Why ChatGPT is Failing Your Construction Site: 'Infrastructure Misc' is Hard to Quantify
Summary
The article discusses the challenges of implementing AI in construction projects, emphasizing that poor data quality, not technology, often leads to failed AI initiatives.
Why It Matters
As the construction industry increasingly turns to AI for efficiency, understanding the foundational data issues is crucial. This article highlights the gap between AI expectations and the reality of data management, urging firms to address their data before adopting new technologies.
Key Takeaways
- AI pilots in construction often fail due to poor data quality.
- Data inconsistencies and formats hinder AI effectiveness.
- Firms must prioritize data organization before implementing AI solutions.
IdeasViewpoint Technology First Read Why ChatGPT is Failing Your Construction Site: 'Infrastructure Misc' is Hard to Quantify The AI tools everyone is excited about were never built for project data. Until firms fix what’s underneath, the pilots will keep stalling. By Alex Ryan Getty Images Among the hazards to using AI without verifying its accuracy is that a general contractor or owner picks the wrong winner in a bid competition. Photo: BestForBest/Getty Images February 25, 2026 Ryan Last fall, a mid-size civil engineering firm I work with tried something that sounded great on paper. They wanted to feed two years of project closeout reports into a large language model and have it surface patterns in cost overruns, schedule slips, change orders. The kind of institutional knowledge that usually disappears when a senior PM retires or moves on.They spent six weeks on it. Burned through about $40,000. Then killed the whole thing. The technology wasn’t the problem. The data was. Their closeout reports were spread across three different formats, living on two SharePoint sites and a shared network drive nobody had bothered to migrate. Half the PDF files were scanned images with no searchable text at all. Naming conventions had changed twice in 18 months, so the model couldn’t distinguish a $2-million highway interchange from a $200,000 drainage repair. Both showed up as “Infrastructure — Misc.” I’ve been consulting on AI and data strategy for AEC and manufacturing firms for abou...
