AI agents are already common across UK financial services, but firms are largely keeping them away from consequential decisions. The Bank of England and Financial Conduct Authority’s 2026 survey says 80% of respondents have agents in production and a further 14% plan to deploy them within 12 months. Most current uses support decisions rather than execute them: only 2% of firms reported agents with broad autonomy.

The wider adoption picture is equally important. Eighty-six per cent of respondents said they use AI, up from 75% in 2024. Third-party systems represented 39% of reported AI implementations and 53% of high-materiality systems. The survey also found concentration in infrastructure providers: the top three cloud and compute providers accounted for 79% of reported relationships. These are survey responses, not a census of the sector, and the regulators caution that some detailed questions had smaller response bases.

For governance teams, the practical message is that human oversight has not disappeared; it has moved into a more complex supply chain. Firms now need evidence that agent recommendations can be challenged, vendor changes can be tested and rolled back, and critical dependencies can be understood before automation expands into approvals, payments or trading.


AI for Leaders: Governance, Risk & Why Strategy Must Come First
Is your organisation adopting AI without a defined strategy or governance? If so, you're not alone — and the consequences range from data breaches to reputational damage to safeguarding failures. AI is a strategic risk, not a silver bullet, and definitely not a magic data fairy. Without clear ownership, rules, and accountability, AI can proliferate across your organisation in ways that cost more — in time, money, and trust — than not using it at all. Speakers: Craig Clark, Director at Clark & Company Information Governance Services, and Stewart Tinson, Project Director, AI-360. You'll learn: Why "understand your why" must precede any AI rollout Which risks senior leaders most commonly overlook — from data leakage to safeguarding How to measure AI value through outcomes, not adoption metrics Why accountability for AI governance sits with the C-suite, not IT or compliance Key topics: AI strategy • Risk appetite • Data protection • Bias & fairness • Safeguarding • AI literacy • Governance models • Return on investment • Executive accountability • AI use case tiering For CISOs, CIOs, and senior leaders who need to govern AI responsibly — before it governs them.
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