What’s getting in the way of AI moving into production?
Hi everyone! A lot of companies are experimenting with AI. Fewer are getting those projects into production. This Data Wire article looks at what can slow that down in financial services, including data readiness, governance, orchestration, and resilience. What are you seeing? What’s the biggest hurdle between an AI pilot and production? Would love to hear what others are seeing in the field.Building the data foundations for the era of AI-powered banking
Everpure sponsored MoneyLIVE North America in Chicago on September 14 and 15. Mike Russo and Richard Galvez co-hosted a roundtable on building the data foundations for the era of AI-powered banking. Richard also joined a panel on whether data and AI can be harnessed effectively against fraud. Both sessions circled the same constraint. In banking, the hard part is rarely the model. It is whether the data underneath it is governed, current and trustworthy enough to put in front of a customer or a regulator. What is holding up your first production use case, the model or the data under it?AI in Production: What Does “It Works” Mean?
Would love your take on this Data Wire piece with MikeRusso and Ricard Galvez on moving AI from pilot into production in capital markets. One question from Richard really stuck with me: what does “it works” actually mean? A model can pass its evaluation and still not make the work better, faster, or less expensive. Pilots measure whether the model performs. Production has to measure whether the work improved. That second test is harder, and it often does not have a clear owner. So I’m curious: on your team, what does “it works” mean for AI?
