The gap between the largest banks and everyone else is widening, and AI is accelerating the divide. According to the
Regional banks and credit unions feel the squeeze. They know AI is a competitive necessity, not a future consideration. But for most mid-market institutions, the path to AI runs straight through a tangle of legacy systems, fragmented workflows and stretched IT teams.
"They are really being squeezed from both sides," says Jennifer Margules, director of portfolio marketing at BMC Software. Larger competitors are pulling ahead with AI-driven services, while customers and members increasingly expect the same seamless digital experience regardless of where they bank.
AI Doesn't Create Operational Problems. It Reveals Them.
The challenge isn't that AI is too complex for smaller institutions. It's that AI demands a level of operational maturity that many don't yet have.
Running AI well requires systems that talk to each other in real time, clean data flowing reliably across the organization, and enough visibility into what's happening across the technology stack to catch problems before they cascade. That means core banking, cloud platforms, data pipelines and third-party services all operating in coordination, not as disconnected islands.
Most regional banks and credit unions aren't there. They have batch processes layered over legacy cores, point fixes bolted on to address specific gaps, and manual handoffs bridging systems that were never designed to work together.
AI doesn't tolerate that kind of fragility. Where a batch process might quietly absorb a failed handoff until someone catches it in the morning, an AI model consuming that same data in real time will produce flawed outputs, missed fraud signals or compliance gaps, with no buffer to intervene.
"Regional banks and credit unions are discovering that their failure point for AI isn't the models or the algorithms," says Margules. "It's back-end orchestration and resiliency. They've been playing catch-up to meet today's demands, and AI raises the bar even further."
Fix the Plumbing Before You Turn On the AI
Many institutions have turned to their core banking providers for modernization, or added point tools to address specific needs. These are reasonable first steps, but they rarely get institutions where they need to be. Core-provider automation tends to be limited in scope, difficult to customize and unable to coordinate workflows that span multiple applications, data platforms and outside vendors.
The institutions making real progress are the ones investing in orchestration that works across their entire environment, not just within individual systems.
Banpará, a regional bank in Brazil that operates across 161 branches in 109 municipalities, offers a useful example. The bank adopted Control-M to automate and schedule routines and processes that had previously been handled manually. The bank gained better visibility and control over its workflows, reduced human errors by automating multi-step manual processes, and integrated internal and external systems so problems are detected and routed correctly at each stage.
Banpará's story isn't an AI story. But it illustrates a prerequisite. You can't layer intelligent automation on top of a broken operational foundation. The institutions getting AI right are the ones that fixed the plumbing first, building the orchestration, visibility and data integrity that AI depends on to function.
Getting the Data Right
Even with a solid operational foundation, AI is only as good as the data it consumes. This is where many smaller institutions face their steepest climb. Legacy infrastructure often comes burdened by data spread across disconnected systems, inconsistent formats and limited governance, leaving AI training sets incomplete or unreliable.
There is some encouraging news here. AI itself can help close the gap. Anomaly detection models trained on historical error patterns can flag data quality issues within seconds of ingestion. AI can also analyze existing code and auto-generate data lineage maps and data dictionaries, keeping metadata current even as systems evolve.
When this works, it creates a virtuous cycle: AI improves the data, and better data improves the AI. Quality goes up, lineage stays current, privacy rules are enforced and new training material becomes available on demand. Institutions that have deployed these capabilities report faster model development, lower operational costs and smoother regulatory interactions.
But none of it works if the underlying systems can't move data reliably in the first place. The data strategy and the operational strategy are inseparable.
The Window Is Narrowing
The pressure on mid-market institutions isn't going to ease. Customer expectations are already set by the largest players, and AI is only raising the bar. Regulators are increasing their focus on transparency, cybersecurity and model governance. And the institutions that invested early in their operational foundations are now compounding that advantage with AI capabilities their less-prepared peers can't easily replicate.
"The banks and credit unions that wait for AI to force the issue will find they're already behind," says Margules. "The foundation work isn't glamorous, but it's what separates the institutions that can move on AI from the ones that keep talking about it."
For mid-market institutions, AI isn't just a technology initiative. It's a stress test for the entire operating model. The ones that pass will be the ones that built the foundation first.
If you're focused on building the foundation first, including workflow orchestration, resiliency, and reliable data movement, this overview of
