Governance readiness score
Use the score as a diagnostic signal that needs evidence review.
Industrial IQ | AI2COE | GovernanceMind AI
GovernanceMind AI Governance Review Report
Report takeaway
What this sample report answers
GovernanceMind 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 Evidence Governance 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.
finding ID, source record, confidence tier
Inspect source context, confidence, assumptions, and limits.
GovernanceMind AI Governance Review 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 Evidence Governance 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 Evidence Governance 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
0.0Governance readiness score
Board-Level RiskRisk level
Medium ConfidenceConfidence
21Evidence 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
Board-Level RiskBoard attention band
Diagnostic components
0MISSING CONFIDENCE ROWS
0MISSING REVIEWER ROWS
24UNRESOLVED REVIEW ROWS
TrueNO ERP WRITEBACK ASSURANCE
active_deterministic_evidence_engineDIAGNOSTIC DEPTH
2REQUIRED FIELDS MAPPED
10OPTIONAL FIELDS MAPPED
24SOURCE ROWS PROFILED
Score formula: 100 - governance_risk/source_rows*100 with missing confidence weighted higher Random score used: False
| Score input | Value |
|---|---|
| missing confidence rows | 0 |
| missing reviewer rows | 0 |
| unresolved review rows | 24 |
| no erp writeback assurance | True |
| required fields mapped | 2 |
| optional fields mapped | 10 |
| source rows profiled | 24 |
| estimated row value total | 24000.0 |
| missing source traceability rows | 0 |
| missing action owner rows | 0 |
| stale review rows | 20 |
| low confidence review rows | 5 |
| validation issue count | 0 |
| validation blocker count | 0 |
| prompt injection signal count | 0 |
Product maturity and competitive depth
Competitive position: Turns trust into the product moat: competitors can optimize data, but Industrial IQ makes every diagnostic explainable, reviewable, auditable, and no-write-back.
| Priority | Implemented product capability |
|---|---|
| P0 | Evidence traceability, confidence tiers, human review, audit log, action owner, no ERP write-back, and responsible AI controls.; Accept/reject/needs-more-data review semantics with confidence and source-record evidence.; Model/diagnostic card for every engine: inputs, logic, assumptions, limitations, and human-review requirement. |
| P1 | Reviewer assignment, stale review escalation, false-positive control, confidence movement, and governance export.; Cross-engine governance dashboard connecting finding, evidence, reviewer, action, export, and audit event.; Customer-facing audit pack with source evidence and limitation language. |
| P2 | Governance learning loop showing accepted/rejected patterns and confidence calibration over time.; Responsible AI report for board, CIO, audit, security, and data governance teams.; Recurring audit readiness score and SOC-style evidence export. |
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 |
|---|---|
| diagnostic cards | [{"engine": "PartsCleanse AI", "human_review_required": true, "inputs": ["description"], "limitations": "Low confidence and missing source fields require human review.", "logic": "Deterministic analyzers produce findings from uploaded or sample data."}, {"engine": "InventoryMind AI", "human_review_required": true, "inputs": ["material_id", "quantity"], "limitations": "Low confidence and missing source fields require human review.", "logic": "Deterministic analyzers produce findings from uploaded or sample data."}, {"engine": "ProcureMind AI", "human_review_required": true, "inputs": ["po_number", "description"], "limitations": "Low confidence and missing source fields require human review.", "logic": "Deterministic analyzers produce findings from uploaded or sample data."}, {"engine": "FinanceMind AI", "human_review_required": true, "inputs": ["material_id", "stock_value"], "limitations": "Low confidence and missing source fields require human review.", "logic": "Deterministic analyzers produce findings from uploaded or sample data."}, {"engine": "AssetMind AI", "human_review_required": true, "inputs": ["asset_id", "description"], "limitations": "Low confidence and missing source fields require human review.", "logic": "Deterministic analyzers produce findings from uploaded or sample data."}, {"engine": "ReliabilityMind AI", "human_review_required": true, "inputs": ["work_order", "description"], "limitations": "Low confidence and missing source fields require human review.", "logic": "Deterministic analyzers produce findings from uploaded or sample data."}, {"engine": "ReadyMind AI", "human_review_required": true, "inputs": ["process_name", "data_source"], "limitations": "Low confidence and missing source fields require human review.", "logic": "Deterministic analyzers produce findings from uploaded or sample data."}, {"engine": "GovernanceMind AI", "human_review_required": true, "inputs": ["finding_id", "finding_type"], "limitations": "Low confidence and missing source fields require human review.", "logic": "Deterministic analyzers produce findings from uploaded or sample data."}] |
| responsible ai controls | ["source traceability", "confidence tier", "named reviewer", "action owner", "no ERP write-back"] |
| governance export model | {"audit_relationship": "source record -> finding -> confidence -> reviewer action -> export event", "export_fields": ["finding_id", "source_record", "confidence_tier", "reviewer", "review_status", "action_owner", "timestamp", "report_id"], "no_writeback_assurance": true} |
| review semantics | ["accepted", "rejected", "needs more data", "duplicate confirmed", "not duplicate", "action assigned"] |
Buyer committee views
CFO
Can quantified exposure justify a diagnostic or remediation budget?
GovernanceMind 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?
1 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
26 nodes | 25 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
3REVIEW QUEUE SIZE
3ACTIONS 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 |
|---|---|---|---|---|---|
| Audit Log | 24 rows show unresolved governance review items | MEDIUM | 78% | 24 | Close, reject, or assign open findings before ERP remediation. |
| Stale Review Analyzer | 20 open review items are older than 30 days | HIGH | 76% | 20 | Escalate stale review items and close accepted/rejected decisions with notes. |
| Low Confidence Control Analyzer | 5 findings require stronger confidence treatment | MEDIUM | 70% | 5 | Keep low-confidence outputs in review and enrich source evidence in the next upload. |
Evidence Records
| ID | Confidence tier | Severity | Description | Value | Source | Reason codes |
|---|---|---|---|---|---|---|
| E-68825a71 | Medium Confidence | MEDIUM | 24 rows show unresolved governance review items | 0 | row:1 | site-context |
| E-a4fba426 | Medium Confidence | MEDIUM | 24 rows show unresolved governance review items | 0 | row:2 | site-context |
| E-55063976 | Medium Confidence | MEDIUM | 24 rows show unresolved governance review items | 0 | row:3 | site-context |
| E-89e49f45 | Medium Confidence | MEDIUM | 24 rows show unresolved governance review items | 0 | row:4 | site-context |
| E-b91e0435 | Medium Confidence | MEDIUM | 24 rows show unresolved governance review items | 0 | row:5 | site-context |
| E-5b515d44 | Medium Confidence | MEDIUM | 24 rows show unresolved governance review items | 0 | row:6 | site-context |
| E-f22a1458 | Medium Confidence | MEDIUM | 24 rows show unresolved governance review items | 0 | row:7 | site-context |
| E-d67ce50a | Medium Confidence | MEDIUM | 24 rows show unresolved governance review items | 0 | row:8 | site-context |
| E-3246cacf | Medium Confidence | HIGH | 20 open review items are older than 30 days | 0.0 | row:1 | site-context |
| E-dd70e101 | Medium Confidence | HIGH | 20 open review items are older than 30 days | 0.0 | row:2 | site-context |
| E-e2afda64 | Medium Confidence | HIGH | 20 open review items are older than 30 days | 0.0 | row:3 | site-context |
| E-ef59a61e | Medium Confidence | HIGH | 20 open review items are older than 30 days | 0.0 | row:4 | site-context |
| E-9261e71e | Medium Confidence | HIGH | 20 open review items are older than 30 days | 0.0 | row:5 | site-context |
| E-15217d74 | Medium Confidence | HIGH | 20 open review items are older than 30 days | 0.0 | row:6 | site-context |
| E-ccb36fc9 | Medium Confidence | HIGH | 20 open review items are older than 30 days | 0.0 | row:7 | site-context |
| E-cd259bf4 | Medium Confidence | HIGH | 20 open review items are older than 30 days | 0.0 | row:8 | site-context |
| E-74b29491 | Medium Confidence | MEDIUM | 5 findings require stronger confidence treatment | 0.0 | row:4 | site-context |
| E-76ee116c | Medium Confidence | MEDIUM | 5 findings require stronger confidence treatment | 0.0 | row:9 | site-context |
| E-879f8d5a | Medium Confidence | MEDIUM | 5 findings require stronger confidence treatment | 0.0 | row:14 | site-context |
| E-3975b789 | Medium Confidence | MEDIUM | 5 findings require stronger confidence treatment | 0.0 | row:19 | site-context |
| E-a08a9a95 | Medium Confidence | MEDIUM | 5 findings require stronger confidence treatment | 0.0 | row:24 | site-context |
Recommended Actions
P1
Close, reject, or assign open findings before ERP remediation.
Owner: CISO | Due: 60 days
P0
Escalate stale review items and close accepted/rejected decisions with notes.
Owner: CISO | Due: 30 days
P1
Keep low-confidence outputs in review and enrich source evidence in the next upload.
Owner: CISO | Due: 60 days
Mapping and validation
| Input | Source column | Completeness | Confidence | Reason |
|---|---|---|---|---|
| finding_id | finding_id | 100.0% | 100.0% | Exact ERP alias |
| finding_type | finding_type | 100.0% | 100.0% | Exact ERP alias |
| confidence | confidence | 100.0% | 100.0% | Exact ERP alias |
| business_impact | business_impact | 100.0% | 100.0% | Exact ERP alias |
| review_status | review_status | 100.0% | 100.0% | Exact ERP alias |
| reviewer | reviewer | 100.0% | 100.0% | Exact ERP alias |
| source_record | source_record | 100.0% | 100.0% | Exact ERP alias |
| action_owner | action_owner | 100.0% | 100.0% | Exact ERP alias |
| decision_reason | decision_reason | 100.0% | 100.0% | Exact ERP alias |
| action_due_date | action_due_date | 100.0% | 100.0% | Exact ERP alias |
| created_at | created_at | 100.0% | 100.0% | Exact ERP alias |
| site | site | 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 | 10 |
| optional total | 10 |
| required completeness | 100.0 |
| row count | 24 |
| column count | 38 |
| 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 |
|---|---|---|---|---|
| Finding Id | finding_id | FND-001 | FND-001 | Normalize finding, exception, alert, issue, or recommendation identifiers. |
| Finding Type | finding_type | duplicate_candidate | duplicate_candidate | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Confidence | confidence | 85 | 85 | Parse confidence percentage/tier and route low confidence into human review. |
| Business Impact | business_impact | Working capital and maintenance readiness review | Working capital and maintenance readiness review | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Review Status | review_status | open | OPEN | Normalize open, accepted, rejected, deferred, needs-review, and assigned states. |
| Reviewer | reviewer | Owner Review Queue | Owner Review Queue | Normalize reviewer, steward, approver, or auditor name. |
| Source Record | source_record | row:1:MAT-001-001 | row:1:MAT-001-001 | Retain source row, record ID, transaction reference, or evidence pointer. |
| Action Owner | action_owner | Materials Manager | Materials Manager | Normalize action owner, responsible role, and due-owner context. |
| Decision Reason | decision_reason | Sample finding open for review | Sample finding open for review | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Action Due Date | action_due_date | 2026-07-02 | 2026-07-02 | Normalize blanks, trim source values, preserve original evidence, and label any assumptions before engine execution. |
| Created At | created_at | 2026-06-02 | 2026-06-02 | Parse creation timestamp into review age and audit sequence. |
| Site | site | Plant-2 | Plant-2 | Normalize plant, site, storeroom, facility, depot, or operating-unit labels. |
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.