2. THE AI STALL-OUT IN FINANCIAL SERVICES
In our research, only 10% of organizations report that data is easily accessible for AI development, and 30% rate their data accuracy and governance processes as inadequate. Overall, less than one in five leaders (17%) claim that their organization has a high level of data readiness to support their AI ambitions.
Unfortunately, we’re now seeing a common pattern emerging across the financial services industry. Organizations are rushing to adopt AI and roll out new tools, only to discover down the line that they lack the data foundations needed to scale.
As a result of not having a proper roadmap for data readiness, organizations are having to pause their AI plans, reevaluate their approach, and retrospectively undertake data refinement programs. This brings unexpected remediation costs, governance gaps, and an erosion of trust. Investment slows, enthusiasm wanes, and programs lose momentum.
We’ve seen this chain of events play out before within transformation initiatives, in cloud migration, digital transformation, and cybersecurity. Many financial services organizations are in an endless mousetrap war. They run headlong into new technologies to get an advantage. But the reality is they get halfway down the road and realize they’re not set up for success. And then they have to stop and unwind a lot of what they’ve done.
The lesson is clear. Building AI without their right data strategy creates fragility, not strength.
The most successful organizations approach AI maturity deliberately. They recognize that data readiness is a prerequisite for AI readiness, not a subsequent cleanup activity. They understand that solid data governance is not an innovation brake but an innovation accelerator; a mechanism for unlocking scale with confidence.
