Symposium

Every data leader in the room has felt it: the pressure to ship AI, the promising pilot, and then the quiet disappointment when results don't scale. The culprit isn't your model. It's the data underneath it.

This session introduces the Enterprise Data Maturity Model — a five-stage framework that helps data leaders honestly assess where their organization stands today and build a credible roadmap to AI that actually works. We'll examine why the majority of enterprises are stuck in the early stages, what separates organizations generating real AI ROI from those still running experiments, and why the path to autonomous agentic systems runs directly through structured, governed data. Whether you're defending last year's AI budget or planning the next wave of investment, this session gives you the language, the framework, and the mandate to lead the conversation differently.

What does it actually look like when an agent can be trusted to act? In this session, we'll pull back the curtain on the infrastructure decisions that separate brittle AI experiments from production-grade agentic systems.

Through candid conversation, we'll walk through how organizations are using governed semantic layers, version-controlled transformation logic, and lineage-complete data products to give AI the context it needs to reason reliably. You'll see firsthand how customers have moved from hallucination-prone text-to-SQL to governed conversational analytics — and hear what the journey looked like from the inside. If you've been wondering what "AI-ready data" means in practice, this session answers that question concretely.

There's no shortage of opinions on how to get AI right. There is a shortage of honest conversations about why it's so hard.

This panel brings together data leaders with different approaches, different starting points, and not always the same answers — to debate the decisions that define whether AI initiatives succeed or stall. How do you make the case for foundational investment when other leadership wants to skip ahead? When is governance an enabler and when does it become a bottleneck? And who actually owns AI readiness — data, engineering, or the business?

Expect a candid conversation. Attendees will leave with sharper questions, stronger conviction, and a clearer sense of where they stand.