Only 10% of financial institutions say their data is well-governed, accurate, and available in real time.
For banks moving from AI experimentation to production, better models are not enough. They need a trusted data foundation that standardizes business logic, applies governance consistently, and gives both users and AI systems the same understanding of critical metrics.
78% report moderate or major challenges caused by the lack of a single source of data truth.
74% cite poor data quality as a significant barrier.
71% say legacy systems limit data integration.
This whitepaper, based on research among 101 financial institution data and technology leaders, explains how to design an AI-ready semantic layer that connects fragmented data environments with governed, context-aware AI.
