Banking on a Force Multiplier: How Agentic AI Could Redefine the Future of Banking

According to the American Bankers Association, more than three-quarters of U.S. consumers now prefer to manage their money through a mobile app or online portal. From checking balances to paying bills, digital banking has made financial management faster and more convenient than ever. Yet the same technology that made it easier has also widened the gap between bankers and customers for some of the institution’s most essential services like lending and personalized advice. It comes as no surprise that bankers still question whether technology truly multiplies human capability.

Now, Agentic AI is poised to change the math.

A force multiplier, a term borrowed from defense strategy, refers to any tool or tactic that dramatically expands human effectiveness. Manufacturing, logistics, and even healthcare have already embraced technologies that extend human reach. But in community banking, the human relationship has always been the multiplier. Trust, empathy, and judgment – the cornerstones of banking since ancient Babylon – don’t translate easily into code.

According to the American Bankers Association June 2025 survey, 80% of U.S. banks have increased their investment in AI. Beyond chatbots and fraud detection, a new class of “agentic” systems is emerging. Agentic AI-enabled systems don’t just respond, they act. Early prototypes show these systems autonomously executing multi-step workflows common in compliance, such as BSA, AML, or KYC reviews. They don’t just flag suspicious activity; they reason through it, plan next steps, and adapt as they learn.

This is not merely an upgrade to the bank’s tech stack – it’s the beginning of a structural shift in how banks operate.

Unlike generative AI, which produces content from human prompts, agentic AI operationalizes intelligence. It takes an objective, plans actions, and carries them out with minimal supervision. Think of generative AI as a smart assistant; agentic AI, by contrast, behaves like a proactive teammate. In retail banking, such teammates or “agents” could help customers budget, refinance loans, or automatically switch to accounts offering higher yields. In doing so, they could unlock billions of dollars in new consumer activity while transforming how banks compete for deposits and loyalty.

But for all its promise, agentic AI faces the same old challenges: data quality, governance, and the delicate balance between automation and trust. Can an algorithm truly learn the nuances of a customer’s financial life? Can it replicate the empathy of a banker who knows a client’s story?

The banking industry’s next great test isn’t just deploying AI, it’s training it to earn trust. If agentic AI is to become a true force multiplier, it must evolve from sidekick to teammate, reshaping not only what banks do but how they serve.