Forbes advice for industrial AI starts with the data on the factory floor

Industrial AI may depend less on a cleverer model than on what technicians already know about their machines. MaintainX chief executive Chris Turlica argues that firms should start by making front-line maintenance knowledge usable, with practical steps for doing so.

Watch Desk analysis

What happened

In a Forbes Technology Council essay, Turlica says repair logs, work orders and technicians’ experience can hold valuable operational data, but often remain scattered across paper records and disconnected tools. His recommendations include assigning one accountable owner, standardising terms before digitising records, auditing what data exists, deciding who owns it and which decisions AI may recommend, and setting a measurable baseline before deployment. Read Turlica’s essay in Forbes.

For rollout, he advises starting with a 60-to-90-day pilot rather than launching everywhere at once. He also suggests making data capture quick, cleaning new records without waiting for perfect historical data, and connecting maintenance systems to tools such as inventory and resource planning.

Why it matters

This is concrete advice about a familiar AI bottleneck: a model cannot make much of operational information that was never recorded, or cannot be found. Turlica’s checklist gives industrial teams a place to start before they buy another system or announce another grand transformation.

Our read

The useful bit is the order of operations: decide what matters, who owns it and how success will be measured before inviting software to solve the problem. A technician’s knowledge is not a dataset until someone captures it, and a 40-field form is a fine way to ensure nobody does.

Turlica writes as a technology vendor’s chief executive, so his argument also supports the case for modern maintenance tools. Still, the practical suggestions stand on their own: begin with a small scope, make the work easier for staff, and measure whether anything improves.

What to watch

• Whether industrial teams adopt small pilots and publish results against a baseline.
• How firms protect technician knowledge while explaining what captured data will be used for.
• Whether maintenance records can be connected to other operational systems without rebuilding the same silos.

Discussion spark: Should factories prioritise capturing technicians’ practical knowledge for AI, even if it means changing how frontline staff document their work?

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