What data does Industrial IQ need?

Industrial IQ works from CSV exports. Required fields vary by engine, and the docs list minimum viable uploads plus best uploads.

Are ROI results final financial outcomes?

No. ROI pages label benchmark, estimate, assumption, limitation, and customer-specific diagnostic requirements.

Does Industrial IQ write back to ERP?

No. Industrial IQ creates evidence-backed diagnostics, reports, actions, and governance review without ERP write-back.

Responsible AI | AI2COE Industrial IQ Documentation

Industrial IQ documentation

Responsible AI: 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. Responsible AI documentation for AI2COE Industrial IQ buyers, operators, data owners, executives, and governance teams.

View Required Fields

Executive decision context

Responsible AI

Industrial IQ uses explainable diagnostics, labeled samples, transparent assumptions, and human approval controls.

This guide explains what problem is solved, who cares, what data is needed, what output is delivered, what value lever is affected, what report can be shared internally, and what recurring value is created.

UploadCSV export or sample dataset

Source FitRequired fields and data quality

AI MatchColumn aliases and confidence

NormalizeCanonical field preparation

DiagnoseEngine logic and evidence

ReportScore, actions, assumptions

RepeatScore history and renewal value

Required data by engine

Use this table before upload.

Engine	Required Data File	Required Fields	Optional Fields	Output Generated	Minimum Viable Upload	Best Upload
GovernanceMind AI	Findings export	Finding Id, Finding Type	Confidence, Business Impact, Review Status, Reviewer, Source Record, Action Owner	Governance readiness score, evidence, report, actions, score history	Finding Id, Finding Type	Required fields plus site, value, date, owner, asset, supplier, and criticality context where available.

Accepted CSV format

Prepare clean exports before upload.

Use UTF-8 CSV with one header row, one operational record per row, stable identifiers, ISO-style dates where available, numeric values without currency symbols, and separate columns for site, supplier, asset, quantity, value, and owner context.

Common errors: merged header rows, hidden subtotal rows, mixed currencies in one value column, duplicate column names, free-text dates, missing material or asset identifiers, and supplier names embedded inside descriptions.

If fields are missing: Industrial IQ still profiles source fit, but unmapped required fields reduce the AI Match Score, Mapping Readiness Score, diagnostic confidence, and report completeness until the user normalizes the data.

Sample row examples

Minimum viable row patterns.

Catalog Intelligence: Description: 6205-2RS bearing sealed SKF
Inventory Risk Intelligence: Material Id: MAT-100245 | Quantity: 42
Procurement Leakage Intelligence: Po Number: PO_NUMBER | Description: 6205-2RS bearing sealed SKF
Working Capital Intelligence: Material Id: MAT-100245 | Stock Value: 1850.00

Related engines

Which diagnostic should the buyer run next?

Related engine

GovernanceMind AI

Evidence traceability, review-level assignment, owner approval, auditability, and no-write-back governance.

Open documentation

Report anatomy

What the buyer can share internally.

Industrial IQ reports include scope, source-fit score, AI match score, mapping readiness, diagnostic confidence, score components, evidence records, assumptions, limitations, review requirements, action items, and recurring score history.

Responsible AIValue lever

Uploaded dataCustomer-specific diagnostic required

GovernanceMind AIRelated diagnostic engine

EstimateAssumption-labeled until reviewed

Benchmark assumptionUploaded evidenceConfidence tierHuman reviewReportable value

Buyer Enablement Validation

Responsible AI buyer enablement checklist.

Problem solved	Turns an operational data question into source-backed diagnostic evidence.
Who cares	CFO, COO, CIO, procurement, maintenance, reliability, ERP, governance, and board stakeholders where relevant.
Data needed	CSV exports with stable IDs, descriptions, quantities, values, dates, site, asset, supplier, and owner context where available.
Output delivered	Scores, evidence table, confidence tiers, executive report, action tracker items, score history, and governance status.
Value quantified	Working capital, carrying cost, emergency premium, dead stock, stockout risk, readiness gaps, or governance risk depending on diagnostic intent.
Decision supported	Whether to act, review, normalize data, escalate findings, fund remediation, or repeat the diagnostic cadence.
Report shared internally	Executive, CFO, procurement, inventory, readiness, governance, or renewal value report.
Recurring value	Baseline score -> re-upload -> score movement -> action closure -> benchmark comparison -> renewal value report.
Assumptions and limitations	Benchmarks and estimates remain planning context until customer-specific uploaded data is analyzed and reviewed.
Trust controls	No ERP write-back, evidence traceability, confidence tiers, human review, false-positive control, audit trail, and data-retention boundaries.

Run Evidence Governance Intelligence View Required Data by Engine Review Trust Controls

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.