Verified Data is the Missing Piece of Agentic Infrastructure With Gary Kotovets (Chief Data & Analytics Officer at Dun & Bradstreet)
In this episode of AI Explained, we are joined by Gary Kotovets, Chief Data & Analytics Officer at Dun & Bradstreet, where he leads data, analytics, and AI strategy across the company's commercial graph of 650-plus million businesses. Before this role, he spent nearly two decades at Bloomberg as global head of data acquisition and management, building the rigor around data quality that now shapes how he thinks about agentic AI. Gary breaks down what it takes to make enterprise data agent-ready — lineage and provenance, roughly 100 billion data quality checks run against source data, and a strict internal policy that every AI-generated answer must be able to show where it came from. He and Krishna dig into why hallucination is a model problem rather than a data problem at D&B, how a shared tools library with built-in rules keeps agents from drifting off script as workflows move from single-turn chat to multi-step autonomous tasks, and where governance, small language models, and agent-to-agent interactions are headed over the next few years. They close with a rapid-fire round covering data versus models, RAG versus fine-tuning, and the most overhyped and underrated ideas in enterprise AI right now.
