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 →
ROI and value realization guide

AI Readiness Value

AI Readiness Value documentation for data required, formula summary, assumptions, limitations, review role, report output, action, and related Industrial IQ diagnostic.

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8Engines
18Industry guides
0ERP write-back
Evidence summary

Industrial IQ documentation

AI Readiness Value: The Industrial IQ documentation center explains source exports, field mapping, report interpretation, no-write-back controls, data lifecycle, and safe next steps for diagnostic adoption. AI Readiness Value documentation for data required, formula summary, assumptions, limitations, review role, report output, action, and related Industrial IQ.

View Required Fields
Review ownerEnterprise evaluators, implementation teams, product champions, and technical reviewers
Input file contextThe documented data files, required fields, optional fields, mapping rules, assumptions, and report artifacts.
Diagnostic evidenceA practical adoption guide that explains how Industrial IQ is used, what it produces, and how recurring value is created.
Recommended actionReview required fields, then start with a sample or uploaded-data Industrial IQ Snapshot.
Value lever

AI Readiness Value

Measures whether ERP, EAM, CMMS, and procurement exports are complete, governed, and usable for the first safe AI use case.

Model summary: Industrial IQ combines uploaded data, source-fit quality, mapped readiness, engine evidence, confidence tier, and human-review status. Formula details are summarized for buyer interpretation, not used as savings guarantees.

Limitations: benchmark and estimate values remain assumptions until a customer-specific uploaded-data diagnostic is run and reviewed.

AI Readiness ValueValue lever
Uploaded dataCustomer-specific diagnostic required
ReadyMind AIRelated diagnostic engine
EstimateAssumption-labeled until reviewed
Benchmark assumptionUploaded evidenceConfidence tierHuman reviewReportable value
Related data

Fields that improve value confidence.

EngineRequired Data FileRequired FieldsOptional FieldsOutput GeneratedMinimum Viable UploadBest Upload
ReadyMind AI ERP export sample Process Name, Data Source Business Unit, Data Owner, Data Quality Score, System Of Record, Automation Readiness, Governance Owner AI readiness score, evidence, report, actions, score history Process Name, Data Source Required fields plus site, value, date, owner, asset, supplier, and criticality context where available.
Buyer Enablement Validation

AI Readiness Value buyer enablement checklist.

Problem solvedTurns an operational data question into source-backed diagnostic evidence.
Who caresCFO, COO, CIO, procurement, maintenance, reliability, ERP, governance, and board stakeholders where relevant.
Data neededCSV exports with stable IDs, descriptions, quantities, values, dates, site, asset, supplier, and owner context where available.
Output deliveredScores, evidence table, confidence tiers, executive report, action tracker items, score history, and governance status.
Value quantifiedWorking capital, carrying cost, emergency premium, dead stock, stockout risk, readiness gaps, or governance risk depending on diagnostic intent.
Decision supportedWhether to act, review, normalize data, escalate findings, fund remediation, or repeat the diagnostic cadence.
Report shared internallyExecutive, CFO, procurement, inventory, readiness, governance, or renewal value report.
Recurring valueBaseline score -> re-upload -> score movement -> action closure -> benchmark comparison -> renewal value report.
Assumptions and limitationsBenchmarks and estimates remain planning context until customer-specific uploaded data is analyzed and reviewed.
Trust controlsNo ERP write-back, evidence traceability, confidence tiers, human review, false-positive control, audit trail, and data-retention boundaries.
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Evaluation resource map

Turn documentation into a guided evaluation path.

Resources and docs should help reviewers move from methodology to templates, proof format, trust review, and pilot decision without hunting through long text blocks.

Download Data Templates
Methodology

Research and framework pages define terms, readiness pillars, and evidence boundaries.

 Required fields

Data templates clarify the source files and fields each diagnostic engine can evaluate.

 Sample outputs

Sample reports show how evidence, scores, confidence tiers, and open findings are presented.

 Review controls

Trust pages explain no ERP write-back, source-file handling, human review, and procurement review language.

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