Gaurav Patole

As organisations accelerate the adoption of Agentic AI, governance has become more than a compliance exercise—it is now the foundation for building AI systems that are trusted, scalable and capable of delivering real business value. But as AI moves from experimentation to enterprise-wide deployment, are today's governance models still fit for purpose? This panel brings together leading experts in data governance, AI governance, analytics and enterprise data strategy; to explore how organisations can build trust by design. From human oversight and data quality to semantics, context and organisational culture; the discussion will examine what it really takes to govern AI in complex enterprises. Attendees will gain practical insights into balancing innovation with accountability, enabling AI through governance rather than restricting it, and creating the conditions for trustworthy decision-making at scale. Key Takeaways: • Understand how governance must evolve to support Agentic AI, moving beyond compliance to become a strategic enabler of innovation. • Explore the role of trust, context and data quality in creating AI systems that are reliable, explainable and fit for enterprise use. • Learn practical approaches to scaling AI responsibly, balancing human expertise, governance and business value in complex organisations.

Every agentic AI system depends on one thing: context; the layer that makes an agent act the way a human actually intended. Get it right, and AI becomes a trusted colleague. Get it wrong, and it confidently does the wrong thing at scale. But there is a question nobody seems able to answer cleanly: where is this context? We've largely solved the technology problem: the platforms, semantic layers and integrations. And yet enterprise AI still stumbles on context. Why? Because context isn't just something you capture and store. It's something you have to assemble, curate and keep true. This session discusses some uncomfortable questions: Where is the context? How do we know that what gets captured is correct and stays that way as systems are sunset, data is migrated and meaning quietly drifts? Maybe the solution demands that every department stop being a passive "data consumer" and become an active "context provider", turning our trusted workforce into curators of the organisation's collective intelligence. Join authors Gaurav Patole (Thoughtworks) and Ole Olesen-Bagneux (Actian) for a candid conversation on enterprise AI's most over-discussed ingredient and how to hold on to it before it slips away.