AI readiness score
Use the score as a diagnostic signal that needs evidence review.
Industrial IQ | AI2COE | ReadyMind AI
ReadyMind AI AI Readiness Report
Report takeaway
What this sample report answers
ReadyMind AI Sample Report shows how AI2COE Industrial IQ turns sample operational data into evidence rows, confidence tiers, score output, actions, report value, and governance controls. Sample results prove workflow and output structure; uploaded customer data is required for customer-specific findings.
Sample boundary: private source files are not required to view this sample. In live diagnostics, uploaded source files are used to generate report packs and then purged.
Review owner
Enterprise buyers inspecting report quality before uploading private operational data.
Input files
Engine-specific operational CSV exports or deterministic AI2COE sample data.
Diagnostic evidence
Evidence, scores, assumptions, limitations, actions, and governance status.
Next action
Sample report output map
How to evaluate the AI Readiness Intelligence output bundle
This visual map helps buyers trace the sample report as a product output: source file, field mapping, evidence rows, confidence context, report pack, and governed owner action stay connected before any private upload.
field completeness, owner coverage, system source
Inspect source context, confidence, assumptions, and limits.
ReadyMind AI AI Readiness Report
Use HTML and PDF views for buyer committee review.
Owner, status, next review
Keep remediation buyer-controlled after the sample is replaced with uploaded data.
Baseline and rerun movement
Use recurring reviews to see what changed after accepted actions.
CSV, mapping, data dictionary
Give data owners the field expectations before private upload.
Output boundary: this sample proves output structure and workflow only. Uploaded operational data is required before customer-specific findings can be reviewed.
Evaluation kit
Download the supporting assets for this AI Readiness Intelligence sample.
Use the HTML sample to inspect report structure, then use the supporting files to understand what data owners would prepare before replacing sample assumptions with uploaded-data evidence.
PDF
Offline review artifact for buyer committees.
Sample CSVDeterministic sample input used for testing the workflow.
MappingColumn mapping guide for source-fit review.
DictionaryField definitions and expected diagnostic meaning.
Product pageReview the AI Readiness Intelligence engine page.
Trust boundary: sample assets are deterministic demonstration files. Live diagnostics remain read-only, require owner review before action, and do not write back to ERP/EAM/CMMS systems.
Executive Summary
85.0AI readiness score
ControlledRisk 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.
Data handling: Uploaded source files are processed to generate the diagnostic report pack and then purged. Summary metrics and audit metadata may be retained for governance.
CFO command view
0.0Capital exposure signal
0.0-0.0Recoverable range
0.0Annual leakage signal
ControlledBoard attention band
Diagnostic components
98.0DATA QUALITY READINESS
62ERP READINESS
95GOVERNANCE READINESS
85.0OPERATIONAL READINESS
ReadyMind data enrichmentFIRST USE CASE
active_deterministic_evidence_engineDIAGNOSTIC DEPTH
2REQUIRED FIELDS MAPPED
12OPTIONAL FIELDS MAPPED
Score formula: average of data quality, ERP readiness, and governance readiness Random score used: False
| Score input | Value |
|---|---|
| data quality readiness | 98.0 |
| erp readiness | 62 |
| governance readiness | 95 |
| operational readiness | 85.0 |
| first use case | ReadyMind data enrichment |
| required fields mapped | 2 |
| optional fields mapped | 12 |
| source rows profiled | 24 |
| estimated row value total | 24000.0 |
| governance owner gap rows | 0 |
| valuation gap rows | 24 |
| site context gap rows | 0 |
| stale master rows | 0 |
| validation issue count | 0 |
| validation blocker count | 0 |
| prompt injection signal count | 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, AssetMind AI | Prove ERP, data, governance, and AI readiness before integration, automation, or migration decisions. |
| CISO / Legal Pack | GovernanceMind AI, ReadyMind AI, FinanceMind AI | Review no-write-back boundaries, source traceability, confidence tiers, limitation language, and audit evidence before industrial AI adoption expands. |
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": 24, "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 0.0 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
11 nodes | 10 evidence relationships. This graph links uploaded source rows to findings, confidence, business impact, recommended actions, report output, and score history.
Renewal value view
0.0EXPOSURE IDENTIFIED
1REVIEW QUEUE SIZE
1ACTIONS CREATED
0ACTIONS REVIEWED
0.0CONSERVATIVE VALUE REALIZATION
0.0BASE 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.
Key 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% | 8 | Start with PartsCleanse AI or InventoryMind AI depending on whether catalog quality or inventory risk is the higher priority. |
Evidence Records
| ID | Confidence tier | Severity | Description | Value | Source | Reason codes |
|---|---|---|---|---|---|---|
| E-8bba166a | Medium Confidence | LOW | First-use-case recommendation is available | 0.0 | row:1 | site-context |
| E-b513048a | Medium Confidence | LOW | First-use-case recommendation is available | 0.0 | row:2 | site-context |
| E-ee7ac449 | Medium Confidence | LOW | First-use-case recommendation is available | 0.0 | row:3 | site-context |
| E-031593e8 | Medium Confidence | LOW | First-use-case recommendation is available | 0.0 | row:4 | site-context |
| E-f05aa813 | Medium Confidence | LOW | First-use-case recommendation is available | 0.0 | row:5 | site-context |
| E-c1dd6db4 | Medium Confidence | LOW | First-use-case recommendation is available | 0.0 | row:6 | site-context |
| E-3df48814 | Medium Confidence | LOW | First-use-case recommendation is available | 0.0 | row:7 | site-context |
| E-778189f1 | Medium Confidence | LOW | First-use-case recommendation is available | 0.0 | row:8 | site-context |
Recommended Actions
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 |
|---|---|---|---|---|
| process_name | process_name | 100.0% | 100.0% | Exact ERP alias |
| data_source | data_source | 100.0% | 100.0% | Exact ERP alias |
| business_unit | business_unit | 100.0% | 100.0% | Exact ERP alias |
| data_owner | data_owner | 100.0% | 100.0% | Exact ERP alias |
| data_quality_score | data_quality_score | 100.0% | 100.0% | Exact ERP alias |
| system_of_record | system_of_record | 100.0% | 100.0% | Exact ERP alias |
| automation_readiness | automation_readiness | 100.0% | 100.0% | Exact ERP alias |
| governance_owner | governance_owner | 100.0% | 100.0% | Exact ERP alias |
| ai_use_case | ai_use_case | 100.0% | 100.0% | Exact ERP alias |
| risk_level | risk_level | 100.0% | 100.0% | Exact ERP alias |
| site | site | 100.0% | 100.0% | Exact ERP alias |
| owner | owner | 100.0% | 100.0% | Exact ERP alias |
| approval_status | approval_status | 100.0% | 100.0% | Exact ERP alias |
| last_updated | last_updated | 100.0% | 100.0% | Exact ERP alias |
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 | 2 |
| required total | 2 |
| optional mapped | 12 |
| optional total | 12 |
| required completeness | 100.0 |
| row count | 24 |
| column count | 42 |
| 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 |
|---|---|---|---|---|
| Process Name | process_name | MRO catalog and inventory readiness review | MRO catalog and inventory readiness review | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Data Source | data_source | SAP PM / MM export | SAP PM / MM export | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Business Unit | business_unit | Operations | Operations | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Data Owner | data_owner | Materials Manager | Materials Manager | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Data Quality Score | data_quality_score | 87 | 87 | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| System Of Record | system_of_record | SAP | SAP | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Automation Readiness | automation_readiness | Candidate | Candidate | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Governance Owner | governance_owner | Data Governance Lead | Data Governance Lead | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Ai Use Case | ai_use_case | MRO duplicate and inventory readiness diagnostic | MRO duplicate and inventory readiness diagnostic | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Risk Level | risk_level | Medium | Medium | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| 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 | approved_for_diagnostic | APPROVED_FOR_DIAGNOSTIC | Normalize approval, workflow, stewardship, review, and governance status. |
| Last Updated | last_updated | 2026-02-02 | 2026-02-02 | 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.