About this episode
The episode discusses building enterprise-grade agentic AI programs and the challenges associated with their implementation.
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…
People in this episode
Host: Michael Marchuk
Guest: Tim Shea
Topics covered
- Agentic AI
- AI in Production
- Enterprise-grade AI
- Analytics Workflow
- Org Design
- Upskilling
- Business Outcomes
Keywords
- Agentic AI
- LLMs
- Data Quality
- Automation
- Business Outcomes
- Analytics
- Org Design
- Upskilling
Mentioned in this episode
Organizations: Latticework Insights, SS&C Blue Prism
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