As enterprises accelerate AI adoption, governance is becoming one of the most important challenges facing data leaders.

In this episode, Apurva Wadodkar, Senior Director and Head of Data and AI at TI Automotive, joins Merav Yuravlivker, Chief Learning Officer at Data Society, to discuss how organizations can build governance structures that support both oversight and innovation.

Wadodkar outlines a practical approach to AI governance built around four pillars: security, privacy, architecture, and legal review. She also explains why enterprises should maintain a central repository of AI initiatives and how architecture governance can prevent duplicated efforts across teams.

The conversation also introduces Wadodkar’s Decision Intelligence Framework, designed to help data organizations focus on improving business decisions rather than simply delivering analytics outputs.

This episode is Part 2 of a three-part series exploring how enterprises are building scalable AI capabilities.