The ROI of decision intelligence is the value of better decisions minus the cost of the program: annual value = decisions per year × improvement per decision × value of that improvement. Measure it against a baseline agreed in advance, run the new decision in shadow mode or with a holdout group, and count only outcomes you can attribute — downtime avoided, scrap prevented, revenue protected, late deliveries avoided, staff time returned or energy saved.
The formula
Decisions per year × improvement per decision × value per unit of improvement
− annual program cost = net value · value ÷ cost = ROI multiple · cost ÷ weekly value = payback
Every use case in the calculator is this formula with different words. Predictive maintenance: downtime hours × cost per hour × share avoided. Quality: production value × scrap rate × share prevented. The discipline is in the inputs — and in agreeing them with finance before the pilot, not after.
How to measure it so finance believes it
- Agree the baseline firstLast 6–12 months of the metric the decision moves: downtime hours, scrap rate, late deliveries, handling time. Signed off before anything changes.
- Run in shadow modeThe platform recommends; people decide as usual. Compare what it would have done with what happened — no risk, real evidence.
- Use a holdoutApply the new decision to some lines, sites or queues and not others. The difference is the effect, net of seasonality and luck.
- Count only attributable outcomesTie each dollar to a decision record: signal, options, choice, owner, outcome. No record, no credit.
- Include the full costPlatform, build, data work, change management and run cost — then report ROI, net value and payback together.
- Keep measuringDecision observability keeps tracking outcome versus expected, so ROI is a live number, not a launch slide.
Use cases and the parameters to collect
| Use case | Value driver | Parameters to collect | Where the data lives |
|---|---|---|---|
| Predictive maintenance | Unplanned downtime avoided | Downtime hours per asset, cost per hour, share avoidable | Historian, CMMS/EAM, production plan |
| Quality & scrap | Scrap and rework prevented | Production value, scrap + rework rate, share preventable upstream | MES, QMS, vision and SPC data |
| Supply-chain exceptions | Revenue protected | Exceptions per month, revenue at risk each, share saved by a faster call | ERP, supplier portals, order book |
| Logistics rerouting | Late-delivery cost avoided | Late deliveries per month, cost each (penalty, expedite, churn), share preventable | TMS, telematics, customer SLAs |
| Healthcare & back-office | Staff time returned | Cases per month, minutes saved each, loaded hourly cost, automatable share | EHR/claims systems, work queues, HR cost |
| Energy | Energy cost avoided | Annual energy spend, share saved by scheduling and set-points | Utility bills, meters, BMS/SCADA |
What the research says
Downtime is the biggest line
Siemens’ True Cost of Downtime 2024 estimates the world’s 500 largest companies lose about $1.4 trillion a year to unplanned downtime — around 11% of revenue — with an idle line in a major automotive plant costing up to $2.3 million an hour.
Predictive maintenance moves it
McKinsey reports predictive maintenance typically reduces machine downtime by 30–50% and increases machine life by 20–40%; in one surfactants plant, production losses fell 58% and maintenance cost 79%.
Analytics compounds
McKinsey also reports combined advanced-analytics programs in process industries delivering EBITDA margin improvements of as much as five to ten percentage points.
Use the conservative end
Benchmarks set a ceiling, not a promise. Size the business case at the low end, then let the holdout show the real number.
Decision KPIs — the leading indicators
| KPI | What it tells you | Good direction |
|---|---|---|
| Decision latency | Time from signal to action | Down |
| Adoption and override rate | Whether people trust the recommendation — and where they don’t | Adoption up; overrides explained |
| Confidence calibration | Whether 80% confidence is right 80% of the time | Predicted ≈ actual |
| Outcome vs expected | Whether decisions deliver what they promised | Gap closing |
| Share executed in policy | Whether autonomy is working inside its limits | Up, with zero breaches |
- Measure decisions, not models.
- Agree the baseline with finance before the pilot.
- Shadow mode and holdouts turn claims into evidence.