Building the AI-Forward Bank: Beyond Automating the Past

A critical challenge is emerging in financial services. Jim Marous recently noted that while 18% of banks have integrated AI workflows, most are simply automating legacy processes instead of innovating. This is a familiar error. Brian Solis, Head of Global Innovation at ServiceNow, and his colleague Dave Wright, Chief Innovation Officer, call it the “iteration trap,” a pitfall that has derailed digital transformation initiatives before. Their new book, Infinite: How Visionary Leaders Transform Today’s Businesses into AI-Forward Companies, draws a crucial distinction between AI-enabled firms that enhance existing operations and the AI-forward firms that pursue capabilities once thought impossible. Very few companies make the cut. In fact, their research shows only 5% have achieved AI-forward status. This article will examine the implications of their work for banking and the changing competitive landscape.

Solis and Wright argue that most financial institutions are applying AI to accelerate yesterday’s work, then measuring the results with yesterday’s metrics. This approach repeats the same mistakes that caused previous digital transformation programs to fail. To find the real points of friction, they advocate for creating a “work chart,” which can expose the value-destroying operational handoffs that a traditional “organization chart” completely obscures. The authors insist that any savings from these iterative AI projects must be redirected. That money should fund the genuinely innovative AI initiatives that can generate new revenue. Wright recalled a government delegation that planned to deflect 90% of citizen calls but had no clear idea of the ultimate purpose or benefit of doing so. In a similar vein, Solis shared that 75 chief executives confessed to him privately that they felt immense pressure to have an AI strategy, even when they had no clarity. Marous added that banking’s culture creates its own headwinds, with a quarterly reporting cycle that rewards immediate cost-cutting and a deep-seated risk aversion taught to every new banker from their first day. He believes the next wave of banking consolidation will be determined by AI readiness, not asset size, making the operational friction an acquired firm brings a critical new metric for any deal.

Key Takeaways

• Only 18% of banks use AI workflows, and most are automating old processes.
• The “iteration trap,” as defined by Brian Solis and Dave Wright, is when AI speeds up existing work instead of creating new value.
• A mere 5% of companies worldwide qualify as “AI-forward,” using AI for tasks that were previously impossible.
• Solis and Wright propose using “work charts,” not “organization charts,” to identify and fix hidden inefficiencies.
• Cost savings from basic AI automation should be reinvested into innovative AI projects that can drive revenue.
• Jim Marous predicts that AI readiness and operational “friction” will soon outweigh size in banking mergers and acquisitions.

Industry Implications

The difference between being AI-enabled and becoming AI-forward has profound consequences for the financial services industry. As Solis and Wright explain, banks stuck in the iteration trap will inevitably fall behind competitors who use AI to completely reimagine their services and customer relationships. The industry’s cultural habits, from its obsession with quarterly results to its inherent risk aversion, form a systemic barrier against the kind of long-term, visionary AI investment required to become an AI-forward institution. This divergence will likely create a market of winners and losers, where a small group of truly advanced banks captures an outsized share by delivering novel products and experiences that their AI-enabled peers simply cannot match.

A market restructuring is coming. The predicted consolidation wave, based on AI maturity instead of sheer scale, will reshape the industry. Smaller, more agile banks that build their strategy around AI could emerge as formidable challengers or prime acquisition targets. Larger, entrenched institutions may struggle with their legacy systems and cultural resistance to change. If these giants acquire technologically inferior and culturally incompatible firms, they will only absorb more “friction,” stalling their own progress toward an AI-driven future. At the same time, customer expectations will shift quickly. People will demand the proactive, personalized, and seamless services that only AI-forward banks can offer, which could trigger an exodus from more traditional institutions. Regulators may also find themselves needing to adapt their frameworks to govern the new risks and ethical questions that arise from sophisticated AI in finance.

Conclusion

AI adoption in banking has reached an inflection point. The majority of institutions are optimizing the past, while a very small minority is building an AI-forward future. The work of Brian Solis and Dave Wright shows that a sustainable competitive advantage will come from developing entirely new capabilities with AI, not just from making old processes faster. The urgency of this shift is underscored by Jim Marous’s forecast that readiness, not scale, will define the next era of banking consolidation. In the financial landscape now taking shape, the leaders will be distinguished from the laggards by their ability to strategically fund true innovation and fundamentally redesign how their organizations work.