Industrial IQ | AI2COE | ReadyMind AI
ReadyMind AI AI Readiness Report
77.7AI readiness score
WatchlistRisk level
Medium ConfidenceConfidence
8Evidence records
Source mode: Sample dataset result. Sample results demonstrate workflow and report structure; uploaded-data results replace assumptions with customer source evidence.
Score interpretation: Lower health or readiness scores indicate higher unresolved exposure, weaker readiness, or stronger review need. Scores are deterministic and derived from mapped source fields, findings, evidence, and component inputs.
Executive interpretation: Focus on asset integrity, turnaround readiness, working capital exposure, and audit-safe ERP preparation.
Report delivery and governance controls
Email
Report email path
Authenticated Industrial IQ runs attempt branded report email delivery and retain delivery status in the platform report inventory.
Review
Human review required
Low-confidence or high-impact findings should be accepted, rejected, assigned, deferred, or marked needs-more-data before remediation.
ERP safety
No ERP write-back
Industrial IQ produces evidence and recommendations only. It does not autonomously change SAP, Maximo, Oracle, EAM, CMMS, procurement, inventory, or asset records.
CFO command view
62062.5Capital exposure signal
7447.5-17377.5Recoverable range
11171.25Annual leakage signal
WatchlistBoard attention band
Diagnostic components
76.0DATA QUALITY READINESS
62ERP READINESS
95GOVERNANCE READINESS
77.7OPERATIONAL READINESS
PartsCleanse AIFIRST USE CASE
active_deterministic_evidence_engineDIAGNOSTIC DEPTH
1REQUIRED FIELDS MAPPED
9OPTIONAL FIELDS MAPPED
Score formula: average of data quality, ERP readiness, and governance readiness Random score used: False
| Score input | Value |
|---|---|
| data quality readiness | 76.0 |
| erp readiness | 62 |
| governance readiness | 95 |
| operational readiness | 77.7 |
| first use case | PartsCleanse AI |
| required fields mapped | 1 |
| optional fields mapped | 9 |
| source rows profiled | 24 |
| estimated row value total | 154200.0 |
| governance owner gap rows | 0 |
| valuation gap rows | 0 |
| site context gap rows | 0 |
| stale master rows | 0 |
Product maturity and competitive depth
Competitive position: Competes against generic AI readiness assessments by using actual ERP/CMMS/export evidence and a concrete next diagnostic path.
| Priority | Implemented product capability |
|---|---|
| P0 | ERP data quality, data freshness, owner accountability, governance readiness, and first-use-case recommendation.; Field completeness, consistency, duplicate-rate, site context, valuation, and review workflow scoring.; ERP/AI readiness output that labels what is proven, missing, assumed, and blocked. |
| P1 | ERP-specific readiness pack for SAP, Maximo, Oracle, Infor, Hexagon EAM, and CMMS exports.; Transformation risk register and first-use-case sequencing across PartsCleanse, InventoryMind, ProcureMind, and ReadyMind.; AI adoption roadmap grounded in uploaded-data readiness instead of generic AI maturity claims. |
| P2 | Industry benchmark comparison for data, ERP, governance, and AI readiness.; Transformation sequencing engine with budget, risk, and data-readiness gates.; Recurring readiness trend for quarterly steering committees. |
ICP packaging
| Package | Engines | Decision supported |
|---|---|---|
| CIO / ERP Pack | ReadyMind AI, GovernanceMind AI, PartsCleanse AI | Prove ERP, data, governance, and AI readiness before integration, automation, or migration decisions. |
Advanced product insights
| Product output | Diagnostic value |
|---|---|
| erp modernization pack | {"infor_hexagon_cmms": "Item, stock, asset, work-order, site, owner, status, and last-updated context are the minimum readiness path.", "maximo": "ITEMNUM, DESCRIPTION, ISSUEUNIT, ORDERUNIT, STOREROOM, SITEID, VENDOR, and ASSETNUM improve readiness.", "oracle": "Item, organization, on-hand, cost, supplier, asset, work-order, and maintenance context improve diagnostic confidence.", "sap": "MARA/MAKT/MARC/MBEW-style material exports should include material, description, UOM, plant, valuation, manufacturer, MPN, and owner."} |
| first use case sequence | ["PartsCleanse AI if descriptions/material IDs are mapped and duplicate rate is unknown.", "InventoryMind AI if quantity, value, movement, and criticality are available.", "ProcureMind AI if PO, supplier, price, and stock overlap fields are available.", "ReadyMind AI if governance ownership, freshness, and ERP readiness need proof first."] |
| transformation risk register | [{"control": "Assign data owner before AI expansion.", "count": 0, "risk": "Missing owner fields"}, {"control": "Add plant/site/facility before cross-site routing.", "count": 0, "risk": "Missing site context"}, {"control": "Refresh export and set recurring cadence.", "count": 0, "risk": "Stale master data"}, {"control": "Add value fields before CFO exposure reporting.", "count": 0, "risk": "Missing valuation"}] |
| readiness control model | {"govern": "owner, approval, review, and audit fields", "manage": "action tracker, review queue, and recurring score history", "map": "ERP export fields and use-case data availability", "measure": "completeness, freshness, value, and evidence coverage"} |
Buyer committee views
CFO
Can quantified exposure justify a diagnostic or remediation budget?
ReadyMind AI shows 62062.5 as the current capital or leakage signal before owner review.
Next question: Which findings have enough confidence and value to enter the financial business case?
COO
Which findings threaten operational continuity, site readiness, or uptime?
0 high-attention findings require operational owner review.
Next question: Which findings must be resolved before the next outage, shutdown, or planning cycle?
CIO
Is the data ready for governed AI without ERP write-back risk?
Industrial IQ produced evidence from exports only and did not change ERP, EAM, CMMS, or procurement systems.
Next question: Which missing fields or governance gaps should be fixed in the next export?
Procurement
Where do supplier, purchase, or stocked-but-purchased signals need review?
Procurement actions should be evidence-led and routed through human review before supplier action.
Next question: Which supplier or purchase findings are defensible enough for category review?
Maintenance
Will spare availability and catalog quality support maintenance execution?
Maintenance should use the evidence queue to protect planned work and critical assets.
Next question: Which findings block planned work, shutdown readiness, or critical equipment coverage?
Board
Is this risk material enough to fund recurring diagnostic intelligence?
The result is diagnostic evidence, not an autonomous system change or unsupported ROI claim.
Next question: Should leadership fund the next diagnostic cycle, review queue, or remediation scope?
Evidence graph
Model: Source Record -> Finding -> Evidence -> Confidence -> Business Impact -> Recommended Action -> Review Status -> Report -> Score History
12 nodes | 11 evidence relationships. This graph links uploaded source rows to findings, confidence, business impact, recommended actions, report output, and score history.
Renewal value view
62062.5EXPOSURE IDENTIFIED
2REVIEW QUEUE SIZE
2ACTIONS CREATED
0ACTIONS REVIEWED
4965.0CONSERVATIVE VALUE REALIZATION
9930.0BASE VALUE REALIZATION
monthly for high-risk sites; quarterly for controlled sitesNEXT REVIEW CADENCE
Recurring value interpretation: Compare this run against the next upload to show exposure reviewed, actions completed, score movement, and remaining risk.
Findings
Trust control: Each finding must be interpreted with its confidence, evidence count, mapped fields, and source records. Similar-looking industrial records may still require owner review before action.
| Analyzer | Finding | Severity | Confidence | Evidence | Action |
|---|---|---|---|---|---|
| Use Case Readiness Analyzer | First-use-case recommendation is available | LOW | 82% | 1 | Start with PartsCleanse AI or InventoryMind AI depending on whether catalog quality or inventory risk is the higher priority. |
| Data Completeness Gate | 2 mapped fields need stronger coverage before recurring automation | MEDIUM | 68% | 8 | Improve field coverage or keep affected findings in human review until the next upload cycle. |
Evidence records
| ID | Confidence tier | Severity | Description | Value | Source | Reason codes |
|---|---|---|---|---|---|---|
| E-386f0895 | Medium Confidence | MEDIUM | 2 mapped fields need stronger coverage before recurring automation | 2925.0 | row:1:MAT-001-001 | material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match |
| E-2df4da04 | Medium Confidence | MEDIUM | 2 mapped fields need stronger coverage before recurring automation | 4400.0 | row:2:MAT-002-002 | material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match |
| E-1011c3e6 | Medium Confidence | MEDIUM | 2 mapped fields need stronger coverage before recurring automation | 6125.0 | row:3:MAT-003-003 | material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match |
| E-b43b9c65 | Medium Confidence | MEDIUM | 2 mapped fields need stronger coverage before recurring automation | 8100.0 | row:4:MAT-004-004 | material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match |
| E-dec43d50 | Medium Confidence | MEDIUM | 2 mapped fields need stronger coverage before recurring automation | 10325.0 | row:5:MAT-005-005 | material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match |
| E-b135de3c | Medium Confidence | MEDIUM | 2 mapped fields need stronger coverage before recurring automation | 6800.0 | row:6:MAT-000-006 | material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match |
| E-82fbb464 | Medium Confidence | MEDIUM | 2 mapped fields need stronger coverage before recurring automation | 8775.0 | row:7:MAT-001-007 | material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match |
| E-7636cc02 | Medium Confidence | MEDIUM | 2 mapped fields need stronger coverage before recurring automation | 2200.0 | row:8:MAT-002-008 | material-id-present, description-signature, supplier-alias-signal, site-context, value-bearing-row, spec-token-match |
Recommended actions
P1
Improve field coverage or keep affected findings in human review until the next upload cycle.
Owner: CIO | Due: 60 days
P1
Start with PartsCleanse AI or InventoryMind AI depending on whether catalog quality or inventory risk is the higher priority.
Owner: CIO | Due: 60 days
Mapping and validation
| Input | Source column | Completeness | Confidence | Reason |
|---|---|---|---|---|
| description | description | % | % | |
| material_id | material_id | % | % | |
| asset_id | asset_id | % | % | |
| quantity | quantity | % | % | |
| unit_cost | unit_cost | % | % | |
| supplier | supplier | % | % | |
| site | site | % | % | |
| owner | owner | % | % | |
| approval_status | approval_status | % | % | |
| last_updated | last_updated | % | % |
Source fit, AI match, and normalization
100%Source fit score
100.0%AI match score
100%Mapping readiness score
94.3%Diagnostic confidence score
Workbench interpretation: Source fit measures whether the uploaded file contains recognizable inputs. AI match measures column-mapping confidence. Diagnostic readiness measures whether the normalized mapped data can support trustworthy engine output.
| Quality signal | Value |
|---|---|
| source fit score | 100 |
| ai match score | 100.0 |
| diagnostic readiness score | 100 |
| required mapped | 1 |
| required total | 1 |
| optional mapped | 9 |
| optional total | 9 |
| required completeness | 100.0 |
| row count | 24 |
| column count | 32 |
| blockers | 0 |
| warnings | 0 |
| source fit band | Strong |
| ai match band | Strong |
| readiness band | Strong |
| diagnostic confidence score | 94.3 |
| diagnostic confidence band | Strong |
Normalization plan
| Engine field | Source column | Original sample | Normalized preview | Rule |
|---|---|---|---|---|
| Description | description | Oil & Gas pump bearing seal kit model 1 stainless 4 inch | OIL & GAS PUMP BEARING SEAL KIT MODEL 1 STAINLESS 4 INCH | Normalize case, abbreviations, punctuation, industrial units, specification tokens, and obvious spacing noise. |
| Material Id | material_id | MAT-001-001 | MAT-001-001 | Trim whitespace, preserve leading zeroes, normalize item/material identifiers, and keep original source reference. |
| Asset Id | asset_id | AST-002 | AST-002 | Normalize asset, equipment, functional location, and tag identifiers. |
| Quantity | quantity | 3 | 3 | Parse numeric quantity, keep negatives for audit context, and separate blank/zero from missing. |
| Unit Cost | unit_cost | 975 | 975 | Parse unit cost, retain source currency, and separate uploaded value from benchmark assumption. |
| Supplier | supplier | Industrial Supply Co | INDUSTRIAL SUPPLY CO | Normalize supplier aliases and vendor naming variants for leakage and overlap analysis. |
| Site | site | Plant-2 | Plant-2 | Normalize plant, site, storeroom, facility, depot, or operating-unit labels. |
| Owner | owner | Materials Manager | Materials Manager | Normalize data owner, process owner, reviewer, planner, buyer, or accountable role. |
| Approval Status | approval_status | Missing | Missing | Normalize approval, workflow, stewardship, review, and governance status. |
| Last Updated | last_updated | Missing | Missing | Parse update date into data freshness bands. |
Assumptions and limitations
Assumptions
- Uploaded data is treated as the source of truth for this diagnostic run.
- No ERP write-back is performed. Outputs are recommendations and evidence records only.
- Financial estimates use uploaded values where available and conservative assumptions otherwise.
- Industry language is adjusted for Oil & Gas: plants, wells, refineries, shutdowns, turnarounds, and asset integrity.
- Workbench scores were calculated before and after engine execution: source fit 100%, AI match 100.0%, mapping readiness 100%, diagnostic confidence 94.3%.
- Public sample report: deterministic AI2COE sample data was used. Replace with uploaded customer data for customer-specific findings.
Limitations
- Results are diagnostic signals, not final accounting entries.
- Low-confidence findings require human review before remediation.
- Missing source fields reduce confidence and may suppress some analyzers.
- Benchmarks are labelled assumptions unless validated by uploaded data.