A striking disconnect is widening at the heart of the global banking industry: institutions are accelerating their artificial intelligence (AI) budgets at a rapid pace, yet the overwhelming majority have failed to generate the widespread, sustained business value those investments were meant to deliver. According to Accenture executive Mike Abbott, the root cause is not the technology itself — it is an organisational failure to restructure work around AI capabilities, compounded by a fear of competitive irrelevance that is distorting spending priorities across the sector.
Abbott's diagnosis is pointed and uncomfortable for an industry that has made AI a centrepiece of its strategic narrative. Banks, he argues, "have not figured out how to reconfigure the work, fully, around AI yet." This is more than a technical shortcoming. It represents a fundamental misalignment between capital allocation and operational transformation — the kind of structural gap that no amount of vendor procurement can close on its own.
The phenomenon Abbott describes has a well-understood name in technology investment cycles: fear of missing out, or FOMO. In the banking context, it manifests as a boardroom-level anxiety that rivals are gaining an irreversible edge through AI deployment, triggering reactive spending decisions that prioritise the appearance of innovation over disciplined return-on-investment (ROI) analysis. Senior executives greenlight AI programmes not primarily because they have identified concrete workflows to transform, but because standing still feels more dangerous than spending without a clear plan.
This dynamic is not new to financial services. Prior technology waves — core banking modernisation, mobile-first transformation, cloud migration — each produced their own version of FOMO-driven expenditure followed by lengthy periods of rationalisation. What distinguishes the current AI cycle is the speed and scale of the spending commitments being made before best practices have been established. Banks are, in effect, running an industry-wide experiment in which the hypothesis has not yet been validated.
The consequence of misaligned AI investment is particularly acute in banking because the sector's cost structure and regulatory environment leave little tolerance for sustained misallocation. Unlike technology companies that can absorb exploratory spending against high-margin software revenues, banks operate in a capital-intensive, compliance-heavy environment where efficiency ratios are scrutinised by investors and regulators alike. AI spending that does not translate into measurable expense reduction or revenue generation will eventually face a reckoning in earnings calls and analyst reviews.
Abbott's framing points toward the deeper organisational challenge: genuine AI value in financial services requires banks to redesign processes, redeploy staff, and fundamentally alter how decisions are made — not simply to layer AI tools onto existing workflows. A fraud-detection model that accelerates case review generates limited value if investigators are not retrained and caseload targets are not recalibrated. A generative AI assistant deployed in a contact centre delivers marginal gains if call-routing logic, staffing models, and quality-assurance frameworks remain unchanged. The technology is a necessary but insufficient condition for transformation.
This is not an argument against AI investment in banking. The long-term productivity and risk-management potential of machine-learning systems, large language models, and predictive analytics in financial services is substantial and well-evidenced in early use cases. Institutions including JPMorgan have pointed to thousands of AI applications in production across trading, compliance, and customer service functions. The argument is rather about sequencing and governance: investment should follow a credible operating model redesign, not precede it in hopes that the technology will force organisational change from the bottom up.
What This Means for the Industry
Abbott's warning arrives at a moment when banking AI budgets show no sign of contraction, meaning the gap between expenditure and realised value could widen further before institutional discipline reasserts itself. The banks that will ultimately extract durable competitive advantage from AI are likely those that invest as heavily in change management, workforce redesign, and process re-engineering as they do in model development and data infrastructure. FOMO may be the spark that gets AI programmes funded, but it cannot substitute for the sustained organisational rigour required to make those programmes pay. For bank boards and technology committees heading into 2027 planning cycles, Abbott's message deserves to sit at the top of the agenda: the question is no longer whether to invest in AI, but whether the institution is genuinely prepared to reorganise itself around what AI can do.
Written by the editorial team — independent journalism powered by Codego Press.