AI in Banking: From Pilots to Production-Pragmatic

After years of discussion and experimentation, AI in banking has entered a new phase: execution. What started as isolated proof-of-concepts in 2024 is rapidly moving into full-scale production in 2025. Banks of all sizes — from global giants to regional players — are no longer asking if AI belongs in their strategy, but how fast they can deploy it.

According to industry leaders, the transition is pragmatic and human-centric. Banks are taking a use-case-first approach, applying AI to specific, high-value processes such as document intelligence, customer onboarding, and operational efficiency. In areas like credit decisioning, institutions remain cautious, embedding human-in-the-loop frameworks to ensure accountability and regulatory compliance.

Rather than overhauling systems wholesale, banks are infusing AI into existing journeys — bolting it onto core workflows instead of creating stand-alone experiments. This “incremental transformation” approach allows them to measure real impact and scale what works.

Supporting this change is a move toward agile, sprint-based transformation. Large, monolithic programmes are being replaced with smaller, iterative initiatives that keep budgets under control and deliver visible results quickly. Alongside this shift, banks are addressing one of their biggest AI challenges: data silos.

Legacy core systems and fragmented architectures make AI in banking expensive and time-consuming. To fix that, institutions are deconstructing their cores and deploying digital wrappers — low-code layers that modernise the customer experience without disrupting the entire system. As one CIO put it, “You can’t run AI on a 20-year-old core.”

The mindset shift is clear: some core modernisation is now mandatory. With agile delivery, transparent data structures, and trusted technology partners, banks are finding the balance between speed, safety, and scale — turning AI from a buzzword into a measurable business advantage.