The AI industry has a supply problem. It’s just not the kind most people expected.

Vendors are racing to release increasingly sophisticated models, products and platforms. Organizations, meanwhile, are still trying to generate value from the tools they already have. Gartner describes this disconnect as an AI adoption gap, with demand lagging far behind the industry's rapidly expanding supply of AI capabilities. Despite years of investment and experimentation, only 22% of organizations have successfully scaled AI across multiple business units.

For banks, the challenge isn't getting access to AI. It's finding practical ways to use it. While there's no universal blueprint for successful adoption, one lesson is becoming increasingly clear: the biggest wins often come from improving how work gets done rather than trying to remove people from the process entirely. This had led many to focus on augmentation over automation.

For all the excitement around autonomous AI, banking remains an industry where judgment matters.

Customers expect personalized support. Regulators demand accountability. Institutions need transparency around how decisions are made. When it comes to complex customer interactions, fully removing humans from the process introduces operational, compliance and reputational risk.

That doesn't mean AI has limited value in banking. It means the most valuable applications are often less dramatic than the industry headlines suggest.

Rather than handing decision-making authority to a machine, many institutions are focusing on the routine tasks consuming employees' time and attention. Research from outside the banking context already suggests this approach can deliver meaningful results.

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