GridRestore Prioritizer

AI-guided restore prioritization for distribution network market systems, helping teams sequence system and dataset recovery during disaster events to restore critical market functions faster.

The Problem

“Disaster recovery teams need faster, defensible restore sequencing for market-critical distribution systems”

Organizations face these key challenges:

1

Static disaster recovery runbooks do not reflect real-time outage conditions

2

System and dataset dependencies are incomplete, outdated, or scattered across tools

3

Teams must make high-stakes restore decisions under severe time pressure

4

Critical market functions can be delayed by restoring lower-value components first

Impact When Solved

Shortens restoration time for market-critical systems and datasetsImproves sequencing accuracy across application, data, and infrastructure dependenciesProvides auditable rationale for recovery decisions during regulated eventsReduces reliance on tribal knowledge held by a few senior responders

The Shift

Before AI~85% Manual

Human Does

  • •Review outage conditions and identify affected market systems and datasets
  • •Assess criticality, dependencies, and recovery objectives using runbooks and spreadsheets
  • •Debate and set restore order across application, data, infrastructure, and operations priorities
  • •Update the recovery plan as new incident information and constraints emerge

Automation

  • •No AI-driven prioritization or dependency analysis is used
  • •No continuous monitoring of changing recovery conditions is performed
  • •No automated ranking of restore actions with rationale is generated
With AI~75% Automated

Human Does

  • •Approve the recommended restore sequence for critical market functions
  • •Resolve conflicts between operational priorities, regulatory needs, and resource constraints
  • •Handle exceptions when field conditions or business priorities change unexpectedly

AI Handles

  • •Continuously analyze system criticality, dependencies, outage scope, and recovery objectives
  • •Rank systems, interfaces, databases, and datasets for restoration with rationale and confidence
  • •Monitor incident updates and re-prioritize recovery actions as conditions change
  • •Simulate recovery tradeoffs and flag bottlenecks or sequencing risks

Operating Intelligence

How it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence97%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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