Works from CSV or workbook exports produced from SAP, IBM Maximo, Oracle ERP, Hexagon EAM, Infor, and other ERP, EAM, or CMMS systems. No direct integration or write-back is required — Review data requirements →
Industrial IQ Benchmarks

Assumption discipline before decision-making.

Benchmarks guide early qualification. Diagnostic runs replace assumptions with mapped source evidence, confidence tiers, and score snapshots.

Evidence summary

Industrial IQ capability guide

Industrial IQ Benchmarks: This Industrial IQ capability guide explains the operating question, evidence model, review boundary, and next action for the relevant platform workflow. Industrial IQ Benchmarks: AI2COE guidance for MRO catalog quality, ERP data readiness, assumption-bound value review, and governed industrial AI adoption.

Run Free Industrial IQ Snapshot
Review ownerIndustrial IQ evaluators, operating sponsors, data owners, and governance reviewers
Input file contextThe platform records, source exports, mappings, reports, score history, actions, and review context relevant to this capability.
Diagnostic evidenceA platform-specific view of how Industrial IQ converts exported data into evidence, reports, governed actions, and recurring visibility.
Recommended actionUse the page to understand the capability, then run Industrial IQ Snapshot or inspect sample reports.
Benchmark governance

Use assumptions to qualify the question, not to claim the result.

Industrial IQ benchmark pages exist to help CFO, COO, CIO, procurement, maintenance, and data leaders frame a diagnostic review before private uploads. The benchmark layer explains what a signal could mean, what source data would be needed to prove it, and which Industrial IQ engine should run first.

Customer-specific findings require uploaded operational data, field mapping, diagnostic scoring, confidence-tiered evidence, and human review. That boundary keeps early planning useful without implying realized savings or unsupported operational outcomes.

Trust boundary: no ERP write-back, no autonomous remediation, source-file purge after report generation, and human review before action remain the control model for moving from benchmark assumption to diagnostic evidence.
Planning boundary

Benchmarks are not customer proof

Benchmark assumptions help a buyer decide whether a diagnostic is worth running. They are replaced by uploaded-data evidence once source files are mapped and reviewed.

Finance discipline

Exposure needs evidence

Working-capital, inventory, and procurement estimates stay explicitly labeled until FinanceMind AI, InventoryMind AI, or ProcureMind AI can trace the signal to exported operational data.

Operational discipline

Risk needs owner review

Downtime, false-stockout, and maintenance-readiness indicators are routed into confidence tiers and review actions before a buyer treats them as operational priorities.

EngineScoreClaims discipline
PartsCleanse AICatalog health scoreUploaded-data result when available; sample and benchmark assumptions are labeled.
InventoryMind AIInventory health scoreUploaded-data result when available; sample and benchmark assumptions are labeled.
ProcureMind AIProcurement leakage scoreUploaded-data result when available; sample and benchmark assumptions are labeled.
FinanceMind AIWorking capital scoreUploaded-data result when available; sample and benchmark assumptions are labeled.
AssetMind AIAsset intelligence scoreUploaded-data result when available; sample and benchmark assumptions are labeled.
ReliabilityMind AIMaintenance readiness scoreUploaded-data result when available; sample and benchmark assumptions are labeled.
ReadyMind AIAI readiness scoreUploaded-data result when available; sample and benchmark assumptions are labeled.
GovernanceMind AIGovernance readiness scoreUploaded-data result when available; sample and benchmark assumptions are labeled.