As organizations race to adopt AI agents, Jon Krohn is examining a fundamental challenge that could determine whether those systems actually deliver value: the quality of the data behind them. In a new episode of the Super Data Science podcast, Krohn sits down with Salesforce’s Gaurav Pathak to explore why powerful AI models alone aren’t enough to build reliable enterprise AI.
Pathak describes the emerging problem as “garbage in, gospel out”—when an AI agent receives unreliable data but presents its resulting answer with confidence. The conversation explores why metadata and context have become increasingly important in the agentic AI era, how organizations can establish stronger data-quality practices, and why failures that once sparked obvious disagreements among employees can become harder to detect when automated systems confidently deliver a single answer. They also discuss the changing ROI case for data management and the skills AI engineers need as organizations deploy increasingly autonomous systems.
As a leading voice in artificial intelligence, machine learning, and data science, Jon Krohn helps audiences move beyond AI hype to understand the technologies, strategies, and skills shaping the future of business. His ability to translate highly technical developments into practical insights gives leaders a clearer understanding of how to approach AI adoption and create meaningful value from emerging technologies.
To learn more about hosting Jon Krohn, contact WWSG.
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