EG
ExecutableGrowthIQ
Backlog-to-Growth Intelligence Platform · IGT OEM Pathfinder
AI learning from 1,940 backlog-conversion outcomes
ENV: PROD · ROLE: CRO / COO
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 22
ArchitectureIQ
OntologyIQ 28,400 linked entities
KnowledgeIQ 6
ReasoningIQ
Executable Growth Operations
Connecting Commercial, Operations, Supply Chain, Engineering & Quality, and Finance into one view of what backlog can convert to profitable revenue
◊ Knowledge graph: 28,400 linked artifacts
Executable Backlog
$1.27B
↑ from $1.14B last quarter
Profitable Growth Capacity
$120M
of $530M backlog requiring attention
Constraint Coverage
92%
of at-risk backlog traced to root cause
Backlog Acceleration Cycle Time
-31%
↓ from 46-day avg to root-cause + recommend
Open Growth-Blocking Risks
7
3 critical
Continuous Intelligence Loop
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
Executable Backlog — 12-week trend
Weekly executable backlog value as constraints are resolved
Backlog Value Released by Intervention Type
$ released per constraint category, trailing quarter
Systems-of-Record Linkage Health
Salesforce · SyteLine · Data Lake freshness feeding the graph
Signal Mix — Last 30 Days
By domain
AI Recommendation Confidence Distribution
Across all active recommendations
Top Growth-Blocking Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Enterprise Backlog Signal Detection
Signals surfaced across Salesforce/EDI, Infor SyteLine ERP, and Azure Data Lake/Databricks/Power BI
● Connected to 4 enterprise 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 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active backlog-acceleration initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 6 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,940 historical backlog-conversion 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 program manager signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at backlog-to-growth recommendations
● Continuous learning active
Outcomes Captured
1,940
↑ 140 this month
SME Corrections
54
→ model refinement
Initiative Success Rate
84%
↑ from 76% (Q1)
AI Recommendation Accuracy
91%
↑ 6pts vs baseline
Failed Closures Studied
11
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
ArchitectureIQ — The Decision Layer on Barnes' Existing Systems
Barnes is already investing in ERP modernization, multi-ERP integration, cloud data architecture, and analytics. This is the next layer — Ontology + Knowledge Graph + AI Agents + Reasoning — connected on top, not another migration.
◊ No system of record is replaced
Systems Connected
7
Salesforce, EDI, SyteLine, Azure stack, Power BI
Entity Types Modeled
9
See OntologyIQ schema
Reasoning Chains Run
312
Traversing the connected systems below
Root-Cause Time Reduction
-63%
vs. manual cross-system reconciliation
ERP Instances Pending Coverage
2
See INIT-2216
From Systems of Record to a Decision Layer
Barnes' current hiring and infrastructure investment already builds the foundation. Click any layer to jump to it.
Traverse the Enterprise, Not Individual Applications
One business question — like "can we accept another $25M of IGT business" — travels this entire chain instead of stopping at any one system
Connected Systems of Record
Click a system to see what it contributes to the graph
OntologyIQ — enterprise backlog-to-growth Knowledge Graph
A living semantic model connecting every entity and risk relationship across active enterprise backlog-to-growth work
◊ 28,400 linked entities · 64,100 relationships
Entity Types Modeled
9
Customers, Opportunities, Contracts, Programs, Backlog, Components, Suppliers, Facilities, Quality
Relationship Types
12
requires · supplies · qualifies · scheduled-on · constrains · governed-by…
Graph Freshness
96%
Entities updated within the last cycle
Query Accuracy (validated)
93%
Against SME-reviewed answer set
Ask the Knowledge Graph
Not sure where to start? Try a sample question below, or type your own — answers are traced back to the underlying graph nodes and edges.
◊ Grounded answers only
Knowledge Graph Schema
Core entity and relationship types behind every answer
Highest-Connectivity Entities
Nodes with the most cross-domain relationships — usually the highest-leverage risk points
KnowledgeIQ — Institutional & Tacit Knowledge Capture
Tracks where program & engineering know-how is captured, at risk, or missing entirely, and visualizes it as a knowledge graph
◊ Linked to the OntologyIQ knowledge graph
Knowledge Assets Captured
27
↑ 5 this month
Experts at Retirement/Rotation Risk
6
2 uncaptured
Capture Coverage
71%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
13d
↓ from 21d last quarter
Knowledge Graph — Experts, Assets, Programs & Sites
Instance-level view of the graph: who holds the knowledge, what's been captured, and which programs and sites it feeds. Click any node.
At-Risk Expertise
Operators and specialists nearing retirement or rotation, ranked by capture status
Knowledge Asset Library
Captured video, process sheets, and annotated drawings, linked to the program and initiative they came from
ReasoningIQ — Evidence-Backed enterprise growth Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple enterprise growth factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
312
↑ this week
Avg. Factors Evaluated
4.8
per recommendation
Avg. Recommendation Confidence
88%
Grounded in graph evidence
Executive Recommendations Delivered
16
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
Salesforce · EDI · Infor SyteLine · Azure Data Lake / Databricks · Power BI
5 systems connected
→
Enterprise Ontology
9 entity types
→
Knowledge Graph
28,400 linked entities
→
Reasoning Engine
312 chains run
→
Business Users
Executive decisions
Reasoning Scenarios
Each scenario evaluates multiple factors across the enterprise before producing a recommendation — click a scenario to see the full evidence chain
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.