2026 Gartner AI Use-Case Assessment for Banking

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2026 Gartner® AI Use-Case Assessment for Banking

Banks are investing in AI, but are they prioritizing the use cases most likely to deliver value?

Access your complimentary licensed copy of the 2026 Gartner® AI Use-Case Assessment for Banking to see where Gartner identifies likely wins, calculated risks and marginal gains across credit underwriting, IDP, enterprise knowledge, risk management, customer service and other areas of banking.

Bank AI investment needs a clearer hierarchy

Banking leaders are under pressure to turn AI investment into measurable improvements in revenue, efficiency, risk management and customer experience.

But not every AI use case offers the same potential return. Technical readiness, internal adoption, regulatory requirements and integration complexity can make the difference between a scalable capability and another isolated pilot.

The 2026 Gartner AI Use-Case Assessment for Banking provides a practical framework for comparing prominent use cases and deciding where investment may be most likely to deliver value.

What you’ll learn from the assessment

How 20 prominent banking AI use cases compare

Review use cases across two important dimensions: potential business value and the feasibility of implementation.

How to distinguish likely wins from higher-risk investments

See how Gartner groups use cases into likely wins, calculated risks and marginal gains to support more informed prioritisation.

Where AI can support credit operations today

Explore how AI can assist with document ingestion, extraction, financial spreading, data validation and practitioner support while final decisions remain governed.

Why IDP is a foundational capability

Understand how document classification, extraction and validation can enable broader use cases across underwriting, onboarding and lending operations.

How enterprise knowledge assistants can improve operations

Examine the role of AI in helping employees find information, interpret policies, navigate complex documentation and answer operational questions.

What determines whether AI can scale successfully

Consider how integration complexity, internal readiness, model governance, explainability, bias mitigation and human oversight affect implementation.