

1. With the publication of the AI regulation white paper on 29 March 2023, we held a formal 12-week public consultation that closed on 21 June 2023. In total, we heard from over 545 different individuals and organisations.

2. Stakeholders were invited to submit evidence in response to 33 questions on the government’s policy proposals for a regulatory framework for AI . Stakeholders were invited to submit evidence through an online survey, email, or post. In total, we received 409 responses in writing. Removing 50 duplicates and blanks left 359 written submissions. See Written submissions below for more detail.

3. We also proactively engaged with 364 individuals through roundtables, technical workshops, bilaterals, and a programme of on-going regulator engagement. Our roundtables sought the views of stakeholders that we might hear from less often with topics including the impact of AI on marginalised communities, public trust, and citizen perspectives. We also held roundtables focused on smaller businesses and the open source community. More detail can be found in the Engagement method and Engagement findings sections below.

Method for analysing written submissions

4. We received written consultation responses from organisations and individuals through an online survey and email. Of the total 409 responses, we received 232 through our online survey and 177 by email.

5. Of the 33 questions, 12 were closed questions with predefined response options on the online survey. We manually coded submissions by email that explicitly responded to these closed questions to follow the Likert-scale structure. The remaining 21 questions invited free text qualitative responses and each response was individually analysed and manually coded. As such, quantitative analysis represents all stakeholders who answered a specific question through email or the online survey. Not all respondents answered every question and we present our findings as an approximate proportion of responses to the question.

6. In accordance with our privacy notice [footnote 122] and online survey privacy agreement, only those individuals and organisations who submitted evidence through our online survey and consented to our privacy agreement will have their names published in the list of respondents (see Annex B).

7. Respondents to the online survey self-selected an organisation type and sector. We manually assigned organisation types and sectors to respondents who submitted written evidence through email. After removing blanks and duplications, we received responses from across 8 organisation types and 18 sectors. Chart M1 shows response numbers by organisation type. The majority of responses came from industry, business, trade unions, and trade associations. This is followed by individuals not representing an organisation and then research groups, universities, and think tanks.

8. Chart M1: AI regulation white paper consultation respondents by organisation type

9. Chart M2: AI regulation white paper consultation respondents by sector

M2 Note: Primary sectors include extraction of raw materials, farming, and fishing. Secondary sectors include utilities, construction, and manufacturing.

10. The sector breakdown in Chart M2 shows that the biggest number of responses came from the AI , digital, and technology industry. This was followed by respondents who selected “other” and then those in the arts and entertainment sector. Further analysis of “other” responses suggests that these responses were often from individuals not representing an organisation and included students.

11. As these demographics indicate, this sample, as with all written consultation samples, may not be representative of public opinion as some groups are over or under represented.

12. In particular, we note that responses received from a number of creative industries stakeholders were either identical or very similar. These responses largely focused on AI and copyright. These responses were analysed and included in the same way as all other responses.

13. 89 emailed pieces of evidence followed the question structure of our online survey. These were analysed alongside responses from the survey to inform quantitative analysis. After removing duplicate responses, we included 66 emailed responses in our analysis.

14. 88 emailed responses provided evidence beyond the scope of our consultation questions or without explicit reference to the questions. We analysed these submissions individually. While our findings from this analysis informs our overall response, we do not include these responses within our quantitative analysis as they do not explicitly answer our consultation questions. Where relevant, we have used insights from these responses to inform our qualitative question summaries. After removing duplicate responses, we included 84 of these in our qualitative analysis.

15. We received 33 duplicate responses that were sent twice through either the online survey or email. We received requests for 4 of these duplications to be deleted on grounds they were incorrect and superseded by a later response. These duplicates were removed from analysis entirely. The remaining 29 duplicates were responses sent by both online survey and email. Where appropriate, we removed either the email or survey response from our quantitative analysis to avoid skewing counts with duplicate submissions. However, in consideration of additional detail given, we analysed both responses to weave any additional insights into our overall qualitative analysis. A further 17 written responses were discounted from analysis entirely on the grounds that they were blank or contained spam. After reviewing and cross-checking responses, we discounted 50 written submissions from the final analysis to avoid overcounting blanks, spam, and duplicate responses. That left 359 submissions of which 209 were received through the online survey and 150 by email.

16. We use illustrative qualitative language such as “many”, “some”, and “a few” to summarise the written responses we received to our consultation. These descriptions are intended to provide an indication of the extent that a particular theme or sentiment was raised by respondents. Not all respondents answered every question. We refer to approximate amounts of respondents to each question, including “a half”, “a quarter”, or “a third”. We use the terms “nearly all” or “most” when a substantial majority of respondents made a particular argument or shared a sentiment. We use the terms “a majority” or “over half” to show when a point was shared by over 50% of respondents. We use “many” when lots of respondents raised a similar point but the theme or sentiment was not shared by over half of respondents. We use “some” to indicate when a theme or sentiment was shared by between a tenth and a fifth of respondents. We use “a few” when a smaller number of respondents made a similar point. We use a “small number” to describe when less than 10 respondents raised a point, specifying if this is “one” or “two” (“a couple”).

Engagement method

17. We held 19 roundtables engaging 278 individuals representing a range of perspectives and organisation types including AI industry, digital, and technology organisations, small businesses and start-ups, companies that use AI , the open source community, trade bodies and unions, legal services, financial services, creative industries, academics, think tanks, research organisations, regulators, government departments, the public sector, charities and advocacy groups, citizens, marginalised communities, and wider civil society.

18. Some roundtables focused on hearing from regulators or stakeholders within a specific sector, including education, transport, financial services, legal services, and health and social care. Others focused on technical elements of the regulatory framework such as methods for AI verification, liability, and tools for trustworthy AI , including technical standards. Some discussions were designed to understand the views of stakeholders we might hear from less often: one explored the impact of AI on marginalised communities, another examined the role of public trust, two further roundtables focused on the perspectives of small businesses and the open source community, and the Minister for AI and Intellectual Property, Viscount Camrose, chaired a citizens roundtable during London Tech Week. Other topics included AI safety, international interoperability, approaches to responsible AI innovation in industry, and the UKRI ’s AI Technology Mission.

19. We are grateful to the partners who worked with us to organise roundtables and workshops including CDEI , the Department for Education ( DfE ), the Department of Health and Social Care ( DHSC ), the Department for Transport (DfT), the Ministry of Justice (MOJ), UK Research and Innovation ( UKRI ), the British Computer Society ( BCS ), Hogan Lovells, Innovate Finance, the Ada Lovelace Institute, the Alan Turing Institute, Open UK, the British Standards Institution ( BSI ), and the University of Bath ART-AI .

20. Alongside this programme of roundtable discussions and technical workshops, we engaged with 42 stakeholders through external engagements where we presented the AI regulation framework outlined in the white paper. We also held 28 bilaterals and held meetings with 16 regulators as part of our on-going work to support implementation. We include insights from this engagement throughout the consultation response.

Engagement findings

21. In this section, we provide a brief overview of our roundtables and workshops, summarising insights into four areas based on roundtable focus and participation from:

  • regulators.
  • civil society.
  • research organisations.

22. We held six roundtables with regulators to understand existing capabilities and needs, including how the approach set out in the AI regulation white paper would be implemented into specific sectors including health and social care, justice, education, and transport.

23. Regulators reported varying levels of in-house AI knowledge and capability, with most supporting central measures to enhance technical expertise. Some agreed that a pool of expertise could enhance regulatory capacity, while others suggested that the proposed central function could provide guidance and training materials for regulators.

24. Regulators were broadly supportive of the central function outlined in the white paper, emphasising that they could serve as a useful point of contact for regulators. However, regulators also stressed that the central function should not infringe on the independence or existing statutory remits of regulators, suggesting that any guidance to regulators on the implementation of the principles should not impede, duplicate, or contradict regulators’ current mandates and work.

25. Participants at the roundtables emphasised that regulators need adequate resources, endorsing government investment in technical capability and capacity. Some noted that the government may also need to introduce new regulatory powers in order for the framework to be effective, stating that achieving meaningful transparency and contestability may require the government to mandate disclosure from developers and deployers of AI at set points.

26. Participants raised several challenges to effective regulator oversight specific to AI including unknown and changing functional boundaries, technical obscurity, unpredictable environments, lack of human oversight or input, and highly iterative technological life cycles. Regulators suggested that collaboration between regulators, safety engineers, and AI experts is key to creating robust verification measures that prevent, reduce, and mitigate risks.

27. While regulators stated that the principles provide useful common ground across sectors, they noted that sector-specific analysis would be necessary to identify gaps in the framework. Some noted that sector specific use-cases would help regulators apply the principles in their respective domains.

28. We heard from a range of industry stakeholders at seven roundtable events with topics ranging from international interoperability, responsible AI in industry, general-purpose AI , and governance and technical standards needs.

29. Some participants were concerned that market imbalances were preventing innovation and competition across the AI ecosystem. In particular, participants argued that more accessible, traceable, and accountable data would promote innovation, noting that smaller companies often have to rely on existing market leaders or lower quality datasets due to the lack of affordable commercial, proprietary datasets. Participants suggested that clear standards for data and more equitable access to higher quality datasets would stimulate AI innovation across the wider ecosystem and prevent incumbent advantages.

30. Participants also noted that some of the potential measures to regulate AI could allow current market leaders to further entrench their advantages and increase existing market imbalances. Participants noted that smaller businesses and the open source community could face a significant compliance burden, with some suggesting that regulatory sandboxes should be used to test the impact of regulation. While some suggested that legal responsibility for AI should be allocated to earlier stages in the life cycle, others warned that placing the legal responsibility for downstream applications on open source developers would severely limit innovation as they would not be able to account for the many potential uses of open source code.

31. There was no consensus on whether licensing requirements for foundation models would effectively encourage responsible AI innovation or, instead, concentrate market power among a few established companies. A few participants noted that practical guidance on implementation and use-cases would support organisations to apply the principles. Some participants noted a licensing framework that only allowed open access to some parts of an AI system’s code could retain some of the benefits of the information sharing and transparency that defines open source.

32. Some participants stated that it is not clear whose job it is to regulate AI , advocating for a new, AI -specific regulator or a clear lead regulator. Participants emphasised the importance of technical expertise to effective regulation.

33. Participants also noted the important role of international interoperability, insurance, technical standards, and transparency in market success for AI .

Civil society and public trust

34. Three roundtables were held with smaller businesses, civil society stakeholders, and special interest groups to discuss public trust and the impact of AI on citizens and marginalised communities.

35. Participants emphasised that fairness and inclusivity were key to realising the benefits of AI for everyone. Participants noted the importance of diversity in regard to the data used to train and build AI , as well as the teams who develop, deploy, and regulate AI . Participants suggested co-creation and collaboration with marginalised communities would ensure that AI could create benefits for everyone.

36. Participants also stressed that organisations using AI not only need to be transparent about when and how AI is used but should also make explanations accessible to different groups. Participants noted that, while AI can offer benefits to marginalised communities, these populations often face a disproportionate negative impact from AI . Participants called for more education on the use of AI on the grounds that there is currently a significant lack of consumer awareness, organisational knowledge, and accessible redress routes.

37. Participants noted that regulators have a key role to play in improving access to contest and seek redress for AI -related harms. Participants emphasised that regulators require adequate funding and resources in order to achieve this. Participants strongly supported a central ombudsman for AI to improve the accessibility of high-quality legal advice on AI . Many noted that legal advice on AI is currently expensive, hard to access, and sometimes given by unregulated providers outside of the legal profession. Participants also noted that the ombudsman would likely receive a large number of small-scale complaints, which they should be adequately equipped to deal with.

38. Participants also advocated for the importance of specific safeguards for young people including potential changes to existing statutory mechanisms such as those for data protection and equality.

Academia, research organisations, and think tanks

39. We held three events to hear from academics, research organisations, and think tanks on AI safety, legal responsibility for AI , and the UKRI ’s AI Technology Mission.

40. Participants suggested differentiating the types of risk posed by AI , noting that both immediate and long term risks would need to be factored into any safety measures for AI . Participants felt that sector-specific analysis should inform assessments of AI -related risks. Participants noted that the technical obscurity of AI can make it difficult for organisations and regulators to determine the cause of any harms that arise. Participants emphasised that, in order to prevent harms, pre-deployment measures are key to ensuring that AI is safe for market release.

41. Participants argued that high quality regulation can help AI move quickly and safely from development to market. Participants argued that there was a need for greater technical knowledge across government and regulators, along with better AI skills across the wider ecosystem. Some called for the certification of AI engineers and developers to enhance public confidence, while another promoted the certification of institutional leads responsible for decisions related to AI . There was no consensus on whether a new, central regulator for AI or existing regulators would implement the proposed framework most effectively. However, participants agreed that aligning regulatory guidance and sharing expertise across sectors would build compliance capability. Participants suggested a “mixed economy” of regulation, with statutory requirements to ensure rules worked effectively.

42. Participants noted that AI life cycles are varied and complex. Participants wanted the government to define actors across the AI life cycle and determine corresponding obligations to clarify the landscape. However, there was no agreement on the best way to do this with participants suggesting actors may be defined by their function (as in data protection regulation), market power or benefit (as in digital markets regulation), or proximity to and reasonable foreseeability of risks (as in product safety legislation). While some participants wanted to see more stringent responsibilities for foundation model developers, others warned that too too narrow a focus could mean that other AI -related opportunities might be missed.

Annex B: List of consultation respondents

List of consultation respondents.

1. We are grateful to all the individuals and organisations who shared their insights with us over the course of the consultation period.

2. Our AI regulation framework is intended to be collaborative and we will continue to work closely with regulators, academia, civil society, and the public in order to monitor and evaluate the effectiveness of our approach.

3. In accordance with our privacy notice [footnote 123] and online survey privacy agreement, only those individuals and organisations who submitted evidence through our online survey and consented to our privacy agreement there have their names listed below. The list represents the 209 online survey submissions that we analysed after cleaning the data for duplications, blanks, and spam (see Annex A for details). Names are listed as they were given, with personal names removed if an organisation name was available. We provide 207 names here as 2 responses included no name.

4. Further detail on the organisation type and sector of those we received written responses from by email and online survey can be found in the extended method for analysing written responses in Annex A.

Respondents to the online consultation survey

Adarga Limited

AGENCY: Assuring Citizen Agency in a World with Complex Online Harms

Agile Property & Homes Limited

AI & Partners

AI Centre for Value Based Healthcare

Aidan Freeman

AIethics.ai

Alliance for Intellectual Property

Altered Ltd

Amendolara Holdings Limited

Arran McCutcheon

ART-AI , University of Bath

Arts Council England

Association for Computing Machinery Europe Technology Policy Committee

Association of British HealthTech Industries

Association of Chartered Certified Accountants ( ACCA )

Association of Financial Mutuals

Association of Illustrators

Association of Learned and Professional Society Publishers

Assuring Autonomy International Programme, University of York

Baringa Partners LLP

Barnacle Labs

Barry O’Brien

Ben Hopkinson

BPI British Phonographic Industry

Bristows LLP

British Copyright Council

British Pest Control Association

British Security Industry Association

Brunel University London Centre for Artificial Intelligence: Social & Digital Innovations

BSI Group The Netherlands B.V.

Bud Financial

Calvin Karpenko

Carlo Attubato

Center for AI and Digital Policy Washington, DC. USA

Centre for Policy Studies

Charlie Bowler

Chegg, Inc.

City, University of London

Colin Hayhurst

Congenica Ltd

Craig Meulen

Creators’ Rights Alliance

CTRL-Shift & Collider Health

CyLon Ventures

DACS (Design and Artists Copyright Society)

Daniel Marsden

Darrell Warner Limited

Deborah W.A. Foulkes

Deloitte UK

Developers Alliance

Department for Education ( DfE )

Direct Line Group

Dr. Michael K. Cohen

EasyJet Airline Company Ltd.

Elliott Andrews

Emma Ahmed-Rengers

Enzai Technologies Limited

Experian UK&I

Falcon Windsor

FlyingBinary

ForHumanity

Freeths LLP

Getty Images

GlaxoSmithKline plc

Glenn Donaldson

Global Witness

Greg Colbourn

Greg Mathews

Hugging Face

International Federation of the Phonographic Industry ( IFPI )

INRO London

Institute for the Future of Work

Institute of Chartered Accountants in England and Wales ( ICAEW )

Institute of Innovation and Knowledge Exchange ( IKE Institute )

Institute of Physics and Engineering in Medicine

Institute of Physics and Engineering in Medicine (Clinical and Scientific Computing group)

Institution of Occupational Safety and Health

International Federation of Journalists

Jake Bailey

Jake Wilkinson

Japan Electronics and Information Technology Industries Association

Joe Collman

Johnson & Johnson

Jonas Herold-Zanker

Joseph Johnston

Judith Barker

Kainos Software Ltd

Kelechi Ejikeme

Knowledge Associates Cambridge Ltd.

Labour for the Long Term

Legal & General Group PLC

Leverhulme Centre for the Future of Intelligence

LSE Law, Technology and Society Group

Lucy Purdon

Luke Richards

Lumi Network

Market Research Society

Martin Gore

Mastercard Europe

MedTech Europe

Megha Barot

Michael Fisher

Michael Pascu

Mind Foundry

Mukesh Sharma

National Physical Laboratory

National Taxpayers Union Foundation ( NTUF )

National Union of Journalists

Nebuli Ltd.

Newcastle University

Nicole Hawkesford

Office for Standards in Education, Children’s Services and Skills ( Ofsted )

Office for Statistics Regulation

Paul Ratcliffe

Pippa Robertson

Planar AI Limited

Policy Connect

Professional Publishers Association

Professor Julia Black

PRS for Music

Publishers Association

Publishers’ Licensing Services

Pupils 2 Parliament

Queen Bee Marketing Hive

Rebecca Palmer

Royal Photographic Society of Great Britain

SambaNova Systems inc

Samuel Frewin

ScaleUp Institute

Scott Timcke

Sharon Darcy

Simon Kirby

Skin Analytics Ltd

South West Grid for Learning

Stability AI

Steve Kendall

STFC Hartree Centre

Surrey Institute for People-Centred Artificial Intelligence

Teal Legal Ltd

Temple Garden Chambers

The Copyright Licensing Agency Ltd

The Data Lab Innovation Centre

The Institute of Customer Service

The Multi-Agency Advice Service ( MAAS ) AI and Digital Regulations Service for health and social care.

The Operational Research Society

The Pharmacists’ Defence Association ( PDA )

The Physiological Society

The Publishers Association

The Society of Authors

The University of Winchester

Tom Edward Ashworth

TRANSEARCH International

Trilateral Research

University of Edinburgh

University of Winchester

Valentino Giudice

W Legal Ltd

Wales Safer Communities Network (membership from Police, Fire, Local Authorities, Probation and Third Sector), hosted by WLGA

Warwickshire County Council

Writers’ Guild of Great Britain

Annex C: Individual question summaries

The revised cross-sectoral ai principles.

1. A majority of respondents agreed that requiring organisations to make it clear when they are using AI would adequately ensure transparency. Respondents who disagreed either felt labelling AI use would be insufficient or disproportionately burdensome.

2. Respondents who argued the measure would be insufficient often stated that regulators lack the relevant powers, funding, and capabilities to adequately ensure transparency. Linked to this, respondents noted issues around enforcement and access to appeal and redress. Some respondents recommended that the government should consider relevant statutory measures and accountability mechanisms. A few respondents suggested that explanations should be targeted to the context and audience.

3. Other respondents were concerned that a blanket requirement for transparency would create a burdensome barrier for lower risk AI applications. One respondent noted that the proposal assumes a single actor in the AI value chain will have adequate visibility across potentially many life cycle stages and applications. A few respondents wanted to see clear thresholds (including “high-risk applications”) and guidance from the government and regulators on transparency requirements.

4. Respondents were concerned that transparency measures may have potential interactions with existing and forthcoming legislation, such as that for data protection and intellectual property.

5. There was strong support for a range of transparency measures from respondents. Respondents stressed that transparency was key to building public trust, accountability, and an effective and verifiable regulatory framework.

6. Many respondents endorsed clear reporting obligations on the inputs used to build and train AI . Respondents noted that transparency would be improved through the disclosure of a range of inputs, from data to compute. Echoing responses to question F1 on foundation models, concerns coalesced around whether training data was of sufficient quality, compliant with existing legal frameworks including intellectual property and data protection, and appropriate for downstream uses. A few respondents argued that compute disclosure would improve transparency on the environmental impacts of AI .

7. Many respondents also supported the labelling of AI use and outputs, with many recommending the measure to improve user awareness and organisational accountability. Some respondents suggested that labelling AI generated outputs would help combat AI generated misinformation and promote intellectual property rights. A few respondents wanted to see clearer opt-ins for uses of data and AI , with options for human alternatives.

8. Some respondents endorsed measures that would encourage explanations for AI outcomes and potential impacts. This includes measures for showing users how models produced outputs or answers as well as addressing model limitations and impacts. Similarly, a few respondents noted the importance of organisational and public education through accessible information and targeted awareness raising. A couple of respondents suggested public or organisational registers for (high risk) AI would help improve awareness.

9. While some respondents advocated for reporting on model details, many emphasised that complex technical information would be best disclosed to regulators and independent verifiers rather than the public. Respondents suggested that organisations share technical model details such as weights, parameters, uses, and testing. Respondents stated that impact and risk assessments, as well as governance and marketing decisions, should be available to either regulators or the public, with a few noting potential compromises with trade secrets. Some respondents endorsed independent assurance techniques, such as third-party audits and technical standards.

10. A few respondents suggested clarifying legal rights and responsibilities for AI , with a few of those recommending the introduction of AI legislation and non-compliance measures.

11. Over half of respondents reported that current routes to contest or seek redress for AI -related harms through existing legal frameworks are not adequate. In particular, respondents flagged that a lack of transparency around when and how AI is used prevents users from being able to identify AI -related harms. Similarly, respondents noted that a lack of transparency around the data used to train AI models complicates data protection and prevents intellectual property rights holders from exercising their legal and moral rights. A few respondents also noted the high costs of individual litigation and advocated for clearer routes for individual and collective action.

12. Many respondents wanted to see the government clarify legal rights and responsibilities relating to AI , though there was no consensus on how to do this. Many respondents suggested clarifying rights and responsibilities in existing law through mechanisms such as regulatory guidance. There was also a broad appetite for centralisation in different forms with some respondents advocating for the creation of a central redress mechanism such as a central AI regulator, oversight body, coordination function, or lead regulator. Some respondents wanted to see further statutory requirements, such as licensing.

13. Many respondents stressed the importance of meaningful transparency and some emphasised the need for accessible redress routes. Respondents felt that measures to show users when and how AI is being used would help individuals identify when and how harms had occurred. Respondents wanted to see clear – and in some cases mandatory – routes to contest or seek redress for AI -related decisions. Respondents noted issues with expensive litigation, particularly in relation to infringement of intellectual property rights. Respondents felt that increasing transparency for AI systems would make redress more accessible across a broad range of potential harms and, similarly, that clarifying redress routes would improve transparency. Some respondents noted the importance of international agreements to ensure effective routes to contest or seek redress for AI -related harms across borders. Measures such as moratoriums and mandatory kill switches were only raised by a few respondents.

14. A majority of respondents agreed that the principles would cover the risks posed by AI technologies when implemented effectively. Respondents that disagreed tended to cite concerns around enforcement and a lack of statutory backing to the principles or wider issues around regulator readiness, including capacity, capabilities, and coordination.

15. Respondents often noted a need for the framework to be adaptable, context-focused, and supported by monitoring and evaluation, citing the fast pace of technological change.

16. A few respondents felt the terms of the question were unclear and asked for further detail on effective implementation.

17. Many respondents advocated for the cross-sectoral AI principles to more explicitly include human rights and human flourishing, noting that AI should be used to improve human life. Respondents endorsed different human rights and related values including freedom, pluralism, privacy, equality, inclusion, and accessibility.

18. Some respondents wanted further detail on the implementation of the principles. These respondents often asked for more detail on regulator capacity, noting that the “effective implementation” of the principles would require adequate regulator resource, skills, and powers. A couple of respondents asked for more clarity regarding how regulators and organisations are expected to manage trade-offs, such as explainability and accuracy or transparency and privacy.

19. Linked to this, some respondents wanted further guidance on how the AI principles would interact with and be implemented through existing legislation. Respondents mostly raised concerns in regard to data protection and intellectual property law, though a few respondents asked for a more holistic sense of the government approach to AI in regard to departmental strategies, such as the Ministry of Defence’s AI strategy. Some respondents stated that the principles would be ineffective without statutory backing, with a few emphasising the importance of mandating AI -related accountability mechanisms.

20. Some respondents advocated for the principles to address a range of issues related to operational resilience. These responses suggested measures for adequate security and cyber security, decommissioning processes, protecting competition, ensuring access, and addressing risks associated with over-reliance. A similar number of respondents wanted to see specific principles on data quality and international alignment.

21. A few respondents recommended the inclusion of principles that would clearly correlate with systemic risks and wider societal impacts, sustainability, or education and literacy. In regard to systemic risks, respondents tended to raise concerns about the potential harms that AI technologies can pose to democracy and the rule of law in terms of disinformation and electoral interference.

A statutory duty to regard

22. Over half of respondents somewhat or strongly agreed that a statutory duty would clarify and strengthen the mandate of regulators to implement the framework. However, many noted caveats that are detailed in Q8.

23. Many felt that targeted statutory measures, including expanded regulator powers, would be a more effective statutory intervention. In particular, respondents noted the need for regulators to have appropriate investigatory powers. Some also wanted to see the consequences of breaches more clearly defined. Respondents also suggested specific AI legislation, a new AI regulator, and strict rules about the use of AI in certain contexts as more effective statutory interventions. A couple of respondents mentioned that any AI duties should be on those operating within the market as opposed to on regulators. 

24. Some respondents felt the proposed statutory duty is the most effective intervention and should be implemented. However, other respondents couched their support within wider concerns that the framework would not be sufficiently enforceable without some kind of statutory backing. Nearly a quarter of respondents emphasised that regulators would need enhanced resources and capabilities in order to enact a statutory duty effectively. Other respondents felt that the implementation of a duty to regard could disrupt regulation, innovation, and trust if rushed. These respondents recommended that the duty should be reviewed after a period of non-statutory implementation, particularly to observe interactions with existing law and regulatory remits. A few respondents noted that the end goal and timeframes for the AI regulatory framework were not clear, causing uncertainty.

25. There was some support for the government to mandate measures such as third-party audits, certification, and Environmental, Social and Governance ( ESG ) style supply chain measures, including reporting on training data. A few respondents were supportive of central monitoring to track regulatory compliance and novel technologies that may require an expansion of regulatory scope.

New central functions to support the framework

26. Nearly all respondents agreed that central delivery of the proposed functions would benefit the framework, with many arguing centralised activities would allow the government to monitor and iterate the framework. Many suggested that feedback from regulators, industry, academia, civil society, and the general public should be used to measure effectiveness, with some calling for regular review points to assess whether the central function remained fit for purpose. A few respondents were concerned that some of the proposed activities may already be carried out by other organisations and suggested mapping existing work to avoid duplication.

27. While respondents widely supported the proposed central functions, many wanted to see more detail on the delivery of each activity, with some respondents endorsing a stronger emphasis on engagement and partnerships with existing organisations.

28. Responses highlighted the importance of addressing AI -related risks and building public trust in AI technologies. Some respondents suggested that the government should prioritise the proposed risk function, noting the importance of identifying and assessing risks related to AI . Respondents noted that this risk analysis should include ethical risks, such as bias, and systemic risks to society, such as changes to the labour market. A few respondents emphasised that the education and awareness function would be key to building public trust.

29. Respondents noted the importance of regulatory alignment across sectors and international regimes. Some respondents argued that the central functions should include more on interoperability, noting cyber security, disinformation, and copyright infringement as issues that will require international collaboration.

30. Some respondents suggested that some or all of the central functions should have a statutory underpinning or be delivered by an independent body. Respondents also stressed that, to be effective, the central functions should be adequately resourced and given the necessary technical expertise. This was identified as particularly important to the risk mapping, horizon scanning, and monitoring and evaluation functions.

31. Additional activities or functions suggested by respondents included: statutory powers to ensure the safety and security of highly capable AI models; coordination with the devolved administrations; and oversight of AI compliance with existing laws, including intellectual property and data protection frameworks.

32. Overall, around a quarter of respondents felt that the government should deliver one or more of the central functions. Respondents also highlighted other organisations that could support the central functions, including regulators, technology-focused research institutes and think tanks, private-sector firms, and academic research groups. Many respondents advocated for the regulatory functions to build from the existing strengths of the UK’s regulatory ecosystem. Respondents noted that regulatory coordination initiatives like the Digital Regulation Cooperation Forum ( DRCF ) could help identify and respond to gaps in regulator remits. Respondents also highlighted that think tanks and research institutes such as the Alan Turing Institute, Ada Lovelace Institute, and Institute for the Future of Work have past or existing activities that may complement those described in the proposed central functions.

33. Many respondents felt the central functions could have further activities to support businesses to apply the principles to everyday practices related to AI . Respondents argued that the government and regulators should support industry with training programs and educational resources. Respondents noted that this support would be especially important for organisations operating across or between sectors.

34. Respondents felt that regulators should develop and regularly update guidance to allow business to innovate confidently. Respondents reported that incoherent and expensive compliance processes could stifle innovation and slow AI adoption.

35. Respondents suggested that the government could improve access to high-quality data, ensure international alignment on AI requirements, and facilitate collaboration between regulators, industry, and academia. Some respondents noted that responsible AI innovation is supported by access to high-quality, diverse, and ethically-sourced data. Respondents suggested that government-sponsored data trusts could help improve access to data. Some respondents saw the government playing a key role in ensuring the international harmonisation of AI regulation, noting that interoperability would promote trade and competition. A few respondents suggested that the government could facilitate collaboration between regulators, industry, and academia to ensure alignment between AI regulation, innovation, and research. A small number of respondents suggested introducing AI legislation rather than central functions to provide greater legal certainty.

36. While respondents identified some activities to support businesses to confidently innovate and use AI technologies that should be led by regulators, a majority of respondents suggested that these activities should be delivered by the government.

37. Respondents prioritised transparency from the cross-sectoral principles, with nearly half arguing that individuals and consumers should be able to identify when and how AI is being used by a service or organisation.

38. Many respondents felt that education and training would build public trust in AI technologies and help accelerate adoption. Respondents emphasised that AI literacy should be improved through education and training that enables consumers to use AI products and services more effectively. Respondents suggested training should cover all stages of the AI life cycle and build understanding of AI benefits as well as AI risks. Respondents stated that, along with the government and regulators, education, consumer, and advocacy organisations should help make knowledge accessible.

39. Some respondents wanted to see clearer routes for consumers to contest or seek redress for AI -related harms. Some emphasised the importance of adequate data protection measures. A few respondents noted that AI specific legislation would provide legal certainty and help foster public trust.

13.1. If so, should these activities be delivered by the government, regulators, or a different organisation?

40. While most respondents recommended that the government, regulators, industry, and civil society work together to help individuals and consumers confidently use AI technologies, nearly half of respondents suggested that activities to improve consumer confidence in AI should be delivered by the government.

41. Many respondents suggested the proposed central functions would be the most effective mechanism to avoid overlapping, duplicative, or contradictory guidance. Respondents noted that the central functions would support regulators by identifying cross-sectoral risks, facilitating consistent risk management actions, providing guidance on cross-sectoral issues, and monitoring and evaluating the framework as a whole.

42. While respondents stressed that consistent implementation of the framework across remits would require regulatory coordination, there was no agreement on the best way to achieve this. Some suggested establishing a new AI regulator, a few proposed appointing an existing regulator as the ‘lead regulator’, and others endorsed voluntary regulatory coordination measures, emphasising the role of regulatory fora such as the Digital Regulation Cooperation Forum ( DRCF ).

43. Some respondents suggested that horizontal cross-sector standards and assurance techniques would encourage consistency across regulatory remits, sectors, and international jurisdictions. Respondents recommended clarifying the specific remits of each regulator in relation to AI to promote coherence across the regulatory landscape. A few argued that introducing AI legislation, including putting the AI principles and regulatory coordination into statute, would prevent regulatory divergence.

Monitoring and evaluation of the framework

44. Over half of respondents agreed with the overall approach to monitoring and evaluation set out in the AI regulation white paper. Many commended the proposals for a feedback loop and advised that industry, regulators, and civil society should be engaged to help measure the effectiveness of the framework. Respondents broadly supported an iterative approach and some suggested consulting industry as part of a regular evaluation to assess and adapt the framework. A few respondents advocated for findings from framework evaluations to be publicly available.

45. Some respondents stated that there was not enough detail or that the approach to monitoring and evaluation was unclear. To determine the practicality of the approach, respondents requested more information about the format, frequency, and sources of data that will be developed and used. Some of these respondents stressed the importance of identifying issues with the framework in a timely way. Respondents emphasised that AI risks will need to be continuously monitored, noting that more clarity and transparency is needed on how risks will be escalated and addressed.

46. Many respondents suggested a data driven approach to measuring the impact of the framework would be most effective. Respondents recommended qualitative and quantitative data collection, impact assessments, and key performance indicators ( KPIs ). Examples of possible KPIs included consumer trust and satisfaction, rate of innovation, time to market, complaints and adverse events, litigation, and compliance costs. A few respondents suggested using economic growth to measure the impact of the framework. A couple wanted to see measurements tailored to specific sectors and suggested that the government engage with regulators to understand how they measure regulatory impacts on their respective industries.

47. Just over a quarter of respondents recommended that the government maintain a close dialogue with industry, civil society, and international partners. Respondents repeatedly stressed the importance of gathering a holistic view on impact with many noting that the government should engage with stakeholders who can offer different perspectives on the framework’s efficacy, including start-ups and small businesses. Respondents felt that broad consultation to gather evidence on public attitudes towards the framework and AI more generally would also be useful.

48. Respondents suggested that international interoperability should be monitored to ensure that the framework allows businesses to trade with and develop products for international markets. Some respondents suggested referencing established indicators and frameworks, such as the United Nations Sustainable Development Goals and the Five Capitals, to inform a set of qualitative and quantitative measures.

49. Half of respondents agreed that the approach strikes the right balance between supporting AI innovation; addressing known, prioritised risks; and future-proofing the AI regulation framework. However, some respondents were concerned that the approach would not be able to keep pace with the technological development of AI , stating that adequate future proofing of the framework will depend on retaining flexibility and adaptability when implementing the principles. Respondents wanted greater clarity on the specific areas to be regulated and stressed that regulators need to be proactive in identifying the risk of harm.

50. Over a third of respondents disagreed. Respondents were concerned that the framework does not clearly allocate responsibility for AI outcomes. Some thought that the focus on AI innovation, economic growth, and job creation would prevent a sufficient focus on AI -related risks, such as bias and discrimination.

Regulator capabilities

18. Do you agree that regulators are best placed to apply the principles and the government is best placed to provide oversight and deliver central functions?

51. Nearly all respondents agreed that regulators are best placed to implement the principles and that the government is best placed to provide oversight and deliver the central functions.

52. While respondents noted that regulators’ domain-specific expertise would be key to the effective tailoring of the cross-sectoral principles to sector needs, some also suggested that the government should support regulators to manage AI risks within their remits by building their technical AI skills and expertise.

53. Some respondents argued that the government would need to work closely with regulators to provide effective oversight of the framework and delivery of the central functions. Some also endorsed further collaboration between regulators. A few felt that the government’s oversight of the framework should be open and transparent, advocating for input from industry and civil society.

54. Some respondents were concerned that no current bodies were best placed to support the implementation and oversight of the proposed framework, with a few asking for AI legislation and a new AI regulator.

55. While regulators that responded to this question supported the proposed framework, just over a quarter argued that the key challenge to proportionate and pro-innovation implementation would be coordination. Regulators saw value in sharing best practices to aid consistency and build existing knowledge into sector-specific approaches. Many suggested that strong mechanisms to share information between regulators and the proposed central functions would help avoid duplicate requirements across multiple regulators.

56. Regulators that responded to this question reported inconsistent AI capabilities, with over a quarter asking for further support in technical expertise and others demonstrating advanced approaches to addressing AI within their remits. Regulators identified common capability gaps including a lack of technical AI knowledge and limited understanding of where and how AI is used by those they regulate. Some suggested that government support in building internal organisational capacity would help them to effectively apply the principles within their existing remits, with some noting that they struggle to compete with the private sector to recruit the right technical expertise and skills. A couple of regulators highlighted how initiatives such as the government-funded Regulators’ Pioneer Fund have already allowed them to develop approaches to responsible AI innovation in their remits. Two regulators reported that the scope of their existing statutory remits and powers in relation to AI is unclear. These regulators asked for further details on how the central function would ensure that regulators used their powers and remits in a coherent way as they apply the principles.

57. Over three quarters of respondents agreed that a pooled team of AI experts would be the most effective way to build common capability and address gaps. Respondents felt that a team of pooled AI experts could help regulators to understand AI and address its unique characteristics within their sectors, supporting the consistent application of the principles across remits.

58. While respondents supported increasing regulators’ access to AI expertise, many stressed that a pooled team would need to contain diverse and multi-disciplinary perspectives. Respondents felt the pooled team should bring together technical AI expertise with sector-specific knowledge, industry specialists, and civil society to ensure that regulators are considering a broad range of views in their application of the principles.

59. Some respondents stated that a pool of experts would be insufficient and suggested that in-house regulator capability with sector-specific expertise should be prioritised.

Tools for trustworthy AI

60. There was strong support for the use of technical standards and assurance techniques, with respondents agreeing that both would help organisations to embed the AI principles into existing business processes. Many respondents praised the UK AI Standards Hub and the Centre for Data Ethics and Innovation’s ( CDEI ) work on AI assurance. While some respondents noted that businesses would have a smaller compliance burden if tools and processes were consistent across sectors, others noted the importance of additional sector-specific tools and processes. Respondents also suggested supplementing technical standards with case studies and examples of good practice.

61. Respondents argued that standardised tools and techniques for identifying and mitigating potential risks related to AI would also support organisations to embed the AI principles. Some identified assurance techniques such as impact and risk assessments, model performance monitoring, model uncertainty evaluations, and red teaming as particularly helpful for identifying AI risks. A few respondents recommended assurance techniques that can be used to detect and prevent issues such as drift to mitigate risks related to data. While commending the role of tools for trustworthy AI , a few respondents also expressed a desire for more stringent regulatory measures, such as statutory requirements for high risk applications of AI or a watchdog for foundation models.

62. Respondents felt that tools and techniques such as fairness metrics, transparency reports, and organisational AI ethics guidelines can support the responsible use of AI while growing public trust in the technology. Respondents expressed the desire for third-party verification of AI models through bias audits, consumer labelling schemes, and external certification against technical standards.

63. A few respondents noted the benefits of international harmonisation across AI governance approaches for both organisations and consumers. Some endorsed interoperable technical standards for AI , commending international standards development organisations ( SDOs ) such as the International Organisation for Standardisation ( ISO ) and Institute of Electrical and Electronics Engineers ( IEEE ). Others noted the strength of a range of international work on AI including that by individual countries, such as the USA’s National Institute of Standards and Technology ( NIST ) AI Risk Management Framework ( RMF ) and Singapore’s AI Verify Foundation, along with work on international governance by multilateral bodies such as the Organisation for Economic Co-operation and Development ( OECD ), United Nation ( UN ), and G7 .

Final thoughts

64. Some respondents felt that the AI regulation framework set out in the white paper would benefit from more detailed guidance on AI -related risks. Some wanted to see more stringent measures for severe risks, particularly related to the use of AI in safety-critical contexts. Respondents suggested that the framework would be clearer if the government provided risk categories for certain uses of AI such as law enforcement and places of work. Other respondents stressed that AI can pose or accelerate significant risks related to privacy and data protection breaches, cyberattacks, electoral interference, misinformation, human rights infringements, environmental sustainability, and competition issues. A few respondents were concerned about the potential existential risk posed by AI . Many respondents felt that AI technologies are developing faster than regulatory processes.

65. Respondents argued that the success of the framework relies on sufficient coordination between regulators in order to provide a clear and consistent approach to AI across sectors and markets. Respondents also noted that different sectors face particular AI -related benefits and risks, suggesting that the framework would need to balance the consistency provided by cross-sector requirements with the accuracy of sector-specific approaches. In particular, respondents flagged that any new rules or bodies to regulate AI should build from the existing statutory remits of regulators and relevant regulatory standards. Respondents also noted that regulators would need to be adequately resourced with technical expertise and skills to implement the framework effectively.

66. Respondents consistently emphasised that effective AI regulation relies on international harmonisation. Respondents suggested that the UK should work towards an internationally aligned regulatory ecosystem for AI by developing a gold standard framework and promoting best practice through key multilateral channels such as the OECD , UN , G7 , and G20 . Respondents noted that divergent or overlapping approaches to regulating AI would cause significant compliance burdens. Respondents argued that international cooperation can support responsible AI innovation in the UK by creating clear and certain rules that allow investments to move across multiple markets. Respondents also suggested establishing bilateral working groups with key strategic partners to share expertise. Some respondents stressed that the UK’s pro-innovation approach should be delivered at pace to remain competitive with a fast moving international landscape.

Legal responsibility for AI

67. Respondents felt that there were two core challenges for regulators applying the principles across different AI applications and systems: a lack of clear legal responsibility across complicated AI life cycles and issues with coordination across regulators and sectors.

68. Over a quarter of respondents felt it was not clear who would be held liable for AI -related risks. Some respondents raised a further concern about confusing interactions between the framework and existing legislation.

69. While nearly half of respondents were concerned about coordination and consistency across sectors and regulatory remits, some indicated that a solution (and the strength of the framework) lay in a context-based approach. Respondents asked for sector-based guidance from regulators, compliance tools, and regulator engagement with industry.

70. Many respondents suggested introducing statutory requirements or centralising the framework within a single organisational body, but there was no consensus over whether this centralisation should take the form of a lead regulator, central regulator, or coordination function. Some respondents suggested mandating industry transparency or third-party audits.

71. Respondents also raised a lack of international standards and agreements as a challenge, pointing to the importance of international alignment and collaboration.

72. While some respondents somewhat agreed that the principles would allocate legal responsibility for AI fairly and effectively through existing legal frameworks, most respondents either disagreed or neither agreed nor disagreed. Many respondents stated that it is not clear how the AI regulation principles would be implemented through existing legal frameworks. Respondents voiced concerns about gaps in existing legislation including intellectual property, legal services, and employment law. Some respondents stated that intellectual property rights needed to be affirmed and clarified to improve legal responsibility for AI . A few respondents noted the need for the AI framework to monitor and adapt as the technology advances and becomes more widely used. One respondent noted that the burden of liability falls at the deployer level and suggested that it would be essential to address information gaps in the AI life cycle to improve the allocation of legal responsibility.

73. Many respondents felt that the framework needed to further clarify liability across the AI life cycle. In particular, respondents repeatedly noted the need for a legally responsible person for AI and some suggested a model similar to Data Protection Officers.

74. Over a quarter of respondents stated that new AI legislation or regulator powers would be necessary to effectively allocate liability across the life cycle. Some named specific measures that would need statutory underpinning, with a few advocating for licensing and pre-approvals and a couple suggesting a moratorium on the most advanced AI .

75. Others felt that it would be best to clarify legal responsibility for AI according to existing frameworks. Respondents wanted clarity on how the principles would be applied with or through existing law, with some suggesting that regulatory guidance would provide greater certainty.

76. Respondents also suggested that non-statutory measures such as enhancing technical regulator capability, domestic and international standards, and assurance techniques would help fairly and effectively allocate legal responsibility across the AI life cycle.

77. Others noted that the proposed central functions, including risk assessment, horizon scanning, and monitoring and evaluation, would be key to ensuring that legal responsibility for AI was fairly and effectively distributed across the life cycle as AI capabilities advance and become increasingly used.

L3. If you are a business that develops, uses, or sells AI , how do you currently manage AI risk including through the wider supply chain? How could government support effective AI -related risk management?

78. Nearly half of respondents to this question told us that they had implemented risk assessment processes for AI within their organisation. Many used existing best practice processes and guidance from their sector or trade bodies such as techUK. Some felt that the proliferation of different organisational risk assessment processes reflected the absence of overarching guidance and best practice from the government. Of these respondents, many suggested that it would be useful for businesses to understand the government’s view on AI -related best practices, with some recommending a central guide on using AI safely.

79. Many respondents noted their compliance with existing legal frameworks that capture AI -related risks, such as product safety and personal data protections. Respondents highlighted that any future AI measures should avoid duplicating or contradicting existing rules and laws.

80. Respondents consistently stressed the importance of transparency, with some highlighting information sharing tools like model cards. Similarly to Q2, some respondents suggested that labelling AI use would be beneficial to users, particularly in regard to building literacy around potentially malicious AI generated content, such as deepfakes and disinformation. A few respondents argued that AI labelling can help shape expectations of a service and should be a consumer protection. Echoing answers to F1, respondents also mentioned that services should be transparent about the data used to train AI models so users can understand how tools and services work as well as their limitations.

81. Responses showed that the size of an organisation shaped the capacity to assess AI -related risks. While larger organisations mentioned that they engage with customers and suppliers to shape and share best practices, some smaller businesses asked for further support to assess AI -related risk and implement the AI principles effectively.

Foundation models and the regulatory framework

82. While respondents supported the AI regulation framework set out in the white paper, many were concerned that foundation models may warrant a bespoke regulatory approach. In particular, respondents noted that foundation models are characterised by their technical complexity and stressed their potential to underpin many different applications across multiple sectors. Nearly a quarter of respondents emphasised that foundation models make it difficult to determine legal responsibility for AI outcomes, with some sharing hypothetical use-cases where both upstream and downstream actors are at fault. Respondents stressed that technical opacity, complex supply chains, and information asymmetries prevent sufficient explainability, accountability, and risk assessment for foundation models.

83. Many respondents were concerned about the quality of the data used to train foundation models and whether training data is appropriate for all downstream model applications. Respondents stated that it was not clear whether data used to train foundation models complies with existing laws, such as those for data protection and intellectual property. Respondents noted that definitions and standards for training data were lacking. Respondents felt that data use could be improved through better information sharing measures, benchmark measurements and standards, and the clear allocation of responsibility to a specific actor or person for whether or not data is appropriate to a given application.

84. Some respondents emphasised the complexity of foundation model supply chains and argued that information asymmetries between upstream developers (with technical oversight) and downstream deployers (with application oversight) not only muddies legal responsibility for AI outcomes but also prevents sufficient risk monitoring and mitigation. While some respondents noted the concentrated market power of foundation model developers and suggested these actors were best positioned to mitigate related risks, others argued that developers would have limited sight of the risks linked to specific downstream applications. Many raised concerns about the lack of measures to rigorously judge the appropriateness of a foundation model to a given application.

85. A few respondents noted concerns regarding wider access to AI , including open source, leaking, or malicious use. However, a similar number of respondents noted the importance of open source to AI innovation, transparency, and trust.

86. Half of respondents felt compute was an inadequate proxy for governance requirements, with many arguing that the fast pace of technological change would mean compute-related thresholds would be quickly outdated. However, nearly half somewhat agreed that measuring compute would be useful for foundation model governance, suggesting that it could be used to assess whether a particular AI model should follow certain requirements when used with other governance measures. A few respondents noted that measuring compute would be one way to capture the environmental impact of different AI models.

87. There was wide support for governance measures and tools for trustworthy AI , with respondents advocating for the use of organisational governance, technical standards, and assurance techniques dedicated to foundation models.

88. Some respondents recommended assessing foundation model capabilities and applications rather than compute. Respondents felt that model verification measures, such as audits and evaluations, would be effective, with some suggesting these should be mandatory requirements. Some respondents noted the importance of downstream monitoring or post-market surveillance. One respondent suggested a pre-deployment sandbox.

89. A small number of respondents wanted to see statutory requirements on foundation models. A few endorsed moratoriums, bans, or limits on foundation models and uses. Others suggested using contracts, licences, and user agreements, with respondents also noting the importance of both physical and cyber security measures.

AI sandboxes and testbeds

S1. Which of the sandbox models described in section 3.3.4 would be most likely to support innovation?

90. While a large majority of respondents were strongly supportive of sandboxes in general, the “multiple sector, multiple regulator” ( MSMR ) and “single sector, multiple regulator” ( SSMR ) models were seen to most likely support innovation.

91. Over a third of respondents felt the MSMR model would support innovation, noting that the cross-sectoral basis would enable regulators to develop effective guidance in response to live issues, harmonise rules, coordinate implementation, ensure applicability to safety critical sectors, and identify complementary policy levers. Respondents suggested that a MSMR sandbox should tackle issues related to the implementation of the AI principles, including identifying and addressing any gaps in the framework, overlap with existing regulation, coordination challenges between sectors and regulators, and any blockers to effective implementation of the regulatory framework, such as regulator capacity. Respondents also stressed that the sandbox should be flexible and adaptable in order to future proof against new technological developments.

92. An equal number of respondents endorsed the SSMR model. Respondents noted that the SSMR and “multiple sector, single regulator” (MSSR) models would be easier to launch due to their more streamlined coordination across a single sector or regulator. For this reason, respondents felt that these models might drive the most immediate value. Some suggested that an initial single sector or single regulator sandbox could be adapted into a MSMR model as work progressed in order to capture the benefits of both models.

S2. What could the government do to maximise the benefit of sandboxes to AI innovators?

93. Some respondents argued that the sandbox should be developed and delivered in collaboration with businesses, regulators, consumer groups, and academics and other experts. Respondents suggested building on the existing strengths of the UK regulatory landscape, such as facilitating cross-sector learnings through the Digital Regulation Cooperation Forum ( DRCF ).

94. Respondents stated that the sandbox should develop guidance, share information and tools, and provide support to AI innovators. In particular, respondents said that information about opportunities for involvement should be shared and noted that sharing outcomes would encourage wider participation. Respondents wanted the sandbox to be open and transparent, with many advocating for sandbox processes, regulatory assessments and reports, decision processes, evidence reviews, and subsequent findings to be made available to the public. Respondents suggested that regular reports and guidance from the sandbox would inform innovators and future regulation by creating “business-as-usual” processes. Respondents felt that measures should be taken to make the sandbox as accessible as possible, with a few advocating for dedicated pathways and training for smaller businesses.

95. Respondents felt that the sandbox should be used to inform and develop technical standards and assurance techniques that can be widely used. A few mentioned that this would help promote best practice across industry. Others noted that, to be most beneficial, the sandbox should be well aligned with wider regulation for AI . Respondents also noted that a sandbox presents an opportunity for the UK to demonstrate global leadership in AI regulation and technical standards by sharing findings and best practices internationally.

96. Respondents noted that the sandbox could support innovation by providing market advantages, such as product certification, to maximise the benefits to AI innovators. Other financial incentives suggested by respondents included innovation grants, tax credits, and free or funded participation in supervised test environment sandboxes. A few stakeholders agreed that funding would help start-ups and smaller businesses with less organisational resources to participate in research and development focused sandboxes. Respondents suggested that the sandbox could collaborate with UK and international investment companies to build opportunities for participating companies.

S3. What could the government do to facilitate participation in an AI regulatory sandbox?

97. Some respondents suggested that grants, subsidies, and tax credits would encourage participation by smaller businesses and start-ups in resource-intensive, research and development focused sandbox models such as supervised test environments.

98. Respondents endorsed a range of incentives to facilitate participation in different sandbox models including access to standardised and anonymised datasets, and accreditation schemes that would show alignment with regulatory requirements and help gain market access. There was some support for innovation competitions that would help select participants.

99. Similarly to S2, respondents agreed that collaboration and consultation with a range of stakeholders would help facilitate broad participation. Respondents suggested research centres, accelerator programmes, and university partnerships. There was support for a diverse group of stakeholders to be involved in the early stages of sandbox development, especially to identify regulatory areas with high risk. There was some support for harmonised evaluation frameworks across sectors to reduce regulatory burden and encourage wider interest from prospective stakeholders. One respondent proposed a dedicated online platform that would provide access to relevant guidance and provide a portal for submitting and tracking applications along with a community forum.

100. There was broad support for a simple application process with clear guidelines, templates, and information on eligibility and legal requirements. Respondents expressed support for clear entry and exit criteria, noting the importance of reducing the administrative burden on smaller businesses and start-ups to lower the barrier to entry.

101. While there was no overall consensus on a specific sector or class of product that would most benefit from an AI sandbox, respondents identified two “safety-critical” sectors with a high-degree of potential risk: healthcare and transport. Respondents noted that these sectors are characterised by an inability for real-world testing and would benefit from an AI sandbox. Respondents noted the potential to enhance healthcare outcomes, patient safety, and compliance with patient privacy guidelines by fostering innovation in areas such as diagnostic tools, personalised medicine, drug discovery, and medical devices. Other respondents noted the rise of autonomous vehicles and intelligent transportation systems along with significant enthusiasm from industry to test the regulatory framework.

102. Some respondents suggested that financial services and insurance would benefit from an AI sandbox due to heavy investment from the sector in automation and AI . Respondents also noted that financial services and insurance are also overseen by multiple regulators, including the Information Commissioner’s Office ( ICO ), Prudential Regulation Authority ( PRA ), Financial Conduct Authority ( FCA ), and The Pensions Regulator ( TPR ). Respondents noted that financial services could leverage an AI sandbox to explore AI -based applications for risk assessment, fraud detection, algorithmic trading, and customer service.

103. It was noted by one respondent that the nuclear sector is currently already benefiting from an AI sandbox. The Office for Nuclear Regulation ( ONR ) and the Environment Agency ( EA ) have taken the learnings from their own regulatory sandbox to develop the concept of an international AI sandbox for the nuclear sector.

Annex D: Summary of impact assessment evidence

This annex provides a summary of the written evidence we received in response to our consultation on the AI regulation impact assessment [footnote 124] . We asked eight questions including seven open or semi-open questions that received a range of written reflections. We asked:

  • Do you agree that the rationale for intervention comprehensively covers and evidences current and future harms?
  • Do you agree that increased trust is a significant driver of demand for AI systems?
  • Do you have any additional evidence to support the following estimates and assumptions across the framework?
  • Do you agree with the estimates associated with the central functions?
  • Are you aware of any alternative metrics to measure the policy objectives?
  • Do you believe that some AI systems would be prohibited in Options 1 and 2, due to increased regulatory scrutiny?
  • Do you agree with our assessment of each policy option against the objectives?
  • Do you have any additional evidence that proves or disproves our analysis in the impact assessment?

In total we received 64 written responses on the impact assessment consultation from organisations and individuals. The method of our analysis is captured in Annex A and a summary of responses to these questions follows below. 

Question 1: Do you agree that the rationale for intervention comprehensively covers and evidences current and future harms?

Summary of responses:

More than half of respondents disagreed that the rationale for intervention comprehensively covers evidence of current and future harms. Nearly half of respondents stated that not all risks are adequately addressed. Many of these respondents argued that the rationale does not account for unexpected harms or existential and systemic risks. One respondent argued that the rationale does not consider the impact of AI on human rights. Another respondent suggested that there should be mandatory requirements for the ethical collection of data and another advocated for pre-deployment measures to mitigate AI risks.

Over a quarter of respondents suggested analysing risks and opportunities for each sector. These respondents often argued that the potential harms and benefits in different industries are not accounted for, such as the impact of AI on jobs.

Some respondents advocated for the government to build the evidence on current and future harms as well as potential interventions. Many of these respondents emphasised the importance of including diverse perspectives and the public voice when conducting research and regulating AI .

A few respondents noted that the government and regulators should adopt a flexible approach that monitors and can adapt to technological developments.

A few respondents stated that excessive regulation and government intervention will stifle innovation instead of encouraging it. These respondents argued that there needs to be a balance between mitigating risks and enabling the benefits of AI .

One respondent stated that there should be an independent regulator for AI .

Question 2: Do you agree that increased trust is a significant driver of demand for AI systems?

Over half of respondents agreed that trust is a significant driver of demand for AI systems. However, around a quarter disagreed and some remained unsure.

Over a third of respondents gave a written answer that could provide further insight outside of agreeing or disagreeing. Of these, many respondents stressed that transparency, education, and governance measures (such as regulation and technical standards) increase trust. These ideas were reflected in both respondents who agreed and disagreed in trust driving demand for AI .

Respondents also argued that trust in AI could be reduced by concerns about bias or safety. Some of these respondents highlighted that unfair or untransparent bias in AI systems not only reduces trust but impacts already marginalised communities the most. Some respondents argued that prioritising innovation over trust in a regulatory approach would reduce trust.

Of the respondents that disagreed that trust was a driver of AI uptake and provided further written responses, two main themes emerged. First, that demand for AI is driven by economic and financial incentives and, second, that it is driven by technological developments. For example, one respondent highlighted that AI could increase productivity and thus the profitability of companies. Respondents also highlighted technological developments as a driver for AI demand, with two respondents stating that companies’ “fear of missing out” in new technologies could drive their demand for AI systems. 

Respondents that disagreed often suggested that increasing AI demand and adoption comes at the cost of safeguarding the public and risk mitigation.

Question 3: Do you have any additional evidence to support the following estimates and assumptions across the framework?

Respondents reacted to each statement differently. There was a mixed amount of agreement across all statements. In written feedback, some respondents suggested that our estimates and assumptions depend on complex factors or that it is not possible to provide estimates about AI due to too many uncertainties.

For the first estimation, that 431,671 businesses will be impacted by adopting/consuming AI less than the estimated 3,170 businesses supplying/producing AI , disagreeing respondents found that it understates the number of businesses that will likely be affected by AI , that the number can rapidly change as it is easy to integrate AI into a product or service, that the division between AI adopters and producers is somewhat artificial, and that consumers should also be considered. 

For the second statement, saying that those who adopt/consume AI products and services will face lower costs than those who produce and/or supply AI solutions products and services, there was some disagreement and one response that agreed. Those who disagreed with the statement argued that consumers of AI will have lower costs than producers of AI since consumers and users more widely can face (increasing) costs of using AI applications. On the other hand, one respondent mentioned that cost savings will apply to users without a deep understanding of the technology and producers will face high salary costs because of a small pool of labour talent able to operate advanced AI systems.

Concerning the third estimate of familiarisation costs (here referring to the cost of businesses upskilling employees in new regulation) landing in the range of £2.7 million to £33.7 million, a couple of respondents that disagreed stated that familiarisation costs could vary from business to business. These respondents argued the current range was understating the full costs and recommended considering other costs. Some suggested that consumers need to be trained on residual risk and how to overcome automation bias. Others mentioned that the independent audit of AI systems will create many new highly-trained jobs.

Finally, on the fourth estimation that compliance costs (here reflecting the cost of businesses adjusting business elements to comply with new standards) will land in the range of  £107 million to £6.7 billion, there was further disagreement. Some respondents said that compliance costs should be as low as possible, but there was no agreement on how best to achieve this. Other respondents stated that companies will not comply and that compliance would necessitate new business activities.

Question 4: Do you agree with the estimates associated with the central functions?

A slight majority of respondents somewhat disagreed with the estimates outlined in the AI regulation impact assessment, suggesting that central function estimates are too high. Some respondents mentioned that the central function could deploy AI and use automation to harness efficiency and drive down cost estimates. Two respondents also highlighted that the central function could employ techniques such as peer-to-peer learning and networks to drive down cost estimates.

On the other hand, some respondents indicated that central function estimates are too low. Some respondents believe that the current estimates are too low because they do not account for costs associated with late upskilling of central function employees. One respondent suggested that the increasing demand for AI from the commercial sector would raise costs further, and create challenges in the central function accessing AI solutions due to inflationary cost pressure. Some respondents suggested that the expanding scale and capabilities of AI would require a larger central function to regulate the technology, arguing current costs are likely to be conservative estimates.

A few respondents did agree that the estimates are accurate. However, many noted that it would be a challenge to pin a specific number to the estimates associated with the central function, and suggested that a lack of clarity in defining terms made it difficult to assess accuracy of the estimates.

Question 5: Are you aware of any alternative metrics to measure the policy objectives?

More than a third of respondents suggested alternative metrics that could be used to measure the policy objectives. Some suggestions included tracking the number of models being audited for bias and fairness; the number of AI -related incidents being reported and investigated; and metrics related to the framework’s operation such as the number of regulators publishing guidance, the nature of guidance and associated outcomes for organisations that have adopted it, or sentiment indicators from stakeholders. Other suggestions included tracking public trust and acceptance of AI systems.

Almost a quarter of respondents suggested existing frameworks and models. A couple of respondents suggested that effective assessment and regulation of harm would be key to measuring the policy objectives.

Question 6: Do you believe that some AI systems would be prohibited in options 1 and 2 due to increased regulatory scrutiny?

Over half of respondents agreed that some AI systems would be prohibited in options 1 and 2 due to increased regulatory scrutiny. Around a quarter of respondents disagreed and just under a third were unsure.

Of respondents that expanded on their thoughts, a third suggested that some AI systems present a threat to society and should be prohibited. These respondents emphasised that prohibition would reduce AI risks and saw prohibition as a positive impact. Some suggested that a lack of any prohibition would represent a failure of the regulatory framework.

Some stakeholders suggested that some AI systems would be prohibited. However, a similar amount suggested that the regulatory scrutiny under options 1 and 2 would not be sufficient enough to prohibit AI systems. These two sets of responses reflected conflicting understanding around the intensity of the proposed regulations, as opposed to inherent views on how regulation might impact the sector. A few indicated that the impact assessment was unable to provide enough evidence around which AI systems might be prohibited.

Question 7: Do you agree with our assessment of each policy option against the objectives?

Just over a third of respondents either strongly or somewhat agreed with the assessment of each policy option against objectives, with most responding that they somewhat agree. A similar amount either strongly or somewhat disagreed, with most of these responding that they only somewhat disagreed. Around a quarter of respondents neither agreed nor disagreed, or indicated they were unsure.   

Question 8: Do you have any additional evidence that proves or disproves our analysis in the impact assessment?

Almost half of written responses suggested that the AI regulation impact assessment insufficiently estimated the impacts of AI . These respondents indicated that the impacts of AI are much larger and more harmful than is implied by the AI regulation impact assessment and white paper.

Just under a third indicated that the government should act quickly to regulate emerging AI technologies. These respondents emphasised that timely action should be a key focus for AI regulation given the quickly advancing capabilities of the technology.

Some respondents indicated that there was too great a degree of uncertainty to make accurate assessments. These respondents thought that any estimation would be inaccurate due to the nature of AI and the many uncertainties around future developments.

Some respondents suggested that regulators should harmonise their approach to AI , emphasising that the use of these technologies across sectors requires coordinated and consistent regulation.

United Kingdom Artificial Intelligence Market , US International Trade Administration, 2023.  ↩

Frontier AI : capabilities and risks , Department for Science, Innovation and Technology, 2023.  ↩

New advisory service to help businesses launch AI and digital innovations , Department for Science, Innovation and Technology, 2023.  ↩

To support the government’s planning and policy development, and given the material uncertainties that exist, the Government Office for Science has prepared a foresight report outlining possible scenarios that may arise in the context of AI development, proliferation and impact in 2030. See: Future risks of frontier AI (Annex A), Government Office for Science, 2023. A full report on the scenarios will be published shortly (this report will not be a statement of government policy).  ↩

Prime Minister’s speech on AI : 26 October 2023 , Prime Minister’s Office, 10 Downing Street, 2023.  ↩

International Science Partnerships Fund , UKRI , 2023.  ↩

How should AI systems behave, and who should decide? , OpenAI, 2023.  ↩

Safety and Security Risks of Generative Artificial Intelligence to 2025 , Government Office for Science, 2023.  ↩

We provide further detail on this area as part of our description of the cross-sectoral safety, security and robustness principle in the AI regulation white paper. See: AI regulation: a pro-innovation approach, Department for Science, Innovation and Technology, 2023.  ↩

Large dedicated AI companies make a major contribution to the UK economy, with GVA (gross value added) per employee estimated to be £400,000, more than double that of comparable estimates of large dedicated firms in other sectors. See: AI Sector Study 2022 , Department for Science, Innovation and Technology, 2023.  ↩

The Global AI Index Tortoise Media, 2023.  ↩

AI regulation: a pro-innovation approach , Department for Science, Innovation and Technology, 2023.  ↩

AI regulation: a pro-innovation approach – policy proposals , Department for Science, Innovation and Technology, 2023.  ↩

Frontier AI : capabilities and risks , Department for Science, Innovation, and Technology, 2023.  ↩

Race towards ‘autonomous’ AI agents grips Silicon Valley , Anna Tong and Jeffrey Dastin, 2023; Introducing superalignment , Jan Leike and Ilya Sutskever (OpenAI), 2023; AI could be one of humanity’s most useful inventions , Google Deepmind, n.d..  ↩

Employment Outlook 2023: artificial intelligence and jobs , OECD , 2023.  ↩

Generative AI and the UK labour market, KPMG, 2023; The economic potential of generative AI : the next productivity frontier , McKinsey, 2023; What drives UK firms to adopt AI and robotics, and what are the consequences for jobs?, Institute for the Future of Work, 2023.  ↩

ChatGPT is the fastest growing app in the history of web applications , Cindy Gordon, 2023.  ↩

Using AI to monitor trackside Britain’s wildlife , Zoological Society London, 2023.  ↩

A foundation model for generalizable disease detection from retinal images , Esma Aïmeur et al., 2023.  ↩

Synthetic lies: understanding AI -generated misinformation and evaluating algorithmic and human solutions , Jiawei Zhou et al., 2023; Fake news, disinformation and misinformation in social media: a review, Yukon Zhou et al., 2023; AI could create a perfect storm of climate misinformation , Victor Galaz et al., 2023.  ↩

Dual use of artificial-intelligence-powered drug discovery , Fabio Urbina et al., 2022.  ↩

AI Foundation Models: initial review , CMA , 2023.  ↩

How do we ensure fairness in AI ? , ICO , 2023.  ↩

Software and AI as a Medical Device Change Programme – Roadmap , MHRA , updated 2023 [2021].  ↩

The government has written to the Office of Communications ( Ofcom ); Information Commissioner’s Office ( ICO ); Financial Conduct Authority ( FCA ); Competition and Markets Authority ( CMA ); Equality and Human Rights Commission ( EHRC ); Medicines and Healthcare products Regulatory Agency ( MHRA ); Office for Standards in Education, Children’s Services and Skills ( Ofsted ); Legal Services Board ( LSB ); Office for Nuclear Regulation ( ONR ); Office of Qualifications and Examinations Regulation ( Ofqual ); Health and Safety Executive ( HSE ); Bank of England; and Office of Gas and Electricity Markets ( Ofgem ). The Office for Product Safety and Standards ( OPSS ), which sits within the Department for Business and Trade, has also been asked to produce an update. Regulators will be best placed to determine the form and substance of their update and we encourage all regulators that consider AI to be relevant to their work to publish their approaches. As we continue to implement the framework and assess regulator readiness, our prioritisation of regulators may change to reflect evolving factors such as our risk analysis. We will also work with other regulators and encourage the publication of action plans to drive transparency across the wider ecosystem.  ↩

Response to Professor Dame Angela McLean’s Pro-Innovation Regulation of Technologies Review: Cross Cutting , HM Treasury, 2023.  ↩

£250 million to secure the UK’s world-leading position in technologies of tomorrow , UKRI , 2023.  ↩

Members of the DRCF include the CMA , ICO , FCA , and Ofcom . See: New advisory service to help businesses launch AI and digital innovations , Department for Science, Innovation and Technology, 2023.  ↩

The AI Standards Hub , AI Standards Hub, 2022.  ↩

Portfolio of AI Assurance Techniques , Department for Science, Innovation and Technology, 2023.  ↩

Public attitudes to data and AI : Tracker survey (Wave 3) , Department for Science, Innovation and Technology, 2023.  ↩

We have previously categorised these as societal harms; misuse risks; and loss of control. See: Frontier AI : capabilities and risks , Department for Science, Innovation and Technology, 2023.  ↩

Bridging Responsible AI Divides , BRAID UK, 2024.  ↩

AI Skills for Business Guidance: Feedback Consultation Call from The Alan Turing Institute , Innovate UK, 2023.  ↩

A recent study by the Institute for the Future of Work shows that the net impact on skills and job creation for UK firms that have adopted AI and robotics technologies is positive. However, these positive impacts on jobs and job quality are associated with the levels of readiness within a firm. See: What drives UK firms to adopt AI and robotics, and what are the consequences for jobs? , Institute for the Future of Work, 2023.  ↩

The impact of AI on UK jobs and training , Department for Education, 2023.  ↩

Apprenticeships are for people aged 16 and over who are not in full time education. See: Find an apprenticeship, Department for Education, n.d..  ↩

Skills Bootcamps are for adults aged 19 and over. See: Find a skills bootcamp , Department for Education, 2024 [2022].  ↩

Lifelong Learning Entitlement overview , Department for Education, 2024.  ↩

A world-class education system: The Advanced British Standard , Department for Education, 2023.  ↩

Creative Industries Sector Vision , Department for Culture, Media and Sport, 2023.  ↩

Algorithmic discrimination in the credit domain: what do we know about it? , Ana Cristina Bicharra Garcia et al., 2023.  ↩

Ethics and discrimination in artificial intelligence-enabled recruitment practices, Zhisheng Chen, 2023.  ↩

Fairness Innovation Challenge , Department for Science, Innovation and Technology; Innovate UK, 2023.  ↩

Guidance on AI and data protection , ICO , 2023.  ↩

Covenant for Using Artificial Intelligence ( AI ) in Policing , National Police Chiefs’ Council, n.d..  ↩

Frontier AI : capabilities and risks, Department for Science, Innovation and Technology, 2023.  ↩

Misinformation in action: Fake news exposure is linked to lower trust in media, higher trust in government when your side is in power , Katherine Ognyanova et al., 2020.  ↩

Emerging Processes for Frontier AI Safety, Department for Science, Innovation and Technology, 2023.  ↩

AI Foundation Models: initial review, CMA , 2023.  ↩

Government AI Readiness Index 202 3, Oxford Insights, 2023  ↩

Chancellor to cut admin workloads to free up frontline staff, HM Treasury; Home Office, 2023.  ↩

£21 million to roll out artificial intelligence across the NHS , Department of Health and Social Care, 2023.  ↩

Generative artificial intelligence in education call for evidence , Department for Education, 2023.  ↩

Generative AI Framework for HMG , Cabinet Office and Central Digital and Data Office, 2024.  ↩

Artificial Intelligence, National Cyber Security Centre, n.d..  ↩

Dual use of artificial-intelligence-powered drug discovery, Fabio Urbina et al., 2022.  ↩

UK Biological Security Strategy, Cabinet Office, 2023  ↩

National vision for engineering biology, Department for Science, Innovation and Technology, 2023.  ↩

Practices for Governing Agentic AI Systems , Yonadav Shavit et al., 2023.  ↩

Future Risks of Frontier AI , Government Office for Science, 2023.  ↩

Pause Giant AI Experiments: An Open Letter , Future of Life Institute, 2023.  ↩

We note, for instance, the enforcement action of the ICO who have used data protection law to hold organisations using AI systems that process personal data to account for breaches of data protection law. The CMA ’s initial review of foundation models notes that accountability for obligations under competition and consumer law applies across the AI life cycle to both developers and deployers. See: AI Foundation Models: initial review, CMA , 2023. Similarly, the Medicines and Medical Devices Act 2021 gives the MHRA enforcement powers sufficient to hold manufacturers of medical devices accountable, including the power to require that unsafe devices are removed from the market. In addition, enforcement of serious non-compliance can, where appropriate, result in criminal prosecution through the courts.  ↩

The same model may be deployed directly by the developer and also integrated into an almost limitless variety of systems, products and tools that will fall under the remit of multiple regulators.  ↩

The law may allocate liability to “Quantum Talent Technologies” in this scenario if the actor has established an “agency” relationship according to equality law or was privately contractually obligated to abide by equality law. The law may also attribute liability along the supply chain in negligence if there is a duty of care that has been breached causing foreseeable damage. However, some laws only apply to actors based in the UK. In this scenario, data protection law would apply, allowing the ICO to take enforcement action for any failure by a relevant data controller (such as “Count Your Pennies Ltd”) to process personal data fairly and lawfully.  ↩

Equality Act 2010: guidance , Government Equalities Office and Equality and Human Rights Commission, 2015 [2013].  ↩

Company Policies , AI Safety Summit, 2023.  ↩

Responsible capability scaling is an emerging framework to manage risks associated with highly capable AI and guide decision-making about AI development and deployment. See: Responsible Capability Scaling in Emerging Processes for Frontier AI Safety . Department for Science, Innovation and Technology, 2023.  ↩

International expertise to drive International AI Safety Report , Department for Science, Innovation and Technology, 2024.  ↩

UK International Technology Strategy, Foreign, Commonwealth & Development Office, 2023.  ↩

The Bletchley Declaration by Countries Attending the AI Safety Summit, 1-2 November 2023 , Department for Science, Innovation, and Technology; Foreign, Commonwealth and Development Office; Prime Minister’s Office, 10 Downing Street, 2023.  ↩

World leaders, top AI companies set out plan for safety testing of frontier as first global AI Safety Summit concludes , Prime Minister’s Office, 10 Downing Street; Department for Science, Innovation and Technology, 2023.  ↩

G7 Leaders’ Statement on the Hiroshima AI Process , Ministry of Foreign Affairs Government of Japan, 2023.  ↩

G20 New Delhi Leaders’ Declaration , Ministry of External Affairs Government of India, 2023  ↩

The 17 goals , United Nations, 2023.  ↩

GPAI New Delhi Ministerial Declaration , Global Partnership on AI , 2023.  ↩

OECD AI Principles overview , OECD , 2024.  ↩

CDEI portfolio of AI assurance techniques, Centre for Data Ethics and Innovation; Department for Science, Innovation and Technology, 2023.  ↩

Catalogue of tools and metrics for trustworthy AI , OECD , n.d..  ↩

Recommendation on the Ethics of Artificial Intelligence , UNESCO , 2023.  ↩

Governing AI for Humanity, United Nations, 2023.  ↩

UK unites with global partners to accelerate development using AI , Foreign, Commonwealth & Development Office, 2023.  ↩

The Atlantic Declaration , Prime Minister’s Office, 10 Downing Street, Foreign, Commonwealth & Development Office, Department for Business and Trade, 2023.US  ↩

The Hiroshima Accord: An enhanced UK-Japan global strategic partnership , Prime Minister’s Office, 10 Downing Street, 2023.  ↩

The Downing Street Accord: A United Kingdom-Republic of Korea Global Strategic Partnership , Prime Minister’s Office, 10 Downing Street, 2023.  ↩

Joint Declaration by the Prime Ministers of the Republic of Singapore and the United Kingdom of Great Britain and Northern Ireland on a Strategic Partnership , Prime Minister’s Office, 10 Downing Street, 2023.  ↩

Developed by the Department for Science, Innovation and Technology ( DSIT ) and Central Digital and Data Office ( CDDO ) for the public sector.  ↩

Emerging Processes for Frontier AI Safety , Department for Science, Innovation and Technology, 2023.databases.  ↩

AI Foundation Models: initial review, CMA , 2023; Generative AI & Advertising: Decoding AI Regulation , ASA, 2023; What generative AI means for the communications sector , Ofcom , 2023.  ↩

How do we ensure fairness in AI ? , ICO , 2023; Software and Artificial Intelligence ( AI ) as a Medical Device, MHRA , updated 2023 [2021].  ↩

Fairness Innovation Challenge , Department for Science, Innovation and Technology; InnovateUK, 2023.  ↩

For an overview of DSIT ’s latest research on public attitudes to data and AI , see: Public attitudes to data and AI : Tracker survey (Wave 3) , Department for Science, Innovation, and Technology, 2023  ↩

The ATRS is the Algorithmic Transparency Recording Standard. For more detail see section 5.1.  ↩

Office for Artificial Intelligence – information collection and analysis: privacy notice, Department for Science, Innovation and Technology, 2023.  ↩

UK Artificial Intelligence Regulation Impact Assessment , Department for Science, Innovation and Technology, 2023.  ↩

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