Banks are accelerating AI adoption, but weak data foundations and limited workforce reskilling could prevent financial institutions from turning widespread deployment into meaningful returns.
According to nCino’s AI in Banking Benchmark, 91% of banking executives say their organisation has an AI strategy, while 71% have established KPIs to track progress. However, operating cost reduction and revenue growth, the measures most directly tied to financial returns, rank lowest among the KPIs banks are monitoring. Some 81% of respondents also said their organisation has prioritised AI adoption over ROI.
The figures do not necessarily mean banks are deploying AI without a clear path to value. nCino argues that financial returns tend to come later in the adoption cycle, after institutions have established use cases and generated measurable improvements in areas such as productivity and workflow automation.
nCino market analyst Danielle Pearson said, “Adoption always precedes value. If you look at those priorities as a timeline rather than a hierarchy, banks are being pretty level-headed about where they are on the journey to ROI.”
Productivity and workflow automation currently lead the KPIs tracked by banks, followed by customer experience at 44% and operational efficiency at 36%. These early improvements can eventually contribute to lower operating costs and increased revenue as AI becomes embedded across more processes.
One example comes from ConnectOne Bank. As reported by American Banker, the bank reduced document search times by 98% within three months of deploying agentic AI in commercial lending, taking the process from around 20 minutes to roughly 30 seconds. An AI agent also reduced the time required to update client relationships in documents by 60%.
Pearson said, “The last thing you get is revenue realization. The first things you get are measurable returns on very specific things, and those compound and aggregate into the financial returns over time.”
However, the first major barrier to achieving those returns is the workforce. While 84% of banks are deploying AI across their organisations and 89% expect humans and AI agents to form a blended workforce within five years, only 55% are actively reskilling employees. Just 33% are hiring workers with AI experience.
This gap could become more significant as AI moves beyond individual productivity tools and begins to reshape how employees carry out core banking processes.
Pearson said, “AI is never going to truly replace people. It might make certain tasks redundant, but it’s only going to augment your capabilities.”
ConnectOne Bank CEO Frank Sorrentino said, “We could save our frontline folks 50% of their time by taking away the administrative tasks.”
He added, “If we give them 1,000 hours back, does that mean we’re going to fire 50% of our producers, or do you think we’re going to allow them to go out and be 100% more productive?”
nCino CMO Nicole Caldwell said, “I don’t think people are scared of change. I think they’re afraid of not being taken along for the ride and not being involved.”
The second challenge is data. While 87% of banking executives say they are confident they can access high-quality data, 52% also report that their data remains trapped in silos. Other challenges include data integrity, consistency and accessibility.
This creates a potential problem for banks looking to scale AI. Models and agents are only as reliable as the information they can access, meaning poor data foundations can undermine the performance of otherwise sophisticated AI systems.
Pearson said, “AI cannot fix underlying structured data. In fact, relying on AI to compensate for gaps, like missing data or siloes, can accelerate data decay. If you have problems with the data that AI consumes, it can translate into poor outcomes. Every executive should be thinking about their data strategy.”
nCino recommends that banks establish a clear baseline for their existing operations before introducing AI, define how individual use cases will generate returns and regularly measure progress towards a predetermined break-even point.
The company’s research suggests banks remain relatively early in their AI journey, with adoption and productivity improvements coming before the financial outcomes executives ultimately want to measure.
nCino CEO Sean Desmond said, “On the other side of every major technology inflection point there has been growth.”
For banks, the next phase of AI adoption may therefore depend less on deploying more technology and more on whether they can build the data infrastructure and workforce capabilities needed to turn that technology into measurable business value.
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