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.

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.