AI adoption in the UK Public Sector: readiness, barriers and opportunities

Artificial Intelligence (AI) is increasingly recognised as a driving force for growth across the UK public sector. From streamlining internal operations to enhancing citizen-facing services, AI offers the potential to deliver measurable efficiency gains, improve compliance, and support digital transformation plans. However, adoption varies widely, influenced by organisational preparedness, financial limitations, and challenges unique to each sector. 

Current state of AI investment 

The research reveals a sector in transition. While interest in AI is high, actual investment varies significantly. 57% of organisations have already invested in AI, with NHS Trusts and Local Government leading the way. Of those invested: 

• 18% report minimal investment (e.g. pilots or training) 
• 30% report moderate investment (e.g. multiple pilots or dedicated teams) 
• 10% report significant investment (e.g. integrated AI strategy across departments) 

Sector breakdown 

NHS Trusts show the highest levels of investment, with over 70% reporting moderate or significant adoption. This reflects the sector’s focus on operational efficiency, patient flow optimisation, and predictive analytics. Local Government demonstrates strong momentum, particularly in areas such as revenues and benefits, planning, and customer services. Housing Associations are more cautious, with many still in early-stage pilots or planning phases. Education lags behind, with limited investment and lower readiness scores, often due to fragmented systems and constrained budgets. 

Investment intent 

32% of organisations are planning to invest in AI, with most targeting a 12-month timeline. This suggests a growing pipeline of adoption, particularly among mid-sized authorities and trusts seeking to modernise legacy systems. 

Current uses 

According to our data, public sector organisations are using AI for: 

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Organisational interest and strategic importance 

Interest in AI is widespread and growing: 

• 75% of respondents are interested in exploring AI, with NHS Trusts and Education showing the highest levels of enthusiasm. 
• 76% believe AI will be important for their organisation in the next 3–5 years, with nearly half rating it as “very important” or “critical”. 
• 68% of public sector respondents say their workplace is embracing new technology “somewhat well”. 
• 26% say they are “excelling”. 
• 65% believe their workplace would benefit from AI tools, with 16% saying “definitely”. 

This reflects a shift in perception. AI is no longer seen as experimental or niche, but as a core component of future service delivery. For many, it is tied to broader transformation goals, including automation, digital inclusion, and financial resilience. As well as a strong appetite for innovation, tempered by the realities of budget cycles, procurement processes, and legacy infrastructure. 

Strategic drivers by sector 

1. Local Government

   AI is seen as a tool to deliver “more with less” amid budget pressures. Councils are exploring AI to reduce manual processing in finance, automate FOI responses, and support fraud detection. 

2. Housing Associations

   AI is viewed as a way to improve tenant satisfaction and reduce operational costs. Predictive analytics for rent arrears and automated scheduling for repairs are emerging use cases. 

3. NHS Trusts

   AI is critical to managing demand and improving clinical outcomes. Trusts are investing in AI to support diagnostics, reduce waiting times, and optimise resource allocation. 

4. Education

   AI is being considered to improve administrative efficiency and student experience. However, concerns around data governance and funding are slowing progress. 

Integration and readiness 

Among those who have invested in AI: 

• 41% report full integration into organisational processes 
• 54% report partial integration 
• 5% are still in early stages 

This suggests that while adoption is underway, maturity levels vary. Full integration is more common in NHS Trusts and larger local authorities, where dedicated digital teams and transformation programmes are in place. 

This indicates a growing familiarity with AI tools, particularly among digital-forward teams in finance and IT. 

Sector adoption challenges 

1. Councils are reporting delays in AI implementation due to legacy finance systems lacking integration capability. 
2. Housing associations cite a lack of internal data governance as a barrier to deploying predictive analytics. 
3. NHS Trusts highlighted the importance of clinical engagement and ethical oversight in AI deployment, particularly for diagnostic tools. 
4. An NHS Trust faced internal resistance from clinical staff concerned about AI replacing human judgement. 

Objectives driving AI adoption 

Organisations are adopting AI with clear, outcome-focused goals: 

Sector-specific priorities 

• Local Government

  Efficiency and compliance dominate, reflecting budget pressures and scrutiny from audit committees. AI is being used to automate payment reconciliation and improve fraud detection. 

• Housing Associations

  Focus on resident experience and cost savings, particularly in repairs and customer service. AI chatbots are being trialled to handle common tenant queries. 

• NHS Trusts

  Innovation and risk reduction are key, with AI used in diagnostics, scheduling, and resource planning. One Trust is using AI to identify patients at risk of readmission, enabling proactive care. 

• Education

  Interest in automation and data-driven decision-making but limited by infrastructure and funding. AI is being explored to support student retention and automate bursary processing. 

Barriers to Adoption 

Despite strong interest, several barriers are slowing progress: 

Public Sector Concerns 

According to our data, public sector concerns around AI include: 

These concerns reflect the sector’s heightened sensitivity to risk, compliance, and public trust, particularly in areas handling personal or financial data. 

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Sector Spotlights 

AI adoption in the public sector looks different across key industries, with varying levels of investment, readiness, and use cases. The table below highlights how local government, housing associations, NHS Trusts, and education providers are approaching AI - what drives their priorities, the challenges they face, and the opportunities emerging in each sector. 

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Strategic recommendations 

To accelerate AI adoption and maximise impact, public sector organisations should consider: 

1. Phased implementation 

• Start with low-risk, high-impact pilots 
• Build internal confidence and capability 
• Avoid disruption to business-as-usual 

2. Invest-to-save business cases 

• Link AI projects to measurable outcomes (e.g. staff time saved, error reduction) 
• Use sector benchmarks and peer examples to support ROI 

3. Skills development 

• Upskill internal teams through training and partnerships 
• Consider shared service models or regional collaboration 

4. Governance and ethics 

• Establish clear frameworks for data use, transparency, and accountability 
• Engage stakeholders early to build trust 

5. Procurement and integration planning 

• Align AI projects with existing systems and contracts 
• Use flexible procurement frameworks to avoid lock-in 

In conclusion

AI presents a significant opportunity for the UK public sector. Not just to modernise operations, but to deliver better outcomes for residents. While investment and readiness vary, the strategic intent is clear. By addressing barriers and aligning adoption with organisational goals, public sector leaders can unlock the full potential of AI. 

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Methodology 

This report draws on two complementary sources of quantitative research to provide a robust, sector-wide view of AI adoption in the UK public sector. 

1. Tank PR & Censuswide Research, Commissioned by: Tank PR for Access PaySuite. Sample size: 252 respondents. Method: Online survey. Scope: The survey included a mix of closed and multiple-choice questions, covering: Current AI investment levels, organisational readiness, perceived importance, barriers to adoption, strategic objectives. Analysis: Responses were segmented by sector, organisation size, and role type to identify trends and sector-specific insights. 

2. Access Evo Public Sector Dataset. Commissioned by: The Access Group. Sample size: 183 respondents. Method: Online survey. Scope: This dataset provides granular insights into: AI usage and familiarity, perceived impact and benefits, specific applications (e.g. ChatGPT, Copilot, chatbots), workplace attitudes toward technology adoption, concerns around AI development (e.g. data security, job replacement, reliability). Analysis: Responses were filtered to isolate public sector-specific trends and benchmark attitudes against broader adoption patterns.