Deloitte Centre for Financial Services – October 2025

AI is at an inflection point. Many banks are under pressure to scale and move beyond pilots, but 2026 will likely demand robust, enterprise-level strategies, governance, and a disciplined approach to return on investment.

Agentic AI offers breakthrough potential, but only if supported by AI-ready data—accurate, timely, broad, and securely governed. Without this data foundation, even the most ambitious models could stall.

2026 could be pivotal for banks as they aspire to become fully AI-powered. Currently, AI implementations are often throttled by brittle and fragmented data foundations, outdated legacy systems, and internal resistance to change. Despite large and growing AI budgets over the past two years, most banks have only achieved sporadic tactical wins rather than true strategic transformation.

AI won’t deliver without the right foundations. Success with AI implementations will likely be limited unless banks modernise core infrastructure and bolster data architecture and governance. Many banks have made significant progress in modernising their data infrastructure, however, without an AI-grade data infrastructure, AI models may underperform and AI pilots stall, or fall short of regulatory standards. As AI moves from pilots to enterprise scale, building a more resilient and future-proof data architecture will become be critically urgent.

AI-ready data should be reliable enough that errors and drift do not erode model performance, timely enough to match the cadence of decisions, broad enough to capture signals across different formats, and governed tightly enough to meet compliance and security demands.

These data attributes are mutually reinforcing. For instance, latency without trust can deliver bad data faster; breadth without context can add noise rather than insight; and governance without usability can starve innovation. Strengthening one dimension often exposes weaknesses in the others. The challenge for banks is often not picking which aspect to optimise but advancing all four in concert so the data foundation keeps pace with the scale, speed, and sophistication of modern AI.

Data readiness is likely a multi-year journey. Banks that align executive sponsorship, budgets, and realistic timelines to make data ready for AI are more likely to realise AI’s full potential.