AssetMind AI | AI2COE Solution

Business problem

Asset-to-Part Diagnostic.

Asset-to-part linkage, critical spare coverage, obsolete asset spares, and plant risk heatmaps.

The page is not a static brochure. The engine has a working upload, preview, map, validate, analyze, evidence, score, report, action, review, and score-history path inside the Industrial IQ portal.

Buyer ownerAsset Integrity, Maintenance, Reliability, and Operations leaders

Trigger eventERP migration, AI readiness, working-capital pressure, procurement leakage, reliability risk, audit readiness, or recurring operational review.

Input data requiredAsset Id, Description

Upload workflowUpload CSV, preview fields, confirm AI-recommended mappings, validate required coverage, normalize where needed, then run the engine.

Diagnostic logicMap source columns, normalize values, run deterministic engine checks, then attach evidence and confidence before report output.

Sample evidence tablePublic sample mode exposes mapped evidence rows before the customer uploads private data.

Score outputAsset intelligence score: lower values mean weaker asset-to-part linkage, critical-spare coverage, plant relevance, and obsolete-asset-spare control.

Executive report previewSample report pages show the decision narrative, evidence rows, confidence tiers, assumptions, limitations, PDF export, and next actions before private upload.

Report outputAssetMind AI Asset-to-Part Risk Report with HTML, CSV evidence, PDF, and report email status for authenticated runs.

Governance controlsNo ERP write-back, no autonomous supplier outreach, confidence-tier labels, human-review routing, audit metadata, and clear sample-versus-uploaded-data labeling.

Upload workflow

Upload -> Validate -> Analyze -> Evidence -> Score -> Report -> Action -> Repeat.

Step	Layer	Customer experience
1	Upload	CSV export enters the parser. Source file retention rules are disclosed.
2	Map	ERP/CMMS aliases are inferred, then corrected or confirmed by the user.
3	Validate	Required fields, completeness, missing values, and confidence reducers are shown before run.
4	Analyze	Engine-specific analyzers generate findings, evidence, and impact estimates.
5	Govern	Findings receive confidence tiers and human-review status before any action.
6	Report	Executive report, evidence table, action tracker, and score snapshot are produced.

Public proof before account creation

Customers can inspect samples before uploading their own data.

Every sample result is labeled as sample mode. It demonstrates mapping, validation, scoring, evidence, confidence, report structure, action logic, and recurring-review context without claiming customer-specific exposure.

Run Industrial IQ Snapshot Run sample diagnostic Download sample PDF Sample CSV Mapping template Data dictionary Product page

Required data and field dictionary

Accepted fields, aliases, and mapping expectations.

Input	Need	Common aliases	Meaning
Asset Id	Yes	asset_id; equipment; equipment_id; asset; tag; functional_location; floc; equipment_tag; asset_tag	Equipment, asset, functional location, tag, or plant-register identifier.
Description	Yes	description; item_description; material_description; maktx; short_text; part_description; long_text; desc	Item, part, asset, work-order, finding, or source-record description used by the engine.
Material Id	Recommended	material; material_id; material_number; matnr; item; item_number; item_id; sku; part; part_number; stock_code	Unique material, SKU, item, or spare-part identifier from the source system.
Asset Status	Recommended	asset_status; equipment_status; status; active_status; retired; lifecycle_status; equipment_lifecycle	Asset lifecycle status such as active, retired, inactive, mothballed, or decommissioned.
Criticality	Recommended	criticality; critical; abc; risk_class; equipment_criticality; asset_criticality	Criticality rating for part, asset, work order, or operating risk.
Equipment Class	Recommended	equipment_class; asset_class; class; equipment_type; asset_type	Equipment class, asset type, system, line, unit, or maintainable item category.
Site	Recommended	site; plant; werks; location; storeroom; warehouse; depot; facility	Plant, site, warehouse, storeroom, region, location, or operating unit.
Last Used Date	Recommended	last_used_date; last_movement_date; last_issue_date; last_work_order_date	Last issue, last work-order use, last asset use, or last consumption date.
Quantity	Recommended	quantity; qty; stock_qty; on_hand; qty_on_hand; unrestricted; labst; stock_on_hand	Quantity, balance, order quantity, stock quantity, or demand quantity depending on engine.
Unit Cost	Recommended	unit_cost; cost; price; moving_average_price; map; valuation_price; standard_price; unit_price	Unit cost, average cost, standard price, last purchase price, or valuation rate.

Recommended diagnostic data pack

What to upload for a stronger run.

Recommended file	Fields that improve confidence
Asset register	asset ID, status, equipment class, site, criticality
Material master	material ID, description, manufacturer, MPN
Work-order or BOM references	asset-to-part references, usage, last used date

Business impact model

Why this matters to the buyer committee.

Asset coverage

Asset coverage model

Asset-to-part linkage, active equipment coverage, obsolete asset-spare exposure.

Reliability risk

Reliability risk model

Critical assets without clear spare coverage and plant-level risk concentration.

Decision output

Decision output model

Asset intelligence score, coverage gaps, risk heatmap, action tracker.

Evidence and confidence

What the report proves.

Output layer	Example	Why it matters
Score	Asset intelligence score	0-100 signal with risk level and trend-ready snapshot.
Score formula	Deterministic calculation	The report exposes the scoring formula and component inputs; random scores are not used.
Finding	AssetMind AI Asset-to-Part Risk Report	Issue title, severity, source engine, and owner-facing action.
Evidence	Mapped source records	Source-row references, relevant fields, analyzer reason codes, and confidence tier.
Evidence graph	Source -> finding -> evidence -> action	The result carries an evidence graph for review, report, action, and score-history continuity.
Confidence	High / Medium / Needs Review	Coverage, completeness, source-field quality, and analyzer agreement.
Action	Owner review item	Recommended action, priority, due window, and review status.
Renewal value	Recurring management view	The report shows exposure identified, review queue size, actions created, and next review cadence.

Buyer interpretation

One diagnostic, multiple executive decisions.

Buyer	Decision question	Evidence source
CFO	Can the finding be tied to capital exposure, carrying cost, leakage, or payback discipline?	AssetMind AI
COO	Does the evidence reduce operating risk, downtime exposure, site friction, or service disruption?	AssetMind AI
CIO / ERP owner	Are source fields mapped, export quality visible, and ERP write-back avoided unless governed?	AssetMind AI
Procurement	Does the diagnostic expose supplier, PO, duplicate spend, stocked-but-purchased, or price-variance risk?	AssetMind AI
Maintenance / Reliability	Does the evidence affect work-order readiness, false stockout, shutdown coverage, or critical-spare confidence?	AssetMind AI
Data governance	Can findings be reviewed, accepted, rejected, audited, and defended after the report is shared?	AssetMind AI

Trust controls

This engine is governed before operational action.

Trust control

No ERP write-back

Findings are recommendations and review items, not autonomous ERP changes.

Trust control

Evidence traceability

Every finding links back to source records, mapped fields, and reason codes.

Trust control

Confidence tiers

High, medium, low, and needs-review logic is visible before action.

Trust control

Human review

Owners can accept, reject, defer, assign, or request more data.

Trust control

Audit trail

Report exports, reviewer actions, and status changes remain traceable.

FAQ

Questions buyers ask before running AssetMind AI.

What data does AssetMind AI need?

AssetMind AI requires Asset Id, Description. Optional fields such as Material Id, Asset Status, Criticality, Equipment Class, Site, Last Used Date improve confidence and business-impact precision.

What does AssetMind AI produce?

It produces AssetMind AI Asset-to-Part Risk Report, a 0-100 asset intelligence score, evidence records, confidence tiers, recommended actions, and a review-ready executive summary.

Does AI2COE write back to SAP, Maximo, Oracle, or any CMMS?

No. AI2COE diagnostics are decision-support outputs. They do not change ERP, EAM, CMMS, procurement, inventory, or asset records automatically.

How does confidence tiering work?

Findings are ranked by source-field coverage, data completeness, evidence quality, analyzer agreement, and whether a human owner should review the recommendation before action.

How should leadership use the report?

Use the report to decide whether the issue is measurable, material, governable, and worth funding before starting a larger ERP, inventory, procurement, maintenance, or AI transformation program.