# AI in Banking: What Senior Executives Really Think About the Road Ahead _Published: 2026-05-13T00:00:00.000-04:00_ New survey of banking executives reveals AI adoption, data challenges and strategy gaps shaping the future of financial services. _Banking leaders are investing heavily in AI, but the gap between adoption and accountability could determine who leads the next era of financial services. _ ### **84% of Banks Are Using AI at an Enterprise Level. The Next Question Is Whether They Can Measure What It Delivers.** nCino recently surveyed 150 senior banking executives across the United States to understand how financial institutions are approaching AI strategy, adoption and measurement. The nCino AI in Banking Benchmark comes at a pivotal moment: the industry has moved past experimentation, and leaders are now working through the harder questions of data readiness, workforce transformation and return on investment. Banks are no longer debating whether AI matters. They’re asking how to make it work at scale, how to measure what it delivers and how to prepare their people for what comes next. ### **Banks Are Going All In on AI, With Generative Leading at 91%** Eighty-four percent of banking executives say their organizations actively use AI at an enterprise level, and nearly all (91%) agree that AI frees employees to focus on higher-value, customer-facing work. But institutions aren’t picking one type of AI and waiting. Generative AI leads adoption at 91%, and predictive and agentic AI follow close behind. Banks are deploying all three at varying levels of maturity, and their comfort with each type is growing in step with how widely they’ve rolled it out. ### **89% of Executives Expect a Dual Workforce Within Five Years** Nearly nine in ten executives expect their organizations to operate as a combination of AI agents and humans within the next five years. Based on what executives report, the shift is already underway with most banking roles already changing significantly because of agentic AI. Banks are preparing by investing in reskilling programs that help employees work alongside AI tools and agents. Today, the most common daily AI use cases center on summarizing documents, searching for information and analyzing data, the survey finds. Over the next two years, executives expect the work AI handles to expand into areas like regulatory research, credit monitoring and client prospecting, a clear shift from support tasks to core operational work. ### **93% Face Data Governance Challenges, Even as 89% Feel Confident in Data Quality** Most executives express confidence that their organizations have accessible, quality data to power AI. At the same time, 93% cite at least one data governance challenge, with siloed data topping the list. That disconnect between confidence and readiness is worth paying attention to. Institutions that close it will be positioned to extract far more value from their AI investments. **Executives see a clear path forward: 94% agree that a fully integrated, end-to-end AI solution would deliver more value than deploying AI in isolated use cases across different systems. **Connecting AI across the full operation, rather than running it in pockets, is the model they want to move toward. ### **Most Banks Have an AI Strategy, but ROI Measurement Hasn’t Caught Up Yet** Eighty-nine percent of senior banking executives have a defined AI strategy in place. Leadership and employees are largely aligned on it, and internal technology leaders like the CTO and CIO rank as the most credible voices for shaping that strategy. The harder question is measurement. Most institutions say they prioritize AI adoption over return on AI investment, and KPIs tied to ROI are the least common metric organizations track. That’s understandable at this stage, but it creates a blind spot: without clear measures of what AI delivers, it’s difficult to know which investments are working and which need to be redirected. Executives acknowledge that internal misalignment on AI strategy costs their organizations time and money. The institutions that build measurement into their AI programs early rather than retrofitting it later will have an advantage as the technology matures. ### **Where the Biggest Opportunities Are** The nCino AI in Banking Benchmark surfaces three areas where financial institutions can accelerate progress. Connecting adoption to accountability is the first. Investment is rising across the industry. Institutions that define clear success metrics tied to revenue, efficiency and customer impact will be the ones that turn spending into results. Closing the gap between data confidence and data readiness is the second. The confidence is there. Addressing fragmentation, silos and governance will unlock the full value that AI can deliver. Building the dual workforce model is the third. As AI agents take on more operational tasks, banks have an opportunity to rethink how their teams work, what roles look like and how human judgment and AI capabilities complement each other. The institutions that get this right will move faster and serve their customers better. **Want to explore additional resources?** - [Download the one-page survey overview](https://assets.ctfassets.net/ze344jjyx19q/7JLqUMF2d3ubPNPyVY026j/a9c93c1c1efb858249c7f5b2c26cd7e6/Infographic_nCino_AI_in_Banking_Benchmark.pdf). - [Download nCino’s AI in Banking Benchmark for the complete survey data.](https://downloads.ctfassets.net/ze344jjyx19q/6N3A7UtUWbkVcdYpEKfP89/00fb427510000c5d7b62b4320597f716/nCino_AI_in_Banking_Benchmark_Full_Survey.pdf) --- [View sitemap](https://www.ncino.com/sitemap)