Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
7 initiatives
→
ActionIQ
6 in execution
→
EffectivenessIQ
31 outcomes learned
SiC Qualification Cycle Time — 12-Week Trend
Average weeks per qualification cycle · target: 14 weeks
Engineering Time Recaptured
Hours recovered via AI-assisted workflows · weekly
ML Inspection Model Health
Recall rate by production line · 12-week trend
Signal Mix — Last 30 Days
By engineering domain
AI Confidence Distribution
Across all active recommendations
Top Program Risks — AI-Prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Power & Silicon Signal Detection
AI monitoring qualification pipelines, inspection ML models, hyperscaler validation data, fab utilization, and demand signals across the power and silicon portfolio
● Connected to 9 engineering and operations systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
7
3 high-strength
RCAs in Progress
4
Avg conf 86%
AI Recommendations
6
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active power and silicon initiatives created from accepted signals, with AI-suggested follow-on tasks
7 active initiatives5 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,314 historical engineering 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 engineering board
Automation Opportunities
Workflows AI can orchestrate end-to-end (with program lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is improving power and silicon engineering recommendations
● Continuous learning active
Outcomes Captured
2,314
↑ 124 this month
SME Corrections
47
→ model refinement
Initiative Success Rate
78%
↑ from 71% (Q1)
AI Recommendation Accuracy
86%
↑ 11pts vs baseline
Failed Closures Studied
29
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
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.