Bank AI Spending Driven by FOMO, Not ROI, Says Accenture

What happened

Banks are pouring money into artificial intelligence largely out of fear of falling behind rivals, rather than because the technology is delivering clear, sustained returns, according to Mike Abbott, who leads Accenture's banking practice. Speaking to Banking Dive, Abbott said most lenders have yet to see widespread value from their AI initiatives because they have not reworked how work actually gets done around the technology.

Abbott's assessment points to a gap between investment and impact: banks are experimenting with AI across functions such as customer service, fraud detection and back-office processing, but few have redesigned roles, workflows or operating models to capture the full benefit. Spending, in his telling, is being driven as much by competitive anxiety — a fear of missing out — as by a proven business case.

Why it matters

The comments cut against the industry narrative that AI is already transforming banking economics. If adoption is outpacing genuine operating-model change, many institutions may be accumulating technology cost and complexity without a commensurate lift in revenue, expense efficiency or customer outcomes. That has direct implications for how boards and executives justify AI budgets and measure success.

For transformation leaders, the message is that deploying AI tools is the easy part; the harder, unfinished work is redesigning processes, decision rights and employee roles so the technology's output actually changes how the organisation operates. Without that reconfiguration, AI risks becoming a layer bolted onto existing workflows rather than a source of durable advantage.

The Renascence take

This is a familiar pattern in enterprise technology cycles, but it is worth naming plainly: competitive anxiety is a poor substitute for a business case, and it tends to produce exactly the kind of shallow, tool-first adoption that struggles to show ROI.

The real signal here isn't that banks are spending on AI — it's that spending is running ahead of redesign. Technology only pays off when the work around it changes: who makes which decisions, how a process flows end-to-end, and what a frontline employee is actually empowered to do differently on day one. A customer-obsessed operator should treat every AI deployment as an operating-model question first and a technology question second — starting with the specific customer or employee moment the tool is meant to improve, and working backwards from there, rather than adopting AI to keep pace with competitors.