The Data Foundation Crisis: 3 Challenges Blocking Successful AI Adoption in Banking

According to research from Cornerstone Advisors, efficiency and cost management top the priority list for 51% of bank executives and 59% of credit union executives, driving an urgent push toward AI solutions. In fact, 76% of banks are planning AI implementation in the next 18 months. The stakes are high: the same research predicts that revenue per employee at leading institutions is expected to exceed $400,000 by 2030 due to automation and AI, compared to $287,000+ today.

But those productivity gains don't come from deploying AI quickly; they come from deploying it strategically. And strategic deployment requires something most institutions are missing: the ability to easily access and analyze their operational data. Sure, you have plenty of data—it's sitting in every system you use. The problem is that data is trapped in silos, inconsistently formatted, and nearly impossible to analyze at scale. Without solving these fundamental data challenges, AI investments rarely deliver the competitive advantage institutions are hoping for. Below are three common data crises keeping financial institutions from successfully adopting artificial intelligence.

Crisis #1: The Democratization Deficit

The most fundamental barrier to AI readiness isn't technology. It's access. More than 50% of banks say siloed data prevents them from making real-time decisions. Operations teams remain dependent on IT specialists and data analysts for basic operational insights, waiting days or weeks for answers to questions that should take minutes.

This creates a competitive cost beyond mere inefficiency. While your institution waits for custom reports, competitors with democratized data access are moving faster on market opportunities. The democratization of data (making organizational data easily accessible to all employees so they can independently use and analyze it) has become a key banking megatrend precisely because the alternative is falling behind.

The expertise bottleneck compounds the problem. Your operations teams know which questions to ask—they understand the nuances of your lending workflows, the friction points in your processes, and the patterns that signal opportunity or risk. But knowing where to look isn't enough. Without direct access to data-driven insights, they can't pinpoint exactly what's broken or measure which fixes will work. So that valuable domain expertise sits idle, waiting for IT to build another custom report that might answer yesterday's question.

Crisis #2: The Manual Reporting Trap

Business analysts at most financial institutions spend the majority of their time building custom reports instead of analyzing insights. If that sounds inefficient, it is: the people you hired to extract strategic value from your data are stuck spending most of their time building ad hoc reports.

The alternatives aren't much better. Many CRM analytics platforms offer powerful capabilities, but per-user licensing models price out broad operational visibility when you're trying to serve hundreds of employees . And even when institutions make that investment, they discover another barrier: these platforms require specialized technical knowledge most operational teams don't possess.

This is the manual reporting trap. You can't afford to democratize access through expensive per-user licensing. You can't free up your analysts because operational teams constantly need their reports. And you can't deploy AI effectively without knowing where it's actually needed or whether it's working. Operational intelligence should guide your decision-making and reveal your best opportunities—not create more work for an already overwhelmed team.

Crisis #3: The Benchmark Blind Spot

Perhaps the most insidious data crisis is what we call benchmark blind spot: making operational decisions without industry context. Your commercial lending team just reduced cycle times by 10%. Congratulations! But are your peers achieving 25%? Are you actually falling further behind while celebrating incremental improvement?

This is flying without instruments. You're making course corrections based on internal data points that feel positive but lack market context. And when it comes to AI investment, those industry blind spots become expensive mistakes. You're not just spending money—you're falling behind institutions that can see where AI actually works.

Without peer benchmarking data, institutions are flying blind. They invest in AI solutions based on what vendors promise, not what data proves. So they end up automating processes that are already working fine while the real problems—the inefficiencies actually costing them customers—never get fixed.

The Consequences of These Crises Are Clear

These data crises aren't theoretical. They're showing up in institutional performance metrics that determine competitive survival.

Revenue per employee provides a stark illustration. While industry leaders hit $287,000+ per employee, many institutions see this critical metric stagnating. That's not just an efficiency problem. That's a profitability problem that directly impacts your ability to compete on rates, service, and innovation.

Efficiency ratios tell a similar story. Banks need ratios within the low 50% range to stay competitive, but many institutions hover above 60%. Every percentage point above that competitive threshold represents resources that could be deployed toward growth, technology investment, or competitive pricing. Instead, they're consumed by operational inefficiency.

Here's what makes this particularly urgent:AI investments are delivering suboptimal returns at these institutions precisely because they're deployed without the data foundation that makes strategic targeting possible.

You can't optimize what you can't measure. You can't benchmark what you can't see. And you can't strategically deploy AI where data doesn't guide the decision.

The competitive gap is already widening. The institutions that will pull ahead are the ones using operational intelligence to guide their AI strategy. They benchmark internally to track what's working and compare against industry peers to see where the real opportunities are. Every AI investment decision gets made with data, not guesswork.

The question isn't whether to invest in AI. The question is whether you do it strategically or speculatively.

A Common Problem. An Intelligent Solution.

What separates analytics from intelligence? Analytics shows you what happened. Intelligence shows you what it means and what to do about it.

Operations Analytics is nCino's answer to banking's data foundation crisis. It's not another business intelligence tool requiring custom development or a generic analytics platform retrofitted for financial services. It's banking intelligence, purpose-built for the unique workflows, metrics, and operational realities of financial institutions.

Operations Analytics delivers pre-built operational performance dashboards spanning Commercial, Small Business, and Consumer lending operations, with zero custom development required. Because it's embedded within the nCino Platform rather than bolted on, there's no complex data extraction, no middleware to maintain, no synchronization delays. The intelligence flows directly from your operational workflows.

Perhaps most importantly, Operations Analytics provides historical trending capabilities beyond the point-in-time snapshots typical of standard reports. AI doesn't just need current data. It needs patterns, trends, and historical context to identify opportunities and predict outcomes. That requires a data architecture built for intelligence, not just reporting.

From foundational operational visibility to advanced peer benchmarking and process mining, Operations Analytics gives institutions the full spectrum of capabilities needed to move from data crisis to competitive advantage. For operations teams, this means self-service analytics in days, not months. For executives, it means operational visibility without six-figure BI platform investments. For analysts, it means shifting from report building to insight generation.

The Power of Collective Intelligence

What makes Operations Analytics uniquely powerful isn't just the dashboards or the peer benchmarking—it's the ecosystem behind it. nCino has built something competitors can't replicate: a massive repository of financial industry operational data. This data doesn't just help individual banks identify where to deploy AI—it shows us where to invest in product development based on actual industry trends, not assumptions. Our collective strength comes from shared intelligence that makes every institution smarter and makes our platform better.

For Financial Institutions: Intelligence Backed by 1,250+ Peers

When you deploy Operations Analytics Pro with peer benchmarking, you gain access to anonymized performance data from across 1,250+ financial institutions, and that number keeps growing. This collective intelligence reveals exactly where nCino solutions deliver highest ROI, identifies operational improvement opportunities with competitive context, and informs strategic technology investments with industry-validated insights.

Consider what this means in practice. A certain product in your commercial lending space has a cycle time of18 days. Good? Bad? Without peer context, you're guessing. With Operations Analytics peer benchmarking, you discover that institutions in your asset size category average 12 days—revealing a $2.1 million annual opportunity cost. Now you're not just measuring performance. You're understanding competitive positioning and quantifying the business case for improvement.

This is the data foundation AI needs to succeed. When you deploy automation or machine learning, you're not guessing which workflows need optimization. You're targeting interventions where peer intelligence proves the opportunity exists and quantifies the expected impact. Financial institutions get more than analytics. They get true banking intelligence backed by the collective experience of thousands of financial institutions.

From Data Crisis to Competitive Advantage

In 2026 and beyond, banking success won't be determined by who adopts AI fastest. It will be determined by who builds the data foundation that makes AI adoption effective.

The choice facing banking leadership is clear:

1. Continue the race toward AI without addressing data infrastructure and join the majority of institutions struggling with implementation

2. Build your intelligence layer first and transform operational data into strategic insights that inform every technology investment

Ready to Take the Next Step?

Schedule an Operations Analytics demo to see how peer benchmarking transforms operational data into competitive intelligence. View the dashboards, see the data difference, and map your journey from data crisis to competitive advantage.

Not sure where to begin? nCino offers professional consultation tailored to your institution's goals, turning insights into action with a strategic roadmap for AI deployment.