Use of AI in Government

Summary

During this Parliament we shall be exploring how Artificial intelligence (AI) will change government’s operations, and how government in turn is mitigating any risks involved. We will want to make sure any adoption is done in a way that is both safe and ethical. We also want to ensure that the AI industry has a Government that is on its side, one that will not sit back and let opportunities slip through its fingers. In a world of fierce competition, we cannot stand by.

AI has the potential to radically change public services by automating routine tasks, making public services quicker and more efficient, and making better use of government data to target support at those that need it. It has the potential to both transform and disrupt almost every area of life, with implications for a multitude of the policy areas under our remit. However, government faces significant challenges if it is to grasp these opportunities, and we remain concerned at the scale of the task.

AI relies on high quality data and compatible systems, but out–of–date legacy technology and the poor quality of data and data sharing in the public sector is putting AI adoption at risk. Out of the 72 highest–risk legacy digital systems prioritised as part of the 2022–2025 digital and data roadmap, 21 still lack remediation funding, and data quality and data sharing barriers are persistent and long–standing.

The Department for Science, Innovation and Technology’s (DSIT’s) progress in embedding transparency into public sector AI adoption, which is fundamental to building public trust, is also slow. Too few departments are reporting transparently on their algorithm–assisted decision making and DSIT must do more to demonstrate that its assurance of high–risk AI is robust. These issues are exacerbated by a shortage of digital and data skills in government, an issue we have raised concerns about repeatedly in the past. We remain sceptical that the reforms DSIT is planning, including strengthening digital leadership and assessing the competitiveness of the overall package for digital and data professionals, will be sufficient to tackle the skills gap where previous attempts have failed.

To grasp the opportunities of AI, government must also learn from pilots and scale up the most promising examples. However, there is no systematic mechanism for bringing together and disseminating the learning from all the pilot activity across government. Concerted effort and leadership from DSIT is needed or government risks duplicating effort and cost from siloed pilot activity.

Government’s approach to AI procurement will also be key to securing value for money and supporting a thriving AI supplier market. Stakeholders are concerned that the AI market is dominated by a small number of large technology companies, and that government’s approach to procurement is not set up to get the best from all suppliers. The success of its plans to develop an AI sourcing and procurement framework and establish a digital commercial centre of excellence will be critical to ensuring a vibrant AI market in the UK and value for money in the procurement of AI in the public sector.

The new Government Digital Service, which brings together the Central Digital and Data Office (CDDO), the Incubator for AI (i.AI) and the existing Government Digital Service under DSIT leadership offers an opportunity to strengthen leadership and drive change. We urge DSIT to take this opportunity to tackle the significant challenges faced by the public sector to realising the benefits of AI.

Introduction

Artificial intelligence (AI) has the potential to transform public services by automating routine tasks and making public services quicker and more efficient, but it also brings with it risks that must be managed effectively to support adoption and maintain public trust. Achieving large–scale benefits will require not only adoption of new technology but significant change in business practices and will be dependent on government putting in place the right foundations, including access to skills, infrastructure and high–quality data.

A new Government Digital Service was established in January 2025 with the aim of uniting efforts to grasp the opportunities of technology and AI under the Department for Science, Innovation and Technology (DSIT). This involved relocating three functions from the Cabinet Office to DSIT: the Central Digital and Data Office (CDDO), which leads the government’s Digital and Data function; the Incubator for Artificial Intelligence (i.AI), which works across the public sector to identify and pilot AI opportunities; and the Government Digital Service, responsible for building common digital products and services across government.

Successive governments have sought to support the use of AI through a range of policy interventions and strategies. Most recently, the government published A blueprint for modern digital government in January 2025 which included an ambition to harness the power of AI for public good and included a number of priority reforms.

Conclusions and recommendations

1. Out–of–date legacy technology and poor data quality and data–sharing is putting AI adoption in the public sector at risk. AI relies on high quality data to learn, but too often government data is of poor quality and locked away in out–of–date legacy IT systems. Progress tackling these issues has been too slow, and 21 out of the 72 highest–risk legacy systems in government prioritised as part of the 2022–25 digital and data roadmap still lack remediation funding. DSIT has set out its ambitions to address these issues in A blueprint for digital government. These include more focus on addressing legacy technology including identifying remediation funding, and a commitment to reduce the barriers to sharing data (which is important for unlocking access to data to train AI models) by implementing standards, frameworks and tools. However, there are no quick fixes and addressing these issues will take time, resources, prioritisation and sustained focus. We remain concerned at the scale of the challenge, government capacity to tackle the problem and the significant risk this represents to successful adoption of AI across the public sector.

2. Public trust is being jeopardised by slow progress on embedding transparency and establishing robust standards for AI adoption in the public sector. Public confidence that the AI technology used by government is fair, accurate, secure and safe is key to successful adoption. Transparency is fundamental to building that trust but as at January 2025, only 33 records had been published on the government website set up to provide greater transparency on algorithm–assisted decision making in the public sector. Some 67% of government bodies responding to the NAO’s survey said that support from the centre of government in fostering public trust in AI was very important. DSIT acknowledges that it has more to do to communicate effectively with the public and to be ‘demonstrably trustworthy’. Its work to strengthen its digital and data spend controls for high–risk AI and develop technical standards and guidelines must be progressed quickly to build public confidence that AI adoption by public sector bodies is safe and responsible.

3. There are persistent digital skills shortages in the public sector and DSIT’s plans to address the skills gap may not be enough. 70% of government bodies responding to the NAO’s survey identified difficulties in recruiting and retaining staff with AI skills as a barrier to AI adoption. In 2024, around 50% of roles advertised in civil service digital and data campaigns were unfilled. In January 2025, the government’s State of digital government review set out the skills challenge faced by government. These include persistent difficulties recruiting, civil service pay levels that are uncompetitive with the private sector and the need for more technical roles within the profession. Its Blueprint for modern digital government sets out a series of priority reforms including plans to adopt a digital–first operating model, assess the optimum employment models, strengthen digital leadership, and assess competitiveness of the overall package for digital and data staff. However, the Public Accounts Committee has repeatedly raised concerns about the digital skills gap in previous examinations of digital transformation in government and we remain sceptical that these reforms will address the issue where previous attempts have failed.

4. DSIT has no systematic mechanism for bringing together learning from pilots and there are few examples of successful at–scale adoption across government. At the time of the NAO report (March 2024), levels of AI use in government were low, but 70% of surveyed government bodies were piloting or planning AI tools. Examples of pilot activity include use of AI to analyse digital images to identify and classify objects, use of natural language processing to summarise or draft text, and use of AI to assess trends and patterns and monitor live data. More recently i.AI has developed a suite of tools to support civil servants with tasks including minute taking and analysis of consultation responses. However, there is so far little evidence of successful adoption at scale. To grasp the opportunities of AI, government must learn from these pilots, identify the most promising examples, and where appropriate, help drive adoption at scale so the whole of the public sector can take advantage. However, there is no systematic mechanism for bringing together the learning from all the pilot activity across government and disseminating it. We welcome the recent publication of the Evaluation Taskforce’s guidance for evaluating the impact of AI tools, and DSIT’s commitment to piloting an AI Knowledge Hub to bring together good practice and learning in one place. However, without concerted effort and leadership from DSIT, government risks duplication of effort and cost from siloed pilot activity.

5. DSIT and Cabinet Office have a long way to go to strengthen government’s approach to digital procurement to ensure value for money and a thriving AI supplier market. The technology market is rapidly evolving with a shift from upfront capital purchases to subscription–based models, and greater consolidation of the supply chain. Technology suppliers, charities and academics have raised concerns with us that the dominance of a small number of large technology suppliers in the AI market risks stifling competition and innovation. We also heard concerns that government procurement approaches are ill–suited to a dynamic, fast–paced market, increasing risks to value for money from vendor lock–in and the inability to adapt and take advantage of rapid technological development. The government is aware it has a lot to do to adapt its procurement approach to keep up with the pace of change in the sector, and to leverage its significant buying power. It has committed to setting up an AI sourcing and procurement framework, including rapid procurement and national tenders where appropriate. It has also confirmed it will establish a digital commercial centre of excellence to deepen its expertise. The success of these measures will be critical if we are to see a vibrant AI market in the UK and value for money in the procurement of AI for the public sector.

6. Realising the benefits of AI across the public sector will require strong leadership from DSIT. Addressing the skills shortage, tackling the persistent issues with poor data quality and out–of–date technology, and providing the support and guardrails the public sector needs to build public trust and adopt AI safely and responsibly all require a clear strategy and strong leadership. In its report, the NAO raised concerns about unclear accountabilities and limited integration of governance across Cabinet Office—which was primarily responsible for AI adoption in the public sector, through CDDO, i.AI and the Government Digital Service (GDS)—and DSIT, which held responsibility for wider AI policy. We welcome the move of CDDO, GDS and i.AI to DSIT in 2024 to create a digital centre of government. This new Government Digital Service, and the proposals in A blueprint for modern digital government to require all executive teams and boards to include a digital leader by 2026, offer an opportunity to strengthen leadership and governance and support digital transformation. However, the scale of the task should not be underestimated and we are seriously concerned whether DSIT has sufficient leverage to drive change across the public sector. One significant way to do this is to ensure that every department and their agencies have a senior Government Digital Service officer embedded at senior management levels in their organisation and agencies to oversee digital policy and procurement change. Equally the relevant procurement and main boards should include people with excellent digital policy and procurement skills.

1 Addressing barriers to safe and responsible AI adoption

Introduction

1. On the basis of a report by the Comptroller and Auditor General, we took evidence from the Department for Science, Innovation and Technology (DSIT) and the Cabinet Office on the use of artificial intelligence (AI) in government.¹

2. The use of AI has been expanding rapidly across society, particularly with the development of generative AI. It is a very fast–moving field. It has the potential to transform public services by automating routine tasks, making public services quicker and more efficient, and making better use of government data to target support at those that need it. However, AI also brings with it risks (for example, to fairness, safety and privacy) that must be managed effectively to support adoption and maintain public trust. Achieving large–scale benefits will require not only adoption of new technology but significant change in business practices, and will be dependent on government putting in place the right foundations, including access to skills, infrastructure and high–quality data.²

3. At the time of the NAO report in March 2024, responsibility for AI sat across DSIT and the Cabinet Office. However, a new Government Digital Service was established in January 2025 with the aim of uniting efforts to grasp the opportunities of technology and AI under DSIT. This involved relocating three functions from the Cabinet Office to DSIT: the Central Digital and Data Office (CDDO), which leads the government’s Digital and Data function; the Incubator for Artificial Intelligence (i.AI), which works across the public sector to identify and pilot AI opportunities; and the Government Digital Service, responsible for building common digital products and services across government.³

4. Successive governments have sought to support the use of AI through a range of policy interventions and strategies. Most recently, the government published A blueprint for modern digital government in January 2025 which included an ambition to harness the power of AI for public good and included a number of priority reforms.⁴

5. We received a number of written submissions from stakeholders, including academics, technology suppliers, and third–sector bodies, which we have drawn on in this report.⁵ The submissions identified opportunities for AI adoption in the public sector, including increased productivity, better use of data to create insights, and higher quality and more efficient public services. However, concerns and challenges were also raised relating to issues with data quality and data sharing; the impact of outdated legacy technology; the lack of digital and data skills in the public sector; procurement practices that are not fit for purpose; biased, inaccurate and discriminatory outputs; a lack of transparency and data privacy concerns.

Legacy technology, data quality and data sharing

6. AI relies on high quality data to learn, but DSIT told us that too often government data is of poor quality and locked away in out–of–date legacy IT systems.⁶ DSIT defines a legacy system as one that is based on “an end–of–life product, out of support from the supplier, impossible to update, no longer cost–effective, or considered to be otherwise above the acceptable risk threshold” and it estimates that 28% of central government systems met this definition in 2024.⁷ In its Digital and Data Roadmap 2022–25, the government committed to ensuring that all of the 72 highest–risk ‘red–rated’ legacy systems across government would have remediation plans in place. However, DSIT told us that 21 of these still lacked remediation funding.⁸

7. DSIT told us that it was a matter of urgency that the issue of legacy systems in government is addressed, not only to take advantage of the opportunities offered by AI, but also to address other risks including cyber security vulnerabilities. It emphasised prioritising the “systems that have the most valuable data” and “the highest levels of security vulnerability”.⁹ It added that this was both a software and hardware problem.¹⁰ However, it also warned that there is no magic bullet, it will take hard work over a long time to fix, and acknowledged that it needed to get a better grip on the issue.¹¹ Concerning hardware, DSIT emphasised the importance of cloud services as a way for government to move away from maintaining large data centres, although we note in this case the potential trade–off between concentrating usage to achieve greater value for money and diversifying providers to seek greater resilience and security.¹² As part of our inquiry into Government cyber resilience, the Cabinet Office told us that “quite a lot of our legacy systems are operating off a very small number of cloud providers, and until you fix some of the legacy issues, it is very difficult to move off some of the cloud services that are there”.¹³ We will consider this issue further in later scrutiny.

8. A blueprint for modern digital government sets out government’s intention to identify remediation funding for legacy technology. DSIT told us the new Government Digital Service will work with HM Treasury to review all spending review bids for legacy remediation to assess their credibility and ambition, while also tracking the allocated money which in the past had too often been reallocated elsewhere.¹⁴ It also intends to pilot funding on a continuous improvement basis to ensure that IT systems are maintained over the long–term.¹⁵

9. Access to good–quality data was identified as a barrier to implementing AI by 62% of the 87 government bodies responding to the NAO’s survey.¹⁶ We received written evidence expressing a range of views on this topic, illustrating the complexity of the issue and the challenge government has in getting it right. For example, the Reform Think Tank raised concerns about a risk averse attitude to data sharing in government which it felt must change if government is to realise the benefits of AI.¹⁷ SAS Institute also highlighted low levels of data sharing between the public and private sectors as a missed opportunity for innovation.¹⁸ However, we also received evidence from Professor Suresh Renukappa and colleagues that highlighted public concerns about sharing sensitive data with the private sector for the development of AI tools, and stressed the importance of citizens being kept informed of how their data is used.¹⁹

10. We challenged DSIT on what it was doing to address barriers to data sharing in government and it told us a change in culture is needed and government bodies need to think about the collective interest of the public when assessing the risks of data–sharing.²⁰ A blueprint for modern digital government sets out government’s intention to work to reduce the barriers to sharing data by implementing standards, frameworks and tools. As set out in the blueprint, DSIT also told us it plans to create a national data library with the aim of making it easier to find and reuse data across the public sector.²¹ Other measures include requirements in the Data (Use and Access) Bill to support appropriate data sharing in the health and social care sector by standardising the way data is collected, processed, and shared.²²

Public Trust

11. 67% of government bodies responding to the NAO’s survey said that support from the centre of government in fostering public trust in AI was very important.²³ Written evidence we received from a wide range of stakeholders emphasised the vital importance of public trust. For example, the Ada Lovelace Institute told us expanding the use of AI in the public sector without maintaining public trust and confidence could increase the risk of a public backlash or people withdrawing consent for the data to be used.²⁴ The Local Government Association also raised concerns at survey findings that show low levels of public trust in how government uses the personal data of citizens.²⁵ Transparency and openness were emphasised in the written evidence we received as important ways in which government can build trust, through meaningful public engagement and transparent reporting on AI use and evaluation.²⁶

12. The Algorithmic Transparency Recording Standard (ATRS), is intended to support public sector bodies to improve transparency and provide information about the algorithmic tools they are using, but the NAO found it was not widely used.²⁷ We challenged DSIT on this lack of transparency as, at January 2025, only 33 records had been published on the ATRS website. It agreed with us that more records should be published and told us that the pace of publication had increased and it expected that to continue over the coming year.²⁸

13. We asked DSIT for reassurances that there would be sufficient transparency and mechanisms for citizens to challenge AI assisted decisions. It told us that there were provisions in the Data (Use and Access) Bill to allow for redress and challenge in cases of automated decision–making. It also acknowledged that it had more to do to communicate effectively with the public and to be “demonstrably trustworthy” and this was an important area of focus for it in the coming years.²⁹ Actions it told us it is taking include developing technical standards and guidelines, publishing guidance, and strengthening DSIT’s spend controls around the use of high–risk AI. It told us it has a role to play in both providing support and guidance but also in strengthening the “stick” of central assurance and controls given the importance of the issues concerned.³⁰

Digital and data skills and capability

14. 70% of government bodies responding to the NAO’s survey identified difficulties in recruiting and retaining staff with AI skills as a barrier to AI adoption.³¹ In January 2025, the government’s State of digital government review set out the digital and data skills challenge faced by government, reporting difficulties in recruitment (around 50% of roles advertised in civil service digital and data campaigns in 2024 were unfilled), pay levels that were uncompetitive with the private sector, such as a 35% pay gap between public and private sector technical architects (equivalent to £30,000 per year), and the need for more technical roles within the profession.³² DSIT told us that 5.4% of the civil service workforce is in the digital and data profession, which compares unfavourably to the private sector benchmark of 8–12%, depending on the industry. It also told us that the skills mix within the profession was imbalanced, and that it relies too much on outsourcing for the more technical roles.³³ Commenting more broadly on the findings of the review, the DSIT said that “there are many challenges and they are not evenly distributed” across government.

15. Written evidence we received emphasised the importance of addressing this skills gap, raising concerns about the public sector’s ability to take advantage of the opportunities of AI responsibly and ethically without greater expertise. Recommendations included building partnerships between the public and private sectors as well as academia, improving basic AI literacy within the public sector, increasing pay for specialist skills, and building AI competencies into existing professional development frameworks.³⁴

16. The digital and data skills gap in government is long–standing and this committee has raised concerns about this issue in the past.³⁵ We challenged the Cabinet Office and DSIT on how they were tackling the problem and they told us they need to do more to communicate the benefits of working in the public sector, including the opportunity for public service and to have an impact on people’s lives.³⁶ They also told us they wanted to encourage the flow of people and skills between the private and public sectors through greater use of secondments and “tours of duty”.³⁷ Other initiatives set out by the Cabinet Office included a digital excellence programme of training for senior civil servants, the creation of a digital, data and innovation pathway and providing different entry routes into the profession (for example, the digital fast stream and targets for new digital apprenticeships).³⁸

17. DSIT’s A blueprint for modern digital government also sets out a series of priority reforms. These include commitments to assess the optimum employment models and the competitiveness of the overall package for digital and data staff. To strengthen digital leadership, the blueprint also sets out plans to require all public sector organisations to have a digital leader on their executive committee and a digital non–executive director on their board by 2026.³⁹ DSIT told us that chief digital information officers are “not sufficiently represented at the executive committee level”.⁴⁰

2 Supporting widespread adoption in the public sector

Learning from AI pilots and scaling–up

18. At the time of the NAO report in March 2024 AI was not widely used across government, with just over a third (37%) of 87 government bodies surveyed reporting they had deployed AI. In contrast, over two–thirds (70%) were piloting or planning AI use.⁴¹ Examples of pilot activity included use of AI to analyse digital images to identify and classify objects, use of natural language processing to summarise or draft text, and use of AI to assess trends and patterns and monitor live data.⁴² DSIT also told us about a suite of tools developed by the i.AI to support civil servants with tasks including minute taking and analysis of consultation responses.⁴³ Commenting more broadly on digital and technical capability, DSIT praised the Met Office and Ordnance Survey.⁴⁴ It emphasised that local government was “an area where we are particularly keen to move early [ … ] because there is a huge amount of potential there to improve citizens’ lives”.⁴⁵

19. The NAO report set out the importance of knowledge sharing to learn from pilot activity, encourage innovation, promote consistent standards, and avoiding duplication of effort in AI adoption across government. However, it also reported that there was no systematic dissemination of knowledge on AI opportunities and adoption across government.⁴⁶

20. We questioned DSIT on how it is evaluating and sharing learning from AI pilot activity across government to avoid reinventing the wheel and to support AI adoption at scale. It told us that it was taking a range of approaches including developing guidance and identifying good practice case studies, establishing communities of AI practitioners, and adopting an ‘open build’ approach to developing AI tools, publishing their results to make them available to others in the public and private sectors.⁴⁷ A witness from the Department said that “you want to run pilots, work out what works and then scale them”.⁴⁸ It also confirmed that the AI Knowledge Hub that it is piloting is intended to bring together advice and guidance in one place and make it accessible and user–friendly.⁴⁹ On the issue of robust evaluation of AI pilots, the Cabinet Office told us that the Evaluation Taskforce had recently published guidance for evaluating the impact of AI tools. It also told us there is a lot of guidance on evaluating programmes from a financial, commercial and technological perspective, but the next challenge was to ensure that these techniques were understood and people knew where to go for support.⁵⁰

AI procurement

21. In its January 2025 State of digital government review, DSIT reported that government procurement processes had not adapted to the changing nature of the technology market, which has seen a shift from upfront capital purchases to subscription–based models, and greater consolidation of the supply chain.⁵¹ Written evidence we received from technology suppliers, third–sector bodies and academics raised concerns that the dominance of a small number of large technology suppliers in the AI market risks stifling competition and innovation.⁵² Concerns were also raised that government procurement approaches were ill–suited to a dynamic, fast–paced market, increasing risks to value for money from vendor lock–in and the inability to adapt and take advantage of rapid technological development.⁵³

22. We questioned DSIT and the Cabinet Office on how they were strengthening digital procurement in government to support competition and innovation. They told us these issues were an ongoing challenge and that they wanted to find a balance between guiding procurement centrally without stifling opportunities to buy locally from SMEs.⁵⁴ Measures they are taking include setting up a digital commercial centre of expertise to ensure that they build the expertise they need.⁵⁵

23. The State of digital government review reported that government is not doing enough to ensure the public sector benefits from the scale of its buying power.⁵⁶ The Cabinet Office and DSIT told us they are working to tackle this, giving as an example a recent agreement reached with Microsoft to offer discounted products to every part of the public sector.⁵⁷ The Cabinet Office described this as “quite groundbreaking”.⁵⁸ In A blueprint for digital government, DSIT has also committed to developing a sourcing and procurement framework for AI, to include rapid procurement and national tenders where appropriate.⁵⁹

Leadership and governance

24. At the time of the NAO report, responsibility for AI in government was split across the Cabinet Office—which was primarily responsible for AI adoption in the public sector, through CDDO, i.AI and the Government Digital Service (GDS)—and DSIT, which held responsibility for wider AI policy. The NAO concluded that limited integration of governance between these respective programmes increased risks to delivery and to the benefits of a coordinated approach.⁶⁰ The NAO also reported that the then–draft strategy for public sector AI adoption did not set out which department had overall ownership and accountability for it.⁶¹ Following the general election in July 2024, the government announced that CDDO, i.AI and GDS would transfer to DSIT with the aim of uniting efforts on the digital transformation of government in one department.⁶² As part of these changes, DSIT has established a new Government Digital Service to bring together the previously separate functions, which will be led by a second Permanent Secretary–level Government Chief Digital Officer.⁶³

25. We questioned the departments on what benefits we would see from these departmental moves, and the Cabinet Office told us that the aim was, in part, to address the issues of accountability and complexity highlighted by the NAO report which it felt the changes had pretty comprehensively addressed.⁶⁴ DSIT also explained how the changes were intended to bring together and align work on AI policy across the wider economy and AI adoption in the public sector (for example, by supporting the AI supplier market through public procurement, and setting public sector standards that can be adopted by the private sector on a voluntary basis).⁶⁵ It is early days and we will be looking for more evidence that these changes will address the NAO’s concerns around complexity and accountability fully.

26. The Departments told us that the departmental moves had been supported by a new digital inter–ministerial group (IMG) which provides greater focus and links the work with wider cross–government work on public service reform and the government’s missions.⁶⁶ DSIT emphasised that this was about “about having multiple members of the Cabinet really focused on the change agenda for Government on digital services” and “a very significant step up in ministerial engagement on the digital transformation of government”.⁶⁷ The Cabinet Office also observed that the IMG is providing improved strategy, prioritisation and join–up, “saying, ‘let us prioritise and set the strategy so that the sum of the parts delivers the greatest bang for the buck’”.⁶⁸ When we questioned DSIT on how it would influence departments across government and deliver on its role to support AI adoption across the public sector, witnesses told us that clear political leadership and prioritisation was key to driving co–ordination across departments and enabling effective delivery.⁶⁹

27. We challenged DSIT and the Cabinet Office on the scale of the challenge and whether the public sector would be able to grasp the opportunities offered by AI. DSIT told us that it had to be self–critical about digital leadership across government, observing that digital leaders are not well represented at executive level across the public sector and many public sector leaders do not have enough technical expertise or training.⁷⁰ As part of its blueprint for modern digital government, it will require all executive teams and boards of public sector bodies to include a digital leader by 2026.⁷¹ Later this year, DSIT also intends to publish a new Digital and AI roadmap for Government, which will set out the detailed projects underpinning the vision set out in the blueprint.⁷²

Formal minutes

Thursday 20 March 2025

Members present

Sir Geoffrey Clifton-Brown, in the Chair

Mr Clive Betts

Luke Charters

Anna Dixon

Peter Fortune

Rachel Gilmour

Sarah Hall

Chris Kane

Sarah Olney

Use of AI in Government

Draft Report (Use of AI in Government), proposed by the Chair, brought up and read.

Ordered, That the draft Report be read a second time, paragraph by paragraph.

Paragraphs 1 to 27 read and agreed to.

Summary agreed to.

Introduction agreed to.

Conclusions and recommendations agreed to.

Resolved, That the Report be the Eighteenth Report of the Committee to the House.

Ordered, That the Chair make the Report to the House.

Ordered, That embargoed copies of the Report be made available (Standing Order No. 134).

Adjournment

Adjourned till Thursday 27 March at 9.30 a.m.

Witnesses

The following witnesses gave evidence. Transcripts can be viewed on the inquiry publications page of the Committee’s website.

Thursday 30 January 2025

Sarah Munby, Permanent Secretary, Department for Science, Innovation and Technology; Cat Little, Permanent Secretary, Cabinet Office; David Knott, Chief Technology Officer for the UK Government, Central Digital and Data Office (CDDO), Cabinet Office Q1-58

Published written evidence

The following written evidence was received and can be viewed on the inquiry publications page of the Committee’s website.

UAIG numbers are generated by the evidence processing system and so may not be complete.

1 Aradhya, Professor Manjunath (Professor, JSS Science and Technology University, Mysuru, India); Bhuttay, Mr Ryan (Student, University of Wolverhampton); Pillai, Professor Prashant (Professor of Cyber Security, University of Wolverhampton); Renukappa, Professor Suresh (Professor of Sustainable Smart Innovation, University of Wolverhampton); Subbarao, Mr Chandrashekar (Research Student, University of Wolverhampton); Suresh, Professor Subashini (Professor of Knowledge Management, University of Wolverhampton); and Veenith, Professor Tonny (Clinical Director of Research, The Royal Wolverhampton NHS Trust, Wolverhampton) UAIG0022

2 AI & Partners UAIG0001

3 Ada Lovelace Institute UAIG0033

4 Axon UAIG0004

5 Bates, Professor Jo (Professor of Data and Society, University of Sheffield); Kennedy, Professor Helen (Professor of Digital Society, University of Sheffield); Oman, Dr. Susan (Senior Lecturer in Data, AI & Society, University of Sheffield); Perea, Dr Itzelle Medina (Lecturer in Information Systems, University of Sheffield); and Pinney, Lulu (Student, University of Sheffield) UAIG0008

6 Boardwave UAIG0014

7 Brand, Ms Jessica (PhD student and research assistant in AI Justice, Bristol University and Goldsmiths, University of London); Dencik, Professor Lina (Professor and University Research Leader in AI Justice, Goldsmiths, University of London); Hintz, Dr Arne (Reader, Cardiff University); and Redden, Dr Joanna (Associate Professor, Western University Ontario) UAIG0019

8 Brown, Professor Alan (Professor, University of Exeter) UAIG0031

9 Child Poverty Action Group UAIG0027

10 City St George, University of London UAIG0026

11 Cook, Mr Nigel D (Retired–P3M Expert) UAIG0025

12 Ezzeddine, Dr. Yasmine (Lecturer in Policing and PhD Researcher, University of Staffordshire) UAIG0011

13 Fetzer, Professor Thiemo (Professor of Economics , University of Warwick & University of Bonn) UAIG0006

14 Gater, Mr Giles (Senior Lecturer and Course Leader, University of Staffordshire); Newberry, Mr Adam (Senior Lecturer and Course Leader, University of Staffordshire); and Northfield, Mr Dean (Senior Lecturer , University of Staffordshire) UAIG0020

15 Glitch UAIG0030

16 Hawkins, Rebecca UAIG0037

17 Hessami, Dr Ali (Director, Vega Systems) UAIG0012

18 Johnson, Dr Princy (Reader in Sensors and Data, Liverpool John Moores University) UAIG0023

19 Local Government Association UAIG0036

20 Newman-Griffis, Dr Denis (Senior Lecturer and AI for Health Lead, University of Sheffield, Centre for Machine Intelligence) UAIG0018

21 Open Cloud Coalition UAIG0034

22 Osborne, Joshua (Student, University of Sheffield) UAIG0010

23 Padget, Dr Julian (Reader in Artificial Intelligence, University of Bath) UAIG0021

24 Public Law Project UAIG0024

25 Pupils 2 Parliament UAIG0009

26 QA UAIG0029

27 Reform think tank UAIG0028

28 SAS Institute UAIG0035

29 Specht, Dr Doug (Head of School of Media and Communication, University of Westminster) UAIG0007

30 Krupiy, Dr Tetyana (lecturer, Newcastle University) UAIG0002

31 The Centre for Public Policy and Administration, Queen’s University Belfast UAIG0003

32 The Institute of Science and Technology UAIG0015

33 The Institution of Engineering and Technology UAIG0013

34 Thorney Isle Research UAIG0016

35 Vaduva, Dr Alina Maria (Senior Lecturer, University of East London) UAIG0005

36 Waller, Dr Alexander (Visiting Professor, American University of Sovereign Nations) UAIG0017

37 Wooldridge, Professor Michael (Professor, University of Oxford) UAIG0032

List of Reports from the Committee during the current Parliament

All publications from the Committee are available on the publications page of the Committee’s website.

Session 2024–25

Number	Title	Reference
17th	The Remediation of Dangerous Cladding	HC 362
16th	Whole of Government Accounts 2022-23	HC 367
15th	Prison estate capacity	HC 366
14th	Public charge points for electric vehicles	HC 512
13th	Improving educational outcomes for disadvantaged children	HC 365
12th	Crown Court backlogs	HC 348
11th	Excess votes 2023-24	HC 719
10th	HS2: Update following the Northern leg cancellation	HC 357
9th	Tax evasion in the retail sector	HC 355
8th	Carbon Capture, Usage and Storage	HC 351
7th	Asylum accommodation: Home Office acquisition of former HMP Northeye	HC 361
6th	DWP Customer Service and Accounts 2023-24	HC 354
5th	NHS financial sustainability	HC 350
4th	Tackling homelessness	HC 352
3rd	HMRC Customer Service and Accounts	HC 347
2nd	Condition and maintenance of Local Roads in England	HC 349
1st	Support for children and young people with special educational needs	HC 353

---

Footnotes

1 C&AG’s Report, Use of artificial intelligence in government, Session 2023–24, HC 612, 15 March 2024

2 C&AG’s Report, paras 1, 1.3, and 3.16

3 Qq 1–2; C&AG’s Report, para 1.4 and Figure 2

4 C&AG’s Report, para 2 and Figure 1; DSIT, A blueprint for modern digital government, January 2025

5 Committee of Public Accounts, Use of AI in Government- Written evidence

6 Qq 18, 22

7 DSIT, State of digital government review, January 2025, pp 5, 17

8 Q 44; CDDO, Transforming for a digital future: 2022 to 2025 roadmap for digital and data, updated September 2023

9 Q 50

10 Q 51

11 Qq 46, 50

12 Q 51

13 HC 643 Q80

14 Q 46

15 Q 47

16 C&AG’s Report, para 3.13

17 UAIG0028

18 UAIG0035

19 UAIG0022

20 Q 38

21 Q 37; DSIT, A blueprint for modern digital government, January 2025, p 20

22 Q 39

23 C&AG’s Report, figure 10

24 UAIG0012, UAIG0017, UAIG0020, UAIG0032, UAIG0033, UAIG0035

25 UAIG0036

26 UAIG0011, UAIG0027

27 C&AG’s Report, para 3.20

28 Qq 10–11

29 Q 57

30 Q 20

31 C&AG’s Report, figure 9

32 DSIT, State of digital government review, January 2025, pp. 29–31

33 Q 25

34 UAIG0006, UAIG0010, UAIG0011, UAIG0018, UAIG0026, UAIG0028, UAIG0029

35 Committee of Public Accounts, Digital transformation in government: addressing the barriers to efficiency, Seventieth Report of Session 2022–23, HC 1229, 13 September 2023; Committee of Public Accounts, Challenges in implementing digital change, Thirtieth Report of Session 2021–22, HC 637, 10 December 2021

36 Q 26

37 Q 26

38 Q 25

39 DSIT, A blueprint for modern digital government, January 2025, p 24

40 Q 27

41 C&AG’s Report, para 12

42 C&AG’s Report, para 12 and Figure 7

43 Qq 17, 24

44 Q 3

45 Q 3

46 C&AG’s Report, para 3.5

47 Q 21

48 Q 15

49 Q 41

50 Q 29

51 DSIT, State of digital government review, January 2025, pp 34–35

52 UAIG0014, UAIG0028, UAIG0033, UAIG0034, UAIG0036

53 UAIG0031, UAIG0034

54 Qq 8, 22

55 Q 9

56 DSIT, State of digital government review, January 2025, p 35

57 Q 8

58 Q 8

59 DSIT, A blueprint for modern digital government, January 2025, p 20

60 C&AG’s Report, para 10 and Figure 2

61 C&AG’s Report, para 9

62 DSIT press release, DSIT bolstered to better serve the British public through science and technology, 8 July 2024

63 Qq 1–2

64 Q 2

65 Qq 1–2, 4

66 Q 7

67 Q 7

68 Q 7

69 Q 49

70 Q 23

71 Q 27; DSIT, A blueprint for modern digital government, January 2025, p 24

72 Q 32