AI in the Public Sector: Challenges, Opportunities, and the Path Ahead

The UK government has bold ambitions to become an AI superpower, leveraging artificial intelligence to enhance public services, cut costs, and boost productivity. However, a recent report from the Public Accounts Committee (PAC) highlights significant barriers; outdated IT systems, poor data quality, skills shortages, and transparency issues, that threaten to derail these plans.

For tech professionals in the public sector, this report serves as both a warning and a roadmap. Let’s break down the key challenges and what needs to be done to overcome them.

1. Legacy IT Systems
2. Data Quality
3. Transparency & Public Trust
4. Digital Skills
5. Leadership Challenges
   
   

1. Legacy IT Systems: The Achilles’ Heel of AI Adoption

One of the biggest roadblocks to AI adoption in government is outdated IT infrastructure. The PAC report reveals that 28% of central government systems in 2024 were classified as "legacy", meaning they are unsupported, impossible to update, and often incompatible with modern AI solutions.

"An estimated 28% of central government systems met this definition in 2024. Approx. a third of Government’s 72 highest-risk legacy systems still lack remediation funding." – Public Accounts Committee

Without urgent investment in modernisation, AI deployment will remain fragmented and inefficient. The PAC recommends:

• Prioritising funding for high-risk legacy system remediation.

• Setting a six-month deadline for DSIT to outline a clear funding strategy.

For public sector tech leaders, this means advocating for cloud migration, interoperability standards, and modular upgrades to ensure AI can be deployed at scale.

2. Data Quality: Garbage In, Garbage Out

AI models are only as good as the data they’re trained on. Unfortunately, government data is often siloed, inconsistent, or of poor quality, making AI adoption risky.

"For AI to be used well, it needs high-quality data on which to learn. The PAC is warning that too often Government data are of poor quality, and often locked away in out-of-date IT systems." – Evening Standard

Key actions needed:

• Data governance frameworks to standardise collection and storage.

• Open data initiatives to improve accessibility and usability.

• Stronger collaboration between departments to break down silos.

Tech professionals must push for data cleansing, integration tools, and robust metadata practices to enable reliable AI applications.

3. Transparency & Public Trust: The Algorithmic Accountability Gap

Public trust in AI is fragile, and transparency is non-negotiable. Yet, the government has been slow to disclose how algorithms influence decision-making.

"By January 2025, only a relative handful of records had been published on a Government website set up to provide greater transparency on algorithm-assisted decision-making." – PAC Report

Recommendations to rebuild trust:

• Mandate algorithmic transparency logs for all AI-assisted decisions.

• Establish independent audits to assess bias and fairness.

• Engage the public in discussions about AI ethics and use cases.

For digital leaders, this means embedding explainable AI (XAI) principles and ensuring compliance with emerging regulations.

4. Digital Skills Shortages: A Talent Crisis

The public sector is struggling to attract and retain AI talent, with 70% of departments reporting difficulties in hiring skilled professionals.

"Persistent digital skills shortages in the public sector [are] in part because of civil service pay levels that are uncompetitive with the private sector." – The Guardian

Solutions to bridge the gap:

• Upskill existing staff through AI training programmes.

• Expand apprenticeship schemes (e.g., the 2,000 new tech apprentices pledged by Starmer).

• Improve pay and benefits to compete with private sector roles.

Tech leaders must champion internal upskilling, partnerships with academia, and flexible hiring models to build a sustainable talent pipeline.

5. DSIT’s Leadership Challenge: Who Drives Change?

The Department for Science, Innovation and Technology (DSIT) is tasked with overseeing AI adoption, but the PAC questions whether it has sufficient authority to enforce change across Whitehall.

"I have serious concerns that DSIT does not have the authority over the rest of Government to bring about the scale and pace of change that’s needed." – Sir Geoffrey Clifton-Brown, PAC Chair

Proposed fix:

• Embed senior digital officers at the board level of every department.

• Strengthen cross-government coordination on AI strategy.

• Engage in cross-government collaboration at DigiGov Expo
  
  

For digital transformation to succeed, centralised leadership with real enforcement power is essential. All government departments working together towards the same goals and sharing solutions to common challenges is a key part of the leading industry event DigiGov 2025.

Conclusion: A Call to Action for Public Sector Tech Leaders

The PAC report makes it clear: AI has transformative potential, but only if the government addresses legacy tech, data quality, transparency, and skills gaps.

For tech professionals in the public sector, the path forward involves:

✅ Advocating for IT modernisation funding
✅ Championing data quality and governance
✅ Ensuring transparency in AI deployments
✅ Closing the digital skills gap
✅ Pushing for stronger leadership in AI strategy
✅ Join the tech sector in attending DigiGov in September

As Sir Keir Starmer pushes for AI-driven efficiency, the public sector must balance innovation with accountability.

To join the conversation on how AI should be rolled out in the public sector, and real-world case studies from departments and councils, register to attend DigiGov Expo today.