Source: Rajat Gangrade
The problem isn’t a lack of data. It’s fragmentation.
Geotechnical reports in PDFs. Designs in BIM. Cost data in spreadsheets. Schedule in Procore. Each silo is optimised individually, none talking to each other, and now we are looking to solve this by adding “AI analysis” to each silo.
That’s optimising the wrong problem.
We’re asking the wrong question about AI. Instead of “How do we add AI to existing processes?” ask “How do we redesign workflows around what AI enables?”
Right now, we’re rushing to automate tasks: faster clash detection, quicker takeoffs, automated compliance. This just makes fragmented workflows slightly faster.
The problem: Every “AI-powered” feature gets commoditised as foundation models improve. Your competitive advantage evaporates every 18 months.
There’s a different playbook.
In Reshuffle, Sangeet Paul describes how containerisation transformed global trade, not by making shipping faster, but by unbundling and rebundling the entire workflow.
Before containers, cargo was packed/unpacked manually at every port. Containerisation unpacked the process by separating cargo handling from transport, storage, and documentation. Then we repacked everything around the standardised container.
The result wasn’t 20% faster shipping. It was the foundation of global supply chains.
AI needs that same mindset in construction.
Right now, two trends are forming:
Path 1: Adding AI features
What AI researcher Rich Sutton calls “the bitter lesson.” Companies that encode human expertise get short-term wins but lose to those building learning systems leveraging computation at scale. These get outdated as models improve.
Path 2: Rethinking workflows
Instead of just adding AI, companies that focus on what models need (like training data and infrastructure) or find new ways to work with it will lead the way.
Here’s what it looks like in action:
Unpack: Break down “coordination.” Design intent in heads, decisions in meeting notes, models in isolated tools, consensus through endless meetings.
Repack: Design with AI capabilities in mind: coordination that doesn’t hinge on getting everyone to agree first.
Instead of months of negotiating standards:
– Teams model in their preferred tools
– AI translates between formats as a semantic layer
– Design intent captured in machine-readable format
– Real-time conflict resolution
– Coordination emerges from continuous learning, not consensus-building
It’s coordination as a continuous learning system.