High-Impact AI Use Cases for Commercial Banking

I’ve spent decades in commercial and small-business banking, in various roles at the intersection of technology and business. So, I was curious what Backbase and EverBank had to say in their recent webinar, “AI that Delivers: 3 High-Impact use cases for commercial banking.” My quick takeaway? What stands out isn’t technology innovation — it is the operational drag beneath it.
Despite a decade of investment, commercial banking digital tools and processes still haven’t conquered friction. Onboarding remains manual and document-intensive. Hours reviewing articles of incorporation, recreating entitlements and permissions, testing ERP maps integrations. If AI can drive efficiency gains of 50% or more in onboarding, that’s not incremental improvement — it’s accelerated revenue realization.
Relationship managers face a similar reality. Many spend most of their time navigating internal systems and responding to routine queries. That’s a tactical model in a market that demands advisory depth and client engagement. For this use case, AI’s role isn’t replacement — it’s reallocation of time spent in a swivel chair to more customer-facing interactions.
Meanwhile, fintechs are rapidly advancing predictive liquidity tools and payment optimization. While banks still hold the trust advantage for the moment, trust alone isn’t enough to eliminate friction.
As banks consider applying AI to high-impact use cases, governance and compliance continue to surface as primary barriers to adoption, and rightly so. Clean data, clear controls, and defined ROI will separate scaled programs from stalled pilots. This isn’t about AI experimentation. It’s about systematically removing operational drag from commercial banking core processes.
The institutions that win won’t be the loudest about AI. They’ll be the most disciplined about where they apply it. Where are structural inefficiencies costing you the most today?