AssetMind AI | AI2COE

Answer-first product brief

AssetMind AI turns uploaded operational data into decision evidence.

AssetMind AI is an active diagnostic engine: it parses source data, maps fields, validates quality, runs analyzers, scores risk, generates evidence records, assigns confidence tiers, creates review actions, and produces AssetMind AI Asset-to-Part Risk Report.

Executive rule: this engine does not replace SAP, Maximo, Oracle, EAM, CMMS, procurement, inventory, or maintenance systems. It creates governed evidence before teams decide what to remediate.

Engine contract

AssetMind AI Asset-to-Part Risk Report

AssetMind AI validates uploaded data, maps source fields, runs deterministic analyzers, creates evidence records, assigns confidence, estimates impact, and produces an executive report.

Input data required

Asset Id
Description

Optional inputs

Material Id
Asset Status
Criticality
Equipment Class
Site
Last Used Date
Quantity
Unit Cost

Buyer relevance

Primary personaAsset Integrity, Maintenance, Reliability, and Operations leaders

Sample dataPublic sample CSV, mapping template, data dictionary, HTML report, and PDF report are available before private upload.

Diagnostic logicDeterministic analyzers read mapped source fields, generate findings, attach evidence, and expose assumptions and limitations.

MetricAsset intelligence score

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

GovernanceNo ERP write-back. Findings require owner review before remediation.

Active outputScore, findings, evidence, confidence, report, action tracker, and score history.

Report outputAssetMind AI Asset-to-Part Risk Report with HTML report, CSV evidence, PDF export, action tracker entry, score history snapshot, and email delivery status.

Report emailCompleted authenticated runs attempt branded report email delivery and retain delivery status in the report inventory.

Try before upload

Run a public sample, then replace assumptions with uploaded data.

Sample mode uses deterministic AI2COE data and is clearly labeled. Uploaded-data mode follows the same mapping, validation, evidence, scoring, PDF export, email, action-tracker, and score-history path.

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

Accepted columns and aliases

What AssetMind AI can map from SAP, Maximo, Oracle, Infor, Hexagon EAM, CMMS, and CSV exports.

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.

Multi-file diagnostic pack

Best customer results come from the right export pack.

Recommended file	Fields that improve score 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

Value model

What leadership can use from this engine.

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.

Product depth

P0, P1, and P2 capabilities built into the Industrial IQ product model.

Priority	Capability depth
P0	Asset-to-part linkage, plant-register relevance, critical asset spare coverage, obsolete asset spare exposure, and asset risk heatmap.
P0	Inference from asset ID, equipment tag, description, manufacturer, model, work-order text, and BOM-like references.
P0	Linked, weakly linked, and unlinked critical-spare classification.
P1	Equipment hierarchy risk heatmap, retired-asset stock queue, equipment-class gaps, and criticality-weighted exposure.
P1	Asset-part knowledge graph connecting asset, material, site, equipment class, status, and spare coverage.
P1	COO and maintenance report views by plant, equipment class, and criticality.
P2	Asset criticality matrix and spares coverage index by plant, line, equipment class, and location.
P2	BOM readiness diagnostic before EAM/CMMS modernization.
P2	Portfolio-level asset-spare coverage trend for recurring reviews.
Competitive moat	Stays above EAM systems like Maximo, Oracle, IFS, and Prometheus by diagnosing exported data rather than replacing asset workflows.

Buyer committee interpretation

How each executive reads the same diagnostic output.

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

Evidence and confidence model

What the engine produces after a governed run.

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.

Workflow

Upload to diagnostic to recurring intelligence.

Step	Layer	Governed behavior
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.

Industry fit

Configured for asset-intensive operating reality.

Oil & GasSAP S/4HANA migration, turnaround readiness

Miningremote stockouts, haul truck downtime

Utilitiesoutage readiness, regulatory audit

Power Generationplanned outages, turbine spare coverage

Chemicalsprocess safety, shutdown readiness

PharmaceuticalsGMP audit, validated maintenance

Transportation & Logisticsfleet uptime, depot duplication

Ports & Marinecrane downtime, terminal uptime

Aviationaircraft-on-ground risk, MRO depot duplication

Construction & Heavy Equipmentequipment availability, site-level duplicate stock

Healthcare Facilitiesclinical uptime, biomed asset coverage

Higher Education Campusescampus maintenance visibility, storeroom consolidation

Data Centersuptime assurance, critical facilities spares

Renewable Energyremote-site availability, turbine spare coverage

Water & Wastewaterservice continuity, pump station spare coverage

Benchmark and claims discipline

Assumptions are separated from uploaded-data results.

Public pages may use benchmark ranges to help leaders understand the problem. A diagnostic run replaces the benchmark with mapped source records, actual evidence, confidence tiers, and report ownership.

Low-confidence or high-risk findings are routed to human review. AI2COE does not make autonomous ERP updates or unsupported ROI claims.

Source resultUploaded data, mapped fields, evidence records, score snapshot

AssumptionBenchmark, industry range, carrying-cost assumption, ROI scenario

GovernanceOwner review, confidence tier, audit log, no write-back

Benchmark methodology Run this engine

Knowledge graph

Problem -> ERP export -> industry context -> engine evidence -> action.

AssetMind AI connects the buyer problem to source-system evidence, industry risk language, report outputs, and governed action tracking. This makes the page readable to executives and buying committees without exposing private datasets or internal code.

Frequently asked questions

Questions buyers ask before running Asset-to-Part Intelligence.

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.