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