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