AV
ProgramIQ
Autonomous Vehicle Program Intelligence Platform · Jira · GitHub · Simulation · Fleet Telemetry · PLM
AI learning from 2,341 AV program outcomes
ENV: PROD · ROLE: SR. PROGRAM MGR
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 28
StaffingIQ 7
AV Program Operations
Continuous program execution intelligence across autonomy, validation, fleet, and manufacturing readiness
◊ Knowledge graph: 61,482 linked program artifacts
Program Delivery Health
76%
↑ 9pts vs last quarter
Milestone Predictability
71%
↓ 4pts — 3 slips detected
V&V Closure Rate
84%
↑ 12pts this cycle
Coordination Overhead
-28%
vs. pre-IQ baseline
Open Delivery Risks
10
3 critical · 7 high
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
28 outcomes learned
Program Coordination Time — 12-week trend
Hours per engineer per week spent on non-delivery coordination
Engineering Time Recaptured
Hours/week recovered through AI-assisted coordination
Dependency Coverage Health
Cross-domain program dependency mapping completeness
Signal Mix — Last 30 Days
By program domain
AI Confidence Distribution
Across active signals
Top AV Program Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — AV Program Signal Detection
AI continuously monitoring Jira, GitHub, simulation environments, fleet telemetry, PLM, safety repositories, and supplier systems
● Connected to 14 AV program 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 88%
AI Recommendations
5
Pending review
Signals 10
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 5
DecisionIQ — Initiative Management
Active AV program initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 7 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,341 historical AV program outcomes to improve every recommendation below
Initiative owners
92% acceptance
Task owners
87% acceptance
Closure paths
79% accepted
Follow-on tasks
68% 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 AV program recommendations
● Continuous learning active
Outcomes Captured
2,341
↑ 187 this month
SME Corrections
43
→ model refinement
Initiative Success Rate
81%
↑ from 67% (Q1)
AI Recommendation Accuracy
88%
↑ 14pts 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
StaffingIQ — Team Capacity & Workforce Intelligence
AI continuously monitoring team load, skill gaps, attrition risk, and hiring requirements across AV program domains
● Analyzing 12 teams · 186 engineers
Teams Over Capacity
7
↑ 3 vs last quarter
Avg Team Load Index
118%
↑ 14pts — above threshold
Critical Skill Gaps
11
Across 5 domains
Open Headcount Needed
24
To restore healthy load
Attrition Risk — High
9
Engineers flagged
Team Overview
Capacity Signals
Demand Forecast
Recommended Actions
Team Load Heatmap — click any card to drill in
Load Index = (Active workload hours) ÷ (Available capacity hours) × 100. AI flags teams above 105%.
● >120% overloaded ● 110–120% stressed ● 100–110% at capacity ● <100% healthy
Load Index Trend — Last 6 Quarters
Average team load across all AV program domains
Headcount Gap by Domain
FTEs needed to restore load index below 100%
AI-detected capacity and staffing risk signals — ranked by program delivery impact
Workload Demand Forecast — Next 4 Quarters
AI projection based on program roadmap, ODD expansion, and historical velocity patterns
● AI projection
Hiring Priority Queue
Roles AI recommends sourcing first based on bottleneck impact
Skill Coverage Matrix
Current coverage vs. program-required proficiency by domain — red cells indicate critical gaps
11 critical gaps identified
AI-recommended actions to improve team capacity, reduce load index, and close skill gaps — ranked by delivery impact
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.