In this episode, host Michael Marchuk speaks with Tim Shea, founder and CEO of Latticework Insights, about what it takes to build an enterprise-grade agentic AI program that delivers measurable value. Shea explains that while LLMs are powerful, hallucinations, non-determinism, and high-stakes risk make many agentic initiatives fail without human checks, clear leadership, and strong operating models. He outlines how to decide when a use case truly needs agentic AI versus deterministic automation, using an analytics workflow (extraction, modeling, interpretation, evaluation) to show where agents help most. Shea shares a restaurant-chain example where an agent rapidly replicated unit tests across pipelines to improve data quality and executive confidence. He warns against rushing, token-burning for its own sake, and losing focus on business outcomes, emphasizing upskilling and domain expertise as key differentiators over the next 12–18 months.

What we talked to Tim about:

-What Agentic AI Means

-Why AI Fails in Production

-When You Need Agents

-Agent Roles in Analytics

-Managing Agent Swarms

-Org Design and Upskilling

-Measuring Real Outcomes

-Common Adoption Mistakes

-Workflow Shifts and Tools

-High Stakes Industries Limits

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