Data Problems Slow AI Adoption in FinanceNearly half of financial institutions identified securing data, insufficient data, integration challenges, data quality and trust in outputs as significant barriers to preparing data for AI projects. Financial institutions typically have enormous volumes of data, but it’s fragmented across systems that don’t usually work together, Blanco says: “The most common issues are inconsistent labeling, incomplete data lineage and governance frameworks built for compliance reporting rather than AI model training.” Indranil Bandyopadhyay, a principal analyst at Forrester, says firms often overestimate how prepared their data environments are for AI, particularly as generative AI introduces new requirements related to context, semantics and unstructured data. “That data was prepared for human consumption, not for AI systems that rely on technologies such as vector databases and multimodal data platforms,” he says. Bandyopadhyay also notes that AI systems require continuous oversight because they are probabilistic in nature, creating risks for model drift and data drift, meaning organizations cannot simply build once and then move to the next thing.