In the years of falling revenues, banks sorely need productivity gains and could potentially get them from AI (Machine learning and Deep Learning technologies). But AI is a double-edged sword, likely to bring cost savings as well as disruption. Agentic AI in particular has the potential to radically reshape banking—and not necessarily to the benefit of the industry as a whole. It could create unprecedented efficiencies and new customer value, but without decisive adaptation by banks, it stands to erode traditional profit pools.
Early adopters will be able to secure a lasting advantage over slow movers. Given these are still the early days of agentic and gen AI, it is imperative to use surgical precision to identify where these technologies can truly generate earnings impact, rather than piling into them because of the fear of missing out.
The magnitude of AI’s effect on banking will likely depend on two key factors: the extent to which banks can become fully agentic and radically lower the cost of operations, and the extent to which customers adopt AI to manage their financial affairs.The “precision toolbox,” applicable to banks of any size, revamps strategy across four core dimensions:
Technology: focusing surgically on technologies with the greatest impact—even within agentic and gen AI—while scaling back investments that don’t improve workflows, customer engagement, or business models
The new consumer: moving beyond broad segmentation to individualization (a “customer segment of one”), delivering hyperpersonalized, data-driven access to products and services that earn trust in an era of fading loyalty
Capital efficiency: shifting from sweeping reallocations to micro-level balance sheet discipline—product by product, client by client, down to individual risk-weighted assets—to free up trapped capital with precision and put it to work where it earns more
Targeted M&A: moving from scale for size’s sake to precision, pursuing deals that add reach in specific micromarkets or geographies, or that bring distinct capabilities in a specialized area.
If just 5 to 10 percent of checking balances migrated to top-of-market rates, an action that might be prompted by AI agents, that could reduce the banking industry’s total deposit profits by 20 percent or more.
The threat from third-party agents could be material. If banks don’t reposition their business models to adapt, over the next decade or so, bank profit pools globally could decline by $170 billion, or 9 percent. That’s enough to bring average returns below the cost of capital.
But the effects won’t be felt equally. AI pioneers could see return on tangible equity (ROTE) increase by up to four percentage points, using their lead to reinvent models and capture value. Conversely, slow movers are likely to see lower profits in the long term prospective.