In Conversation with Eric Siegel: The Business Case for Predictive AI

In this episode of A Tale of Two AIs, host Razi Raziuddin sits down with Eric Siegel, CEO of Gooder AI and founder of Machine Learning Week, to unpack one of the most persistent challenges in enterprise AI: why so many machine learning models never make it to production.

Eric has spent decades helping organizations bridge the gap between technical performance and business impact in AI. As the author of Predictive Analytics and The AI Playbook, and a longtime advisor to enterprise data science teams, he has a front-row seat to what actually determines whether AI creates value, or quietly stalls.

The conversation centers on a hard truth: building a technically sound model is not the same as delivering business value. And in many organizations, that disconnect is still among the biggest bottlenecks in scaling predictive AI.

Evaluation vs. Valuation

Critical to the conversation is the difference between machine learning evaluation and valuation.

Most data science teams are fluent in technical metrics to evaluate their models, but few are able to properly measure and communicate the business value tied to those models. 

Eric argues that until predictive AI projects are framed explicitly in monetary or KPI terms before deployment, they struggle to gain internal traction. The model may be statistically strong, but if its impact isn’t translated into business terms, it won’t be prioritized.

Has GenAI Fixed Anything Fundamental?

While GenAI has accelerated experimentation and lowered barriers to building intelligent interfaces, it hasn’t eliminated the need for probabilistic decision-making. Enterprises still rely on predictions to triage fraud, prioritize leads, forecast churn, and manage risk.

Eric makes a compelling case that predictive AI isn’t being replaced. Instead, it’s poised to enter a new phase: predictive models as a reliability layer for generative systems.

Instead of asking whether an agent can handle every scenario, organizations can use predictive models to assess risk and route high-risk cases to humans. That shift can unlock meaningful automation while managing downside risk.

The Organizational Bottleneck

Beyond technology, the conversation returns repeatedly to organizational alignment.

When predictive AI initiatives fail to scale, it’s often because business and technical teams aren’t collaborating end-to-end. Projects are defined by stakeholders, handed to data science, and revisited only at the end.

But successful AI programs require shared ownership:

• Shared metrics
• Shared definitions of value
• Shared accountability for deployment

Without that alignment, even strong models can stall or veer off course. 

The Next Five Years

Looking ahead, Eric predicts a renewed focus on predictive AI, driven by advances in tabular foundation models, growing recognition of GenAI’s limitations, and the increasing need for reliability in agentic systems.

The core principle he leaves listeners with is simple: enterprises run on uncertainty, and uncertainty requires probabilities.

Generative systems may generate language and actions, but predictive systems provide the probabilistic backbone that makes large-scale operational decisions possible.