Level-up AI Outcomes
Accelerate value realisation – not risk
75% of UK financial services firms are already using AI with a further 10% planning to adopt within 3 years. AI agents and assistants are moving fast from experimentation to production. In regulated environments, the question becomes: can you evidence control, accountability and compliance at scale?
AI is accelerating value and exposure in UK Financial Services & Insurance.
Almost half of organisations report a negative consequence from GenAI
AI doesn’t create most governance issues – it reveals them instantly. Surfacing whatever your organisation can already access, including over-retained content, inconsistently applied sensitivity labels and orphaned repositories
Gartner predicted…
Gartner predicted 30% of generative AI projects would be abandoned after a “Proof of Concept” by the end of 2025 due to poor data quality, inadequate risk controls, escalating costs or unclear value
Of data is typically redundant, obsolete and trivial
Data Discovery and Information Governance projects have repeatedly shown large proportions of corporate information to be ROT. This undermines AI initiatives and increases risk by reducing the quality of AI outputs.
Why AI adoption becomes a risk issue
AI expands the exposure surface
Generative AI can introduce risks spanning over sharing, accuracy failures, cyber risk, IP leakage and reputational damage – and those risks are already being experienced by many organisations.
Configured Governance ≠ Operationalised Governance
Many firms have governance tooling and policies in place, but controls often don’t run end‑to‑end across legacy content and hybrid estates. AI quickly exposes where classification, retention, and access controls are inconsistent.
Regulation is moving from principles to demonstrable obligations
The EU AI Act establishes a risk-based framework and introduces obligations for higher‑risk AI systems, plus requirements for general-purpose AI (documentation, transparency and more, depending on systemic risk).
Three foundations for good AI governance
In the UK, the Financial Conduct Authority (FCA) emphasises safe and responsible adoption with scrutiny on the systems and processes firms have in place. This increases the need for defensible governance.
A practical approach comes down to three foundations:
- Policy rooted in regulatory reality and business strategy
- Risk-based classification of AI use cases – aligned to the EU AI Act categories
- An AI Register to provide visibility, accountability and evidence of control
Microsoft & LinkedIn 2024 Work Trend Index
Pathways to level-up AI outcomes
Pros: Executive accountability; clear guardrails; audit defensibility.
Cons: Risks becoming “paper governance” if not operationalised.
Pros: Proportionate controls; faster approvals; regulatory alignment.
Cons: Requires ongoing maintenance and a clear intake workflow.
Pros: Single source of truth; reporting; accountability.
Cons: Degrades if a manual process; must connect to operational workflows.
Pros: Reduces AI and Copilot exposure risk; improves defensibility and resilience.
Cons: Legacy remediation requires effort and sustained ownership.
Pros: Early detection of risky usage; supports continuous compliance.
Cons: Needs risk-based tuning and enablement to avoid workaround behaviour.
Learn more – access our insights
The below assets specifically relate to AI and Information Governance within the UK FSI context. For more of our insights click the link.
Blog: AI Governance – a practical approach to getting started
Govern AI with confidence. This blog outlines a practical approach to begin your AI governance journey – policies, risk-based classification, and an AI register, to reduce compliance and security risk, build trust, and scale AI safely across the organisation.
White Paper: AI Governance: A blueprint for turning risk into trust and strategic advantage
Most organisations are rolling out AI use without sufficient governance and control. That gap creates avoidable risk (bad decisions, privacy leakage, reputational damage) and erodes trust with customers, employees, and the public at large. A practical AI governance approach comes down to three things and this whitepaper will explore them to help you get started.
Case Study: Informotion Empowers Leading European Bank to Achieve GDPR Compliance
A major European bank, serving millions of customers and operating over 2,000 production systems, faced the daunting challenge of ensuring data privacy and compliance with the General Data Protection Regulation (GDPR).
Webinars & Events
Thursday 30th April 2026, 12:30pm GMT – Save the date for our up-coming webinar on how to get started on your AI Governance journey. More details to come soon.
Reduce AI risk without slowing innovation
Reach out and request an AI governance risk briefing.
Need Support?
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