The Value Translation Gap: AI's Deployment Problem

In this episode of The Edge, we sit down with Eric Siegel, a 30-year machine learning veteran and founder of Gooder AI, to discuss the critical challenges enterprises face in deploying predictive AI models. Episode Highlights: The Deployment Problem Introduction to the "Value Translation Gap" in enterprise AI Why only 15-20% of predictive models reach production The four critical predictions businesses rely on: who will click, buy, lie, or die Why Models Fail The "metrics mirage" problem in AI deployment Understanding the workflow-reality gap Scale challenges in moving from pilot to production Implementation costs (26%) and ROI translation (18%) as key barriers BizML Framework Three essential concepts for business stakeholders: What's being predicted How well it predicts What actions those predictions drive Translating technical metrics into business outcomes The Future of AI Products Evolution from consulting to product-based solutions The importance of domain-specific architectures How successful companies embed business logic into ML pipelines Investment Opportunities Value Translation Tools Vertical Solutions Deployment Frameworks The shift from model development to value realization Featured Guest: Eric Siegel, Founder of Gooder AI and machine learning veteran