Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
22 outcomes learned
Sourcing Cycle Time — 12-week trend
Median days requisition → PO, by category
Buyer Time Recaptured
Hours/buyer/week returned by AI assistance, by activity
Spend Coverage Health
On-contract vs off-contract vs tail spend
Signal Mix — Last 30 Days
Procurement signals by category
AI Confidence Distribution
Confidence across active recommendations
Top Procurement Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Procurement Signal Detection
Signals surfaced across ERP/P2P spend, SRM supplier data, CLM contracts and market price indices
● Connected to 7 procurement systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
6
3 high-strength
RCAs in Progress
4
Avg conf 86%
AI Recommendations
5
Pending review
Signals 10
Patterns 5
Correlations 6
Root Cause Analysis 4
Recommendations 5
DecisionIQ — Initiative Management
Active procurement initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives5 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,043 historical procurement outcomes to improve every recommendation below
Initiative owners
94% acceptance
Task owners
89% acceptance
Closure paths
82% accepted
Follow-on tasks
71% accepted
ActionIQ — Execution Coordination
Two-tier Kanban: initiatives at the top, task drill-down on click
Initiatives Kanban
AI Operational Copilot
Live workflow optimizations across the board
Automation Opportunities
Workflows AI can orchestrate end-to-end (with category-manager signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at procurement recommendations
● Continuous learning active
Outcomes Captured
2,043
↑ 142 this month
SME Corrections
64
→ model refinement
Initiative Success Rate
84%
↑ from 78% (Q1)
AI Recommendation Accuracy
91%
↑ 7pts vs baseline
Failed Closures Studied
19
Root-caused
Recent Learning Events
Outcomes from closed initiatives feeding back into the recommendation model
Improvement Over Time
Recommendation acceptance rate by quarter
What's Working (Reinforced)
Patterns the AI is doubling down on
What's Not Working (Down-weighted)
Patterns the AI is moving away from
(after the 5 core panels; after staffingiq-panel.html if present).
Pure structure — only hook IDs the engine populates. No tokens to fill.
The four panels are rendered lazily by knowledgeiq.js on first tab open.
Panel order follows the deck narrative:
Ontology -> Semantic Models -> Knowledge Graph -> ArchitectureIQ
(file order below is arch-first only because the source build was authored
that way; tab/nav order is controlled by the nav block, not this file.)
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How fragmented systems, documents and expertise become connected, governed intelligence — from data sources to reasoning agents
◊ 10 source systems · 6 domains
Most AI programs invest in models, GPUs and agents. The highest-performing programs invest in context, relationships and business meaning. ArchitectureIQ is the layered stack that turns enterprise data into that context. Select a layer to inspect it.
Core Domains
All domains become connected through the ontology and graph
AI Agents Across the Enterprise
Specialized agents reasoning over the shared graph
From Search to Reasoning
Enterprise value increases as the platform moves up the stack
Business Value Framework
Typical outcome ranges across the program
OntologyIQ — Enterprise Ontology
The common language of the factory: a consistent understanding of business concepts across every system and location
◊ Steward: Knowledge & Ontology team
The ontology creates the vocabulary; the knowledge graph creates the relationships. Select an entity class to see its properties, source systems and how it connects.
Entity Classes
Relationship Types
Typed connections that give the graph its meaning
A defect is never just a defect — it is associated with a component, supplied by a vendor, manufactured at a plant, and resolved by a corrective action.
SemanticIQ — Semantic Models
Where raw system fields are mapped to governed business concepts — so every system speaks the same language
◊ Semantic model v2.3
System-to-Concept Mappings
Source field → canonical ontology concept, with steward and governance status
Source System / Field
Canonical Concept
Domain
Steward
Status
Business Glossary
Shared definitions that anchor the semantic layer
Example Semantic Triples
Subject → predicate → object statements the model asserts
KnowledgeIQ — Living Knowledge Graph
A connected digital representation of the factory — query it in natural language and watch the platform reason across relationships
◊ 48,217 linked artifacts
✦
Engineering Copilot
Ask a question — the copilot reasons across the graph and highlights the entities behind its answer. Days become minutes.
Drag nodes · click to inspect
Add Custom Task
AI has pre-filled this based on the initiative's closure path
AI Pre-fill — Suggestions below are editable. Owner defaults to AI's recommendation.