Every industrial AI vendor, including us, will show you a savings number. The number that matters more is the one underneath it: what does it cost to reach that savings, and how long does it take to get there?
Start with the cost of the status quo
Unplanned downtime cost is usually understated because most sites only track the direct cost — the line stoppage — and miss the surrounding cost: expedited parts, overtime for the repair crew, and the schedule ripple across downstream stations. A more complete number is: (downtime hours × fully loaded cost per hour) + (average expedite premium × incidents per month).
Then be honest about recovery rates
No monitoring system recovers 100% of unplanned downtime. In practice, well-instrumented predictive maintenance programs recover somewhere between 30% and 55% of unplanned downtime cost in the first year, with the number climbing as the model's baseline matures. Vendors that promise higher than that in year one are usually counting savings that were already achievable through basic scheduled maintenance.
The part everyone forgets: implementation drag
The real payback period isn't calculation-start to calculation-end — it's contract-signed to model-trusted. Most sites spend 2–4 weeks on instrumentation and another 2–4 weeks letting the model build a baseline before its first recommendation is trustworthy enough to act on. A payback estimate that ignores this ramp will always look better on paper than it does on your floor.
A simple way to sanity-check any vendor's number
Take their promised annual savings, divide by 12, and compare that monthly figure to your platform cost per month. If the ratio implies payback in under a month, ask what recovery rate they assumed — if it's above 60% in year one, that's the number to push back on. Our own Automation Payback Estimator defaults to a 42% downtime recovery rate and a 24% labor recovery rate, which reflects what we've seen hold up across real deployments, not a best-case scenario.