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.

In this episode of AI Explained, we are joined by Maryam Ashoori, PhD, VP of Product and Engineering for watsonx.governance at IBM, where she leads the teams building IBM's platform for governing AI models and agents across the enterprise. Before this role she headed product for watsonx.ai, led engineering for Lyft's bikes and scooters operations, and spent six years at IBM Research working on emerging technologies including AI and quantum computing.

Maryam breaks governance down into three foundations — visibility, control, and accountability — and explains why enterprises can only govern the AI they can see while shadow AI keeps agents and models out of view. She and Krishna dig into what an AI control plane should actually do (define, implement, enforce, and track controls), why accountability is the top challenge enterprises cite as agent adoption scales, and how third-party risk, business continuity, and an evolving regulatory landscape are reshaping what "in control" means. They close with a rapid-fire round covering copilots vs. autonomous agents, frontier vs. small models, and the one AI belief Maryam has changed her mind about.

In this episode of AI Explained, we are joined by Juhi Parekh, GM of Key Frontier AGI Accounts at Turing. Juhi brings experience across the full AI stack, from applied AI and foundation models to data infrastructure, with prior product roles at Apple, Amazon, Niantic, Spatial, and Samsung Research US, where she focused on commercializing frontier AI.

She explains how Frontier Labs curates hard datasets that maximize information gain rather than raw difficulty, why the sweet spot for reinforcement learning tasks is problems frontier models fail at least 30 percent of the time, and how long-horizon, real-world workflows are pushing agents to take on more complex work. She also shares the usual suspects when agents break in production (inaccurate tool calls, consistency gaps, permissioning, and output format), why training a capable model and building a reliable agent are two different problems, and why the winners will be the organizations that safely expand agent freedom as guardrails improve.