How Can Data Leaders Shift from Data-First to Business-First Mindset? With Deepak Jose, Global Data, Analytics and AI leader

Even well-designed algorithms can fail and end up hurting the people they're meant to help. In this episode, Dr. Ben Green, Assistant Professor of Information and Public Policy at the University of Michigan, explains what it takes to build AI that works for humans. He shares what keeps data from being used for good, why AI regulation can't be left to AI companies, and what organizations risk losing when they hand too much of their thinking to AI.

Every leadership team is arguing about which AI model is best, but that debate is a distraction from the real problem. In this episode, Jeff McMillan, founder of McMillanAI and former head of firmwide AI at Morgan Stanley, breaks down the real bottlenecks behind enterprise AI strategy. He shares why fixing your data matters more than picking a model, why your best AI leader is probably already inside your company, and why AI eliminates tasks rather than entire jobs.

Most AI playbooks are built for a workforce tethered to a desk, but many workers are not near a keyboard. In this episode, Beth Miles, Chief AI Officer at Advantage Solutions, breaks down how she's building an AI-ready culture across a distributed workforce of more than 60,000 teammates. She shares why AI literacy has to start with leadership, why redesigning a broken process matters more than simply automating it, and how she measures AI success well beyond adoption metrics.

In this special episode of The Data & AI Chief, host Cindi Howson sits down with three incredible authors to explore what it really takes to lead through the AI era, from scaling innovation to building AI-ready data foundations to designing systems that keep humans at the center.

Semantic layers and ontologies have moved from nice-to-have data modeling tools to the foundational engine required for enterprise AI. In this episode, Raman Tallamraju, Senior Director and Head of Enterprise Data Architecture and Engineering at Vanguard, breaks down how Vanguard is architecting its AI semantic layer to turn scattered institutional knowledge into reliable, agent-ready context. He shares why autonomous agents expose decades of hidden data debt, how to bridge domain-specific definitions like clients versus prospects, and how to balance building a unified semantic layer with a pragmatic, federated data operating model.