Financial services AI projects can stall when teams demonstrate a promising model but lack the data, expertise, and operational controls needed to use it reliably in daily work. AI program leaders, CDOs, and transformation sponsors should check those requirements early and choose use cases their organizations can support. A successful pilot is a starting point, but it is not proof that the institution is ready to deploy the solution.

At FIMA 2026, Larry Hunt, Field Chief Data Officer at Ataccama, described a call center initiative that encountered a basic obstacle: the archived operating procedures it needed were unavailable. His experience led to a practical question to ask when assessing proposed use cases: "What are the questions you can answer with that data?" Teams need to establish what their sources can support before committing to an AI application that depends on them.

Another FIMA 2026 discussion described an approach that connects AI intake and experimentation with approved tools, architectural patterns, and ongoing monitoring. This makes production readiness part of the development process rather than a separate approval exercise after the pilot.

Before expanding a pilot, project leaders should confirm that the required data exists, assess its quality, and identify the experts who can validate the results. Assign owners for the application, its data, and its ongoing controls. Then test a limited workflow with clear success measures and review requirements. If a necessary source or capability is missing, narrow the use case or address the issue before increasing the project's scope.

Join financial services data leaders at FIMA 2027 to compare practical approaches to moving AI projects from promising pilots into production.