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Explore the key findings below or download the full report.
Banks are generating more ideas for AI use cases than ever before. Working out how to scale up use cases, deliver value, and orchestrate the AI activities across the company has, in many banks, become the mandate for newly established group AI leadership teams. These teams are focused on building capabilities across five priority areas.
The exact definition of a use case can vary between banks. However, within a bank there should be consistent and standardised classification and terminology. Depending on how a bank itemises its use cases, it may report having tens, hundreds, or thousands across the organisation.
Shareholders and senior leaders are increasingly demanding tangible outcomes from AI investments. Banks need a common methodology and process to measure, track and report on the value created by their existing (and future) AI use cases.
The best use cases are intimately tied to business problems, and ChatGPT has led to the proliferation of ideas of AI use cases like never before. Leading AI teams are investing in initiatives to fuel and harness this bank-wide AI ideation.
There may be few limits to the opportunities offered by AI - but delivery resources are always constrained. Banks need a robust, standardised and aligned process to prioritise AI use cases for delivery. This has to cover ROI, operational capacity, risk and governance issues.
Delivering value from AI at scale requires foundations to be well laid. Banks are focused on building foundational AI tools; delivering long-term data strategy; establishing external partnerships; and building fit-for-purpose model validation frameworks.
