The news: Banks are entering a new phase of AI adoption, according to EY. While many have already realized efficiency gains from early AI deployments, the firm argues that future success will depend on five key attributes:

• Moving beyond cost savings to growth and differentiation: Most banks have captured productivity gains from chatbots, copilots, and automation. The next challenge is using AI to drive revenue growth, improve the customer experience, accelerate product innovation, and strengthen risk management. 
• Ensuring proper governance: Banks with formal oversight, real-time monitoring, and clear accountability are more likely to realize value from AI while minimizing risks such as hallucinations, bias, poor data quality, and regulatory issues. Currently, insufficient governance is banks’ biggest barrier to scaling AI.
• Managing AI like a strategic portfolio. Banks should focus resources on a small number of high-impact use cases tied directly to business goals, financial returns, and measurable KPIs. 
• Centralizing the AI platform rather than siloed rollouts: Shared infrastructure, reusable AI agents, common data standards, and enterprise governance help avoid duplication, reduce risk, and scale successful use cases across the organization. 
• Prioritizing people and company culture: As we recently covered, AI should augment employees rather than simply automating tasks—requiring significant investment in upskilling, change management, and business ownership to ensure adoption and sustained value creation. 

Implications for banks: EY's emphasis on governance, explainability, and human oversight speaks to a broad challenge facing the industry: trust. While AI adoption is increasing, consumers still place more trust in their banks than in AI tools, suggesting banks' ability to deploy AI responsibly—and maintain clear paths to human support—could be a key factor in adoption. As institutions expand customer-facing AI experiences, trust and transparency may be as important as the underlying technology.

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