The news: Banks are deploying AI in three distinct ways that each serve different business functions and require different forms of oversight, per Ncino’s new AI in Banking Benchmark.

Zoom in: Eighty-four percent of banks have adopted AI at the enterprise level. But among them, 91% have made generative deployments, 87% predictive, and 81% agentic.

Treating these deployments as a monolith can obscure accountability and governance. Instead, banks should orchestrate them as complementary capabilities.

Why this matters: The banking industry is entering a more mature phase of AI adoption where AI use alone is no longer a competitive advantage. Success will now depend on how well institutions integrate multiple AI systems, maintain oversight, and prepare employees to work alongside them.

Recommendations for banks: As AI capabilities become more specialized, banks should:

• Prioritize interoperability over expansion: Before investing in new solutions, ensure existing generative, predictive, and agentic AI systems are integrated with core banking platforms and data infrastructure.
• Create enterprisewide AI performance dashboards. Measure not only how often tools are used, but how different AI deployments work together to improve key business outcomes, such as loan turnaround times, fraud prevention, and operational efficiency.
• Invest in organizational readiness and training: The AI competitive advantage will increasingly depend on employee upskilling and collaboration between technology, risk, compliance, and business teams. AI frontrunners are investing heavily in winning the next generation of AI talent.

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