AI adoption is everywhere — but results aren’t. A recent New York Times report found that most companies see little to no business impact from their AI investments, with abandoned pilots and untrustworthy outputs stalling progress. In financial services, where compliance, accuracy, and time-to-market are critical, the stakes couldn’t be higher.
We’ll explore why so many AI initiatives fail to move past proof-of-concept and how industry leaders are using MongoDB Atlas on Google Cloud to change the game — unifying operational, vector, and unstructured data in one secure platform, grounding Vertex AI in fresh, regulated data, and delivering applications that are accurate, explainable, and regulator-ready. Expect candid conversations, peer-tested strategies, and practical frameworks to move from hype to high-value AI in fraud prevention, lending, real-time payments, and personalized banking.
Many AI pilots in financial services struggle to move beyond experimentation, often hindered by fragmented systems, stale data, and regulatory hurdles. Success requires real-time, compliant data pipelines that ground AI in fresh, auditable information to reduce risks like hallucinations in high-stakes areas such as fraud detection or lending. By unifying data and embedding AI into core operations—whether for AML monitoring, customer personalization, or scaling enterprise-grade deployments—organizations can avoid disillusionment, build trust, and achieve sustained ROI from their AI investments.
