Description
Every firm is piloting AI. Most pilots stall for the same reason, and it isn't the model. It's the data. Decades of drawings, RFIs, specs, and contracts sit scattered across servers, inboxes, and forgotten project folders. The firms winning with AI in 2026 treat project history as an asset. They make it findable, permissioned, and trusted before pointing AI at it. This talk shows how to fix the data layer first: what to organize, how to govern access, and how to build the foundation that makes AI accurate and safe. You'll leave with fresh insights on how to turn scattered project data into firm knowledge your teams, and your AI, can actually use.
Principaux enseignements
- Identify why most AEC AI pilots stall at the data layer instead of the model.
- Assess your firm's project data for findability, permissions, and trust before deploying AI.
- Build a governance and access framework that makes project history ready for AI.
- Prioritize which data problems to solve first to see faster, safer AI results.
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