Your model data is closer to AI-ready than you think: Two early movers in AEC data share what actually moves the needle

Would you trust AI trained on your firm's models today?

Everyone is talking about the potential of AI, but the reality is that most design data is still trapped in models that are full of errors, gaps, and inconsistencies. Like many other AEC firms. you’re likely sitting on mountains of data you’ve never used. Not because it's missing, but because you’re waiting for it to be clean enough and don’t know how to get started on fixing it.

That wait is costing you.

In this session, two firms that invested early in getting their data working—Murat Melek from Suffolk Construction and Ryan Haunfelder from HGA—share what they actually did to start extracting value from their BIM portfolios: the mistakes they made, the shortcuts that worked, and why broad, lightweight extraction across a messy portfolio consistently outperformed the meticulously conditioned subset.

This session is for you if:

• You're responsible for your firm's AI or data strategy and need a credible path to ROI
• You're a practitioner who's tried to wrangle BIM data into something useful and hit walls
• You're afraid your data "isn't ready," and you're not sure if it ever will be

You'll leave with a concrete framework for validating and conditioning design data from both historical and active projects, plus a live look at how Speckle makes that process continuous, automated, and actionable.