Crossing the GenAI Divide in Financial Services
MIT’s recent State of AI in Business 2025 report delivers a sobering statistic: 95% of enterprise GenAI initiatives produce zero measurable return on investment. Despite billions in spending, only a small minority of organizations have turned pilots into production systems that deliver meaningful business value. MIT calls this the GenAI Divide—the growing gap between institutions experimenting with AI and the few that scale it into production with real business impact.
Financial services firms sit squarely in this dilemma. There is no lack of interest; research and pilots are abundant, but transformation is rare. MIT’s findings show that the divide is not caused by model quality or even regulation, but by organizational approach. We see the same in banking. While the industry is highly regulated, regulation itself is not the bottleneck. The real barrier is the back-office, resource-intensive process, such as AI integration and AI Model Risk Management (MRM).
Experian’s recent survey of more than 500 financial institutions confirms this point. Sixty percent of banks still rely entirely on manual processes for compliance, with risk teams spending up to a third of their time on documentation instead of innovation. According to this survey, nearly 70% say current technology fails to meet regulatory needs, and regulators are escalating supervisory concerns more frequently than ever. In other words, outdated MRM processes drain resources, slow adoption, and expose institutions to regulatory risk.
The Solution: Modernized Model and AI Risk Management
When modernized, MRM can transform from a bottleneck into a catalyst. It becomes a highly integrated and automated control tower for AI, accelerating adoption and strengthening trust by providing audit-ready documentation, robust validation, and transparency throughout the AI lifecycle for every model. It can also deliver measurable returns by shortening approval cycles, reducing consulting spend, and freeing skilled risk teams from repetitive manual work.
Yet many institutions remain trapped on the wrong side of the GenAI Divide. MIT shows that internal builds fail twice as often as external partnerships, and Experian highlights the high cost and skills shortage banks face when trying to maintain homegrown tools. Despite this evidence, many organizations continue to invest in internal systems or cling to legacy providers that cannot deliver modern, learning-capable, customizable solutions.
Crossing the GenAI Divide requires a shift. Banks that modernize their approach to Model Risk Management and establish a strategic relationship with a vendor will accelerate AI adoption, strengthen compliance, and do so in a way that regulators, customers, and employees can trust.
The Bottom Line
The MIT and Experian studies should serve as a wake-up call: without the proper focus, your AI project is far more likely to fail than succeed. But failure isn’t inevitable. By modernizing Model Risk Management—embedding automation, transparency, and trust at the core—banks can cross the GenAI Divide and achieve measurable business impact.
At ValidMind, we work every day to ensure our customers don’t end up in the 95% that fail. Instead, we help them build trustworthy, scalable AI solutions that deliver on their promises.
AI is too important to get wrong. Let’s make sure it doesn’t become another failed experiment.
