InsideAnalysis

LLMs are remarkably powerful at language and open-world knowledge, but that doesn't necessarily make them reliable for quantitative reasoning or analysis of enterprise data. Join this episode of InsideAnalysis as Eric Kavanagh speaks with Pradeep Ravikumar of WoodWide.ai about bridging that gap. Learn more about how WoodWide.ai is working to automate the journey from data to intelligence while keeping data teams in the loop. Hear more about the fundamental differences between language models and structured enterprise data, the importance of quantitative and causal reasoning for use cases such as customer churn and fraud detection, and how organizations can leverage the strengths of LLMs without sacrificing the rigor of their existing data models.

So, they want to slow down? Don't believe your lying sighs! The AI race just caught the yellow flag, or was it black? The LLM CEOs cried for a slowdown. AI "will kill us all," they said. But, how? And are they serious? Check out this episode of InsideAnalysis to hear a candid take on what's really happening. As David Linthicum asked, "Are we being played?" Also joining the fray: the legendary Stephen Klein of Curiouser.ai, and David Sweenor, purveyor of the concisely insightful TinyTechGuides.

AI agents are transforming enterprise technology, creating new challenges around governance, security, observability, and infrastructure. Join this episode of InsideAnalysis as host Eric Kavanagh speaks with Jeremy Snyder, Founder and CEO of FireTail.ai, about governing AI usage and protecting enterprise data, and Debosmit Ray, CEO of DevZero, about optimizing Kubernetes infrastructure for the unique demands of AI agentic workloads. Explore how organizations can build the visibility, control, and infrastructure needed to scale AI safely and efficiently.

How do organizations harness AI without losing human control - and how do they build the data infrastructure needed to make AI truly reliable? Join this episode of InsideAnalysis as Eric Kavanagh speaks with Jon Cheney of GenAIPI about AI-powered entrepreneurship, human agency, AI implementation, and using generative AI to amplify human capabilities. Then, Anshul Verma of Zeta Global discusses data observability, automated pipelines, and self-healing mechanisms designed to improve data reliability at scale. Explore the human and technical foundations of implementing AI - from maintaining human oversight to building reliable data infrastructure for AI-driven workloads.

Promises, promises! We've been told time and again that self-driving cars will save lives. But there's more to safety than crash data. If an autonomous vehicle blocks a fire truck, for example, that's a severe edge case. And there are many others like it. Most people tend to focus more on stories than stats, which causes friction when the self-driving auto industry tries to make its pitch. Join this episode of InsideAnalysis to hear from the legendary Carnegie Melon visionary, Philip Koopman, the man who helped write the safety standards for self-driving cars. He'll be joined by AI architectural guru Mark Madsen of Third Nature, who's worked with numerous car companies over the years to create their data and analytics products. Attendees will learn: * Why perceptions of safe behavior often belie statistics * How edge cases can undermine traditional testing and engineering * Why crash rates provide an incomplete picture of autonomous safety * How this next generation of vehicles creates an accountability conundrum