GI
GridIQ
Intelligent Grid Operations Platform · SCADA · ADMS · EMS · DERMS
AI learning from 2,360 grid operational outcomes
ENV: PROD · ROLE: GRID OPS LEAD
Dashboard
TrendIQ 9
DecisionIQ 7
ActionIQ 7
EffectivenessIQ 27
StaffingIQ 7
Intelligent Grid Operations
Continuous operational intelligence across grid modernization, DER orchestration, and large-load integration
◊ Knowledge graph: 58,140 linked grid assets & events
Grid Operational Health
96.4%
↑ 1.8 pts this quarter
DER Observability Coverage
88.1%
↑ 6 pts
Automated Dispatch Coverage
73.5%
↑ coordination scaling
Operator Response Time
-29%
Faster vs. baseline
Open Grid Risks
9
2 critical
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
9 active signals
→
DecisionIQ
7 initiatives
→
ActionIQ
6 in execution
→
EffectivenessIQ
27 outcomes learned
Operator Response Time — 12-week trend
Mean time to coordinate a dispatch action, by domain
Operator Time Recaptured
Hours/operator/week before vs. after AI assistance
DER Observability Health
Live vs. stale vs. missing telemetry
Signal Mix — Last 30 Days
Grid signals by domain
AI Confidence Distribution
Across active recommendations
Top Grid Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Grid Signal Detection
Signals surfaced from SCADA, ADMS, EMS, DERMS, GIS, PMUs, and grid-edge IoT telemetry
● Connected to 10 grid systems
Active Signals
9
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
7
3 high-strength
RCAs in Progress
3
Avg conf 86%
AI Recommendations
5
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active grid initiatives created from accepted signals, with AI-suggested follow-on tasks
7 active initiatives 4 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,360 historical grid operational 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 operator signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at grid operational recommendations
● Continuous learning active
Outcomes Captured
2,360
↑ 142 this month
SME Corrections
63
→ model refinement
Initiative Success Rate
84%
↑ from 71% (Q1)
AI Recommendation Accuracy
91%
↑ 12 pts vs baseline
Failed Closures Studied
14
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 grid operations domains
● Analyzing 8 teams · 130 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 grid operations 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 grid modernization roadmap, interconnect pipeline, 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.