LIVE ROUNDTABLE: June 11th, 12pm ET
If you're like most organizations, you began your AI journey with a specific use case, a defined set of users, and a manageable data footprint. But soon—if it hasn't already—the snowball effect kicks in. More use cases. More business units. More systems, more stakeholders, and suddenly the data layer that worked for one initiative has to work for ten.
That's when the architectural decisions you made early—or didn't make—start to matter enormously.
AI-forward companies have been living in that complexity from day one. They didn't have the option of starting small and figuring out the data layer later. What they've built to solve for context, connectivity, and governance at scale is exactly what enterprises are beginning to grapple with now.
Join three leaders building AI-forward products in production for a candid roundtable on:
Why context—not the model—determines whether AI produces correct answers in production, and what it takes to get it right across fragmented, inconsistently structured enterprise data
How governance stops AI initiatives from reaching production, and what your organization will require before anything goes live at scale
How these teams think about data architecture differently from day one, and why the patterns that worked for traditional software break down for AI
Whether you're building AI features from scratch or scaling what you've already rolled out, this session covers what the early movers learned—and what's coming for everyone else.
