IN THIS SESSION

The banking industry has entered a new phase of AI maturity. After several years of building foundational capabilities, experimenting, and rolling out productivity tools across the enterprise, banks are now focused on deploying AI faster, embedding AI into legacy workflows, and proving measurable impact.

Evident Co-Founder and Co-CEO Alexandra Mousavizadeh, Managing Director of Banking & Payments Daniel Shackleford, and VP of Intelligence Colin Gilbert unpack the key findings from the 2026 Evident AI Index for Banks. In this session, they explore:

• Why established AI leaders continue to pull ahead, despite unprecedented industry-wide acceleration
• The five definitely “hallmarks” that set these institutions apart
• How investments in talent, research, infrastructure, and governance are increasingly translating into measurable business impact

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Key Discussion Topics

AI maturity is accelerating, but the gap between leaders and laggards persists: The average bank’s Index score rose 26% year-on-year, approximately 3x the historical rate of improvement. Yet eight of last year’s top-10 banks retained their place in the winner’s circle. While access to AI technology has become cheaper and deployment playbooks more widely available, the organizational capabilities required to scale AI remain difficult to replicate. The top-10 account for 45% of AI talent and 35% of use cases with measurable outcomes, illustrating how years of accumulated investment continue to reinforce their competitive advantage.

The era of enterprise-wide tool rollouts is giving way to the “industrialization” of AI: Public disclosures show that at least 60% of employees across the 50 banks have access to general productivity tools. But rollout alone is no longer a meaningful measure of success. Leading banks are establishing processes to find the applications their employees have built, standardize them, and redeploy them across business functions. This is reflected in the diversification of use cases that are spreading across more lines of business and reaching new application areas. The next frontier involves tackling larger transformations, where AI is embedded alongside broader process re-engineering efforts.

Reusable architectures and institutional intelligence are becoming the foundations of competitive advantage: As frontier models become more interchangeable, differentiation is shifting toward the infrastructure surrounding them: proprietary data, integrations, tools, permissions, evaluations, controls, and orchestration. Reusable architectures let banks deploy common components across multiple applications, while dynamic model routing optimizes performance and inference costs. Beyond technical infrastructure, the panel also explored an emerging concept of “reusable intelligence.” This means capturing institutional knowledge and domain expertise that would otherwise remain siloed within individual teams, and making it accessible across the enterprise.

Talent, applied research, and governance are evolving from foundational enablers into accelerants of deployment: As banks move from experimentation to industrialization, the capabilities required to scale AI are shifting toward the people and processes that bridge technical development and business implementation. AI hiring keeps growing, but leading banks are prioritizing enablement roles, such as AI product managers, forward-deployed engineers, and specialized risk professionals. Research is similarly becoming more applied, with more attention on evaluation, reliability, and the practical challenges of deployment. Meanwhile, reusable controls and automated governance are letting banks build faster without compromising oversight. Together, these shifts reflect a broader reorientation of AI investment: from building foundational capabilities toward clearing the bottlenecks that stop successful applications from scaling.

The Bigger Picture: The 2026 Evident AI Index for Banks shows an industry moving rapidly from adopting AI to absorbing it into how the business runs. Access to AI is becoming a commodity, but the ability to deploy it effectively is not. The defining question is no longer whether banks can deploy AI. It is whether they can turn it into a repeatable engine of enterprise-wide value creation.

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