Britain's AI adoption gap puts £47bn boost at risk

UK risks a "long, slow death" without faster AI adoption - what government leaders must do next

Britain's research strength won't save its economy if businesses keep delaying AI adoption. Matt Clifford - the prime minister's former AI adviser and chair of the Advanced Research and Invention Agency (Aria) - warns the UK is showing "dangerous complacency" and is "probably the worst adopter of new technology in the developed world."

The threat is immediate for sectors where the UK has an edge: life sciences, financial services and media. If adoption stays slow, those advantages fade and market share shifts abroad.

The adoption gap by the numbers

A government review estimates AI could add £47 billion a year to the UK economy over the next decade, lifting productivity by 1.5% annually with broad, safe deployment. Yet adoption remains uneven: only 8% of manufacturers have deployed AI or machine learning; many creative firms are too small to invest; and life sciences progress is held back by poor access to high-quality health data.

Main barriers: high upfront costs, a lack of workforce skills, weak information on proven use-cases, and regulatory uncertainty. Government has set out a pro-innovation approach to AI oversight, but execution and clarity now matter more than intent. See the government's framework here: AI regulation: a pro-innovation approach.

Infrastructure is now a policy bottleneck

Power constraints are becoming strategic. In a survey of FTSE 250 executives for the Energy Networks Association, nearly nine in ten said grid upgrades are essential to growth in high-demand industries like AI. Eight in ten warned the UK cannot compete globally without reliable, high-capacity power for data centres.

Without faster grid connections and planning reform, data centres and advanced compute stay stuck in the queue. That stalls private investment and slows adoption across every sector. Learn more about the sector's position: Energy Networks Association.

What government can do in the next 12 months

• Make the public sector the lead adopter. Pilot AI use-cases across major departments with clear guardrails on safety, security and accountability. Publish results and move successful pilots into procurement frameworks.
• Turn policy into clarity. Issue sector-specific guidance aligned with the government's AI framework. Expand regulatory sandboxes and set 90-day approval service levels for low-risk, high-benefit use-cases.
• Cut upfront cost for SMEs. Co-fund adoption pilots (50:50) for priority sectors. Extend full expensing to AI software and compute. Fast-track R&D tax relief claims for AI projects that pass predefined criteria.
• Scale skills, fast. Fund modular micro-credentials and apprenticeships for data, AI and automation roles. Tie funding to adoption outcomes, not course enrollment. For practical options by role, see AI courses by job.
• Unlock health and public data safely. Expand trusted research environments, standardise data access agreements, and invest in data quality and interoperability to accelerate life sciences and clinical innovation.
• Provide shared compute and tools. Stand up regional AI adoption hubs with pooled GPUs, secure MLOps platforms and expert support. Offer adoption vouchers that cover integration, not just experimentation.
• Fix grid and planning. Publish a transparent grid upgrade roadmap with milestones. Prioritise connections for data centres and research facilities. Designate strategic sites as nationally significant infrastructure to streamline approvals.
• Publish reference use-cases. In finance and media, release validated patterns (e.g., risk modelling, fraud detection, rights management, localisation) with model prompts, evaluation criteria and ROI benchmarks.

What success looks like

By 2027: AI in production at over half of medium and large firms in priority sectors; connection times for critical data centres cut materially; trusted research environments expanded with measurable throughput; and a pipeline of certified AI talent exceeding departmental demand.

Most of all, the public sector proves adoption is safe, effective and accountable - giving business the confidence to scale.

Immediate next steps for policymakers

• Form a cross-department AI adoption unit with delivery authority and a 90-day plan for top 20 public-sector use-cases.
• Update procurement rules to favour solutions with measurable AI productivity gains and strong auditability.
• Launch three sector sandboxes (financial services, life sciences, media) with clear pathways from pilot to production.
• Issue a grid acceleration statement with connection targets, planning reforms and a data centre siting framework.
• Publish a national AI skills catalogue mapped to roles and pay premiums to drive uptake across regions.

The message is clear: research leadership means little without adoption. Move decisions from papers into pilots, from pilots into procurement, and from procurement into measurable productivity. The clock is ticking - and rivals aren't waiting.