Colin Zima

Using Databricks and Omni, Condé Nast encoded business logic into a governed semantic layer and trained the system on how teams define performance, interpret metrics, and make decisions. Built on a rigorous AI orchestration framework, AI now generates campaign takeaways, surfaces performance drivers, and automatically guides recommendations across teams. This session explores the interplay between analytics and AI and the requirements for moving a solution into production.

Chat interfaces and coding agents are quickly becoming the primary way people consume analytics inside organizations. The new world lowers the barrier to access and shortens the path from question to insight. But what does this mean for how data teams operate? Data leaders share how they’re adapting, how teams across the business are using AI to get answers, how data leaders build trust in AI over time, and what iterative feedback and tuning look like when AI becomes the front door to your data. AI may be the new interface, but dashboards, models and even Excel still matter. The challenge is connecting these foundations to AI without losing trust.

In this session, Omni CEO Colin Zima and VP of Product Arielle Strong will share how early experiments led to AI features our customers actually use and love: from natural language chat, to embeddable AI products, to APIs and an MCP server. They’ll walk through what worked, what didn’t, and how AI has reshaped our product roadmap. Expect real-world examples of AI analytics in production, along with best practices for getting your data AI-ready.

The semantic layer is essential, and it must be vendor-neutral and seamlessly integrate with your existing tech stack. Data modeling may be everything, broken or completely transformed by AI — perhaps all of these are true. In this session, Pedram Navid, Head of DevRel + Marketing at Dagster Labs, and Colin Zima, CEO at Omni, will have a candid discussion about the state of the semantic layer — diving into what we should keep, what we should throw out, and what we should bet on for the future of data modeling.