Start with the decision, not the model
Most AI projects in heavy industry fail for the same reason: they start with a model looking for a problem. The teams that get value start somewhere else — with a specific operational decision that is being made badly, slowly, or too late.
If you can name the decision, name who makes it, and name what it costs when they get it wrong, you have a candidate for automation. If you can't, no model will save you.
Where it pays off
In oil & gas, utilities, and logistics, the wins cluster around a few shapes:
- Anomaly detection on telemetry — flagging the asset that's about to fail before it does, instead of after.
- Forecasting under uncertainty — demand, outages, restoration windows — where a slightly better estimate compounds across thousands of events.
- Document and data extraction — turning unstructured operational paperwork into structured, queryable data.
Where it doesn't
AI quietly underdelivers when the decision is rare, the cost of error is low, or the data is thin and messy. In those cases a clear dashboard and a well-designed workflow beat a model every time — and cost a fraction to build and run.
How we approach it
We scope the decision first, prove value on a narrow slice, and only then invest in the pipeline behind it. That keeps the failure cost low and the time-to-value short — which is the whole point.