Banking executives are under growing pressure to show that AI investments deliver more than incremental efficiency. EY research shows only 40% of banks consider themselves AI leaders, and just over half have seen revenue gains from AI. The top 10 banks are maturing their AI capabilities at 2.3 times the rate of peers, while 98% of institutions report financial losses from AI-related risks.
The findings point to a gap between deployment and real value. The EY analysis identifies five attributes that separate leaders from the rest, a theme explored in our AI for Executives & Strategy coverage. "The challenge isn't a lack of AI use cases, but narrowing the options to those that generate real value, measured against financial returns and strategic goals," said Beatriz Sanz Sáiz, EY Global AI Sector Leader.
Bold, coordinated vision
Fragmented pilots create duplicated tools, inconsistent data, and governance gaps. An enterprise-wide AI strategy should define common infrastructure and reusable capabilities. "Without a platform-based approach, banks risk creating 15 versions of the same AI function - just like they once used 15,000 spreadsheets," said Sameer Gupta, EY Global Financial Services AI Leader. A clear vision sets non-negotiable policies centrally while allowing front-line teams room to experiment. Platform-based AI libraries for fraud detection, forecasting, and other tasks keep logic consistent across the organization.
Risk-informed governance
Governance remains the top AI challenge for 52% of banks, and only 44% invest in technology to support ethical AI adoption. A watchtower model - combining automated controls, human oversight, explainability standards, and continuous model validation - has become a leading practice. "Standardization is the way to scale responsibly," Gupta said. Flexible governance models that cover external partners and SaaS platforms are essential, as are metrics that track risk-adjusted returns.
Business-led, strategic focus
Many banks still treat AI as a technology backlog rather than a capital allocation decision. "AI isn't a backlog of use cases; it's a capital allocation decision. Fund the few bets you can govern and measure end to end, and shut down everything that can't prove both value and trust at scale," said Preetham Peddanagari, EY UK&I Chief Technology Officer. Some banks now use digital twins for intraday liquidity optimization and AI agents to automate the full order-to-cash cycle. A disciplined ROI-based roadmap linked to EBITDA and functional KPIs helps overcome the 44% of executives who struggle to prioritize use cases.
Human-centered approach
Technology alone won't deliver returns. Banks need skilled AI teams, change management, and redesigned workflows. Junior analysts review AI outputs for deal books, while senior underwriters validate AI credit decisions. New roles like prompt engineers and AI workflow designers are emerging. "The biggest barrier to scaling AI isn't the algorithm - it's the change management. Training teams, reworking processes and putting in the right governance often takes twice the effort of building the model itself," Gupta said. An EY survey found 84% of desk-based employees are enthusiastic about working with AI agents, but 56% worry about job security.
Future-focused design
Bank leaders must plan for how AI will interact with quantum computing, tokenization, and digital assets. Technical architecture should emphasize modularity and scalability. Hybrid approaches - combining cloud with on-premises models for sensitive use cases - will become more common. Vendor relationships need constant evaluation as new capabilities emerge.
Recommended actions for banking leaders
- Set a clear vision that links AI to core strategic goals and financial targets, moving beyond fragmented pilots.
- Build an ROI-based roadmap tied to EBITDA, P&L, risk reduction, and customer engagement metrics.
- Establish a governance watchtower with board-level oversight, automated monitoring, and explainability standards.
- Prioritize data lineage and quality, using AI to automate tracking and identify issues.
- Shift accountability for AI outcomes from IT to business leaders, especially in product development and client service.
- Engage regulators to help shape industry standards for data security and ethical use.
- Invest in upskilling, change management, and a culture that encourages responsible AI experimentation.
Why this matters for executives and strategy
The gap between AI leaders and laggards in banking is widening, and the leaders are pulling away at more than twice the speed. Executives who treat AI as a technology project rather than a business driver will see their investments erode. The five attributes - coordinated vision, rigorous governance, business-led focus, human-centered design, and long-term thinking - form a framework that turns AI spending into measurable returns. The immediate priority is to set the tone from the top, define clear success metrics, and build the governance and talent structures that allow AI to scale without fracturing trust or control.
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