David Taylor
Chief Commercial Officer
Jan 26, 2026
AI is failing in capital projects because we’re treating it like software, not infrastructure.
Across the industry, organizations are buying AI tools the same way they buy scheduling add-ons or reporting plug-ins: install it, connect a few data sources, and expect insight to appear. The demos look impressive. The pilots show promise. And then… nothing scales.
The problem isn’t the algorithms. It’s the wiring.
AI isn’t a power tool you plug in and start using. It’s electricity. Without a properly designed and connected infrastructure, the switch can be on and the lights still won’t turn on. In capital projects, that infrastructure is data — how it’s structured, governed, connected, and made available across the portfolio.
Most AI initiatives fail because they’re isolated by design. They’re trained on narrow data sets, dependent on manual data prep, and disconnected from the systems that actually run projects. Schedule data lives in one place. Cost data in another. Risk, progress, and change management somewhere else entirely. AI ends up analyzing fragments instead of reality.
That’s why results stall.
When AI only sees part of the picture, it can’t detect early signals. It can’t understand cause and effect across schedule, cost, and risk. And it certainly can’t support enterprise-level decisions. At best, it becomes a clever point solution. At worst, it produces confident answers that leadership shouldn’t trust.
The uncomfortable truth is this: most project organizations are trying to power advanced analytics on an extension cord.
That’s missing is a unified data foundation built specifically for capital projects — one that treats project data as infrastructure, not exhaust. That means consistent data models, governed integrations, and a single way of representing project performance across systems and programs.
This is where the Unified Project Platform (UPP) plays a critical role.
UPP doesn’t compete with AI tools. It makes them viable. By connecting schedule, cost, risk, and performance data into a standardized, enterprise-ready foundation, UPP becomes the wiring that allows AI to operate across the entire portfolio — not just inside a sandbox.
When the infrastructure is right, AI stops being experimental. Early warnings become earlier. Forecasts become more reliable. Insights stop requiring explanation. And leadership can finally trust what they’re seeing without asking where the numbers came from.
The industry is racing toward AI-driven project controls. That’s inevitable. But the winners won’t be the ones with the flashiest tools. They’ll be the ones who invested in the unglamorous work first — building the infrastructure that makes intelligence possible.
Before buying the next AI solution, ask a simpler question: Do you actually have the wiring in place to turn it on?