Define the operating event
Inventory exception reporting creates value when AI helps planners find shortages, stale stock, late signals, and replenishment conflicts sooner. McKinsey State of AI 2025 is relevant because it ties AI value to redesigned workflows and scaled operating practices, not isolated pilots. The first ROI question is what operating event should change: cycle time, exception rate, rework, decision quality, or downstream handoff speed.
IBM Institute for Business Value AI capabilities research supports the same measurement discipline from a capability lens. Data quality, operating model, adoption, and measurement all have to be present before a workflow ROI claim is credible.
Measure exceptions before claiming savings
NIST AI Risk Management Framework gives the risk-management structure: map the use case, measure failure modes, manage controls, and govern accountability. For inventory exception reporting, the ROI model should count exceptions, review effort, overrides, and quality misses before claiming productivity improvement.
Microsoft 365 Copilot data protection architecture matters because many workflows draw context from email, documents, collaboration spaces, CRM exports, and shared drives. Permission cleanup, data freshness, and auditability belong in the ROI model because weak controls can erase the value case.
Use a stop-or-scale decision
McKinsey State of AI 2025 is relevant here because its AI research identifies manufacturing and IT as functions where respondents commonly report use-case-level cost benefits. The first production test should produce a baseline, acceptance threshold, owner for benefits realization, and a decision cadence. If the workflow does not improve the operating metric, the correct outcome is to change the scope or stop the pilot.
Use the AI ROI Calculator, the AI Opportunity Score, and Human Renaissance AI transformation services to turn the ROI model into a managed operating decision.
