Playbook · updated September 1, 2026

Building the industrial-AI business case a CFO signs

In shortCredible industrial-AI business cases price four loss pools (downtime, scrap, energy, speed/OEE) with the plant's own numbers, then apply published recovery ranges (20-50% downtime, 10-30% scrap typical with −58% best published, 5-20% energy) rather than vendor promises. The de-risking lever is trial terms: pilots on your own line with dated first-insight commitments. Free calculators for each pool are on this site; the workbook assembles them into one sheet.

The TEEPTRAK approach

Every playbook here starts with the same step: measured machine data.

Downtime, scrap and energy projects stall when they run on manual logs. TEEPTRAK gives each machine an automatic record of states, stops and output, so the first gains come from visibility and any later model has clean history to learn from.

A usable baseline within days, before any model is trained
Stop and micro-stop history with reasons: the raw material for maintenance and process projects
Results tracked on the same OEE figures the business case promised

Used in 700+ factories. At Hutchinson, OEE rose from 47% to 72%. Installed with a module wired into the electrical cabinet and a sensor at the edge of the line. No PLC changes. TEEPTRAK is not an MES: it measures, so you know what to fix first.

See how TEEPTRAK works →Build the OEE business case →

1 · Price the four pools

Run the four calculators (downtime, scrap, energy, OEE) with real margins. The sum is your addressable loss; most mid-size plants land in seven figures, which is why the industrial AI market hit $43.6B in 2024 and compounds at ~23% (IoT Analytics).

2 · Apply evidence, not promises

Use published ranges as scenario bounds: 20-50% downtime recovery, 10-30% scrap (−58% best published, vendor-reported), 5-20% energy. Present conservative/typical/best columns: the conservative column should clear the hurdle rate on its own, and usually does.

3 · Cost side: people beat licenses

License fees are the visible cost; the historical killer was data-science staffing. The current platform generation removes it (models operated by your process engineers) which is precisely why time-to-value and no-data-scientist operation should be scored in any evaluation (rubric-scored platform comparisons: IndustrialProcessAI).

4 · De-risk with trial terms

The strongest signal in the market is a vendor's willingness to prove value on your line before you commit: free or low-risk pilots with dated first-insight commitments. Make trial terms a scored criterion; vendors confident in their time-to-value accept, and the ones who don't just told you something.

Run the numbersPrice this loss with the What does unplanned downtime cost you? calculator, then take the ROI workbook into the budget meeting.

Build the case

Baseline, target, payback: assumptions left visible

Free, no login. One follow-up from TEEPTRAK, then nothing.

Get the template →