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
