Energy & Utilities

Reimagine grid operations, field dispatch, and asset reliability.

Roles and workflows

Three roles in energy ops, where people and AI work together

Pattern A

Field service dispatch

Dispatch coordinator

Responsibilities to review

  • Read work order and asset location
  • Match technician skills and availability
  • Pull prior service history
  • Draft dispatch with route and ETA

Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.

AI reads the work order, asset location, technician skills and availability, prior service history. Drafts the dispatch with route and ETA. Coordinator reviews exceptions (storm response, regulatory escalations).

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Pattern B

Grid operations

Grid operations specialist

Responsibilities to review

  • Read SCADA telemetry and weather data
  • Cross-reference demand forecasts
  • Pull prior similar anomalies
  • Draft response plan with switching orders

Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.

AI reads SCADA telemetry, weather data, demand forecasts, prior anomalies. Drafts the response plan with switching orders pre-validated. Operator reviews and authorizes the action.

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Pattern C

Asset reliability

Reliability engineer

Responsibilities to review

  • Read condition monitoring data
  • Review prior failure modes
  • Pull similar-asset history across the fleet
  • Draft reliability assessment with prioritized actions

Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.

AI reads condition monitoring data, prior failure modes, similar-asset history across the fleet. Drafts the reliability assessment with prioritized actions. Engineer reviews the trade-offs.

See the role detail →
12,380
Real job postings in this industry
524
Companies in this industry
10,051
Tasks mapped to industry roles
369
Source examples classified for AI support

Prepare your workforce for the redesigned work.

Your workforce brings people and AI agents together. Workflows define how they share the work. Define what agents do, who reviews their output, and when they hand work back. Use Task Intelligence to understand the tasks within each step, then prepare your people to build and run the new version through simulations, hands-on projects and skill validation. Revisit affected responsibilities and readiness when the work changes.

Task evidence within energy workflows

These research inputs include vendor and source-reported software capabilities, product features and descriptions of work. The stored classifications in this selection contain 369 source examples for AI support and 505 for automation. They are not validated workflow outcomes or proof of agent performance. Task Intelligence helps examine the work in context; review each example against your responsibilities, controls and exceptions before using it in a redesign. The first 5 AI-support examples are below.

Augment
MicroJet performs in-stream quality control
Source context: Automated Single-Stream Recycling Sortation · AMP Robotics
Augment
Delta robotic arm handles specialty sortation
Source context: Automated Single-Stream Recycling Sortation · AMP Robotics
Augment
Quality verification before output
Source context: Municipal Solid Waste (MSW) Processing · AMP Robotics
Augment
AMP designs customized facility layout
Source context: Operations & Maintenance as a Service for Waste Facilities · AMP Robotics
Augment
Builds and deploys AMP ONE sortation systems
Source context: Operations & Maintenance as a Service for Waste Facilities · AMP Robotics
505 source examples classified for automation›
Automate
Vision system performs real-time material characterization
Source context: Automated Single-Stream Recycling Sortation · AMP Robotics
Automate
Jet high-volume air-jet system separates materials at high speed
Source context: Automated Single-Stream Recycling Sortation · AMP Robotics
Automate
Vac system provides vacuum-based separation for fine materials
Source context: Automated Single-Stream Recycling Sortation · AMP Robotics
Automate
AMP ONE facility-scale system ingests MSW
Source context: Municipal Solid Waste (MSW) Processing · AMP Robotics
Automate
AI vision characterizes materials in real time
Source context: Municipal Solid Waste (MSW) Processing · AMP Robotics
Automate
Multi-technology sortation separates organics, recyclables, and residuals
Source context: Municipal Solid Waste (MSW) Processing · AMP Robotics
Automate
ANYmal legged robot navigates complex terrain including stairs and confined spaces autonomously
Source context: Autonomous Robotic Inspection for Oil & Gas Facilities · ANYbotics
Automate
Performs visual, thermal, gas, and acoustic inspections at scheduled waypoints
Source context: Autonomous Robotic Inspection for Oil & Gas Facilities · ANYbotics
Automate
Transmits real-time data to Flotilla fleet management system
Source context: Autonomous Robotic Inspection for Oil & Gas Facilities · ANYbotics
Automate
Runs missions without local supervision
Source context: Autonomous Robotic Inspection for Oil & Gas Facilities · ANYbotics
Automate
ANYmal deployed at facility after lab testing (e.g., Equinor K-Lab)
Source context: Carbon Capture & Storage (CCS) Facility Inspection · ANYbotics
Automate
Robot runs first autonomous mission via Flotilla within two weeks of deployment
Source context: Carbon Capture & Storage (CCS) Facility Inspection · ANYbotics
Automate
Continuous monitoring without local supervision
Source context: Carbon Capture & Storage (CCS) Facility Inspection · ANYbotics
Automate
Transmits inspection data to operators remotely
Source context: Carbon Capture & Storage (CCS) Facility Inspection · ANYbotics
Automate
ANYmal performs regular thermal and visual inspections of power generation assets
Source context: Power & Utilities Asset Inspection with Predictive Maintenance · ANYbotics
Automate
Inspection data fed into GE Vernova APM for predictive analytics
Source context: Power & Utilities Asset Inspection with Predictive Maintenance · ANYbotics
Automate
Extract alarm configuration data from DCS and SCADA systems for baseline analysis
Source context: Alarm Management and Rationalization · AVEVA
Automate
Analyze alarm metrics: alarm rate per operator, standing alarms, chattering alarm frequency
Source context: Alarm Management and Rationalization · AVEVA
Automate
Identify top offending alarms by frequency, duration, and operator response patterns
Source context: Alarm Management and Rationalization · AVEVA
Automate
Conduct alarm rationalization workshop with operators and process engineers for each process unit
Source context: Alarm Management and Rationalization · AVEVA
Automate
Document alarm rationale: purpose, consequence of no action, operator response, and priority classification
Source context: Alarm Management and Rationalization · AVEVA
Automate
Recommend alarm setpoint changes, dead-band adjustments, and suppression strategies
Source context: Alarm Management and Rationalization · AVEVA
Automate
Implement approved alarm changes in DCS/SCADA with management of change documentation
Source context: Alarm Management and Rationalization · AVEVA
Automate
Monitor post-implementation alarm performance against ISA-18.2 benchmark targets
Source context: Alarm Management and Rationalization · AVEVA
Automate
Track alarm system KPIs monthly: alarms per hour, alarm flood incidents, acknowledged vs shelved
Source context: Alarm Management and Rationalization · AVEVA
Automate
Conduct periodic re-rationalization as process conditions or operating procedures change
Source context: Alarm Management and Rationalization · AVEVA
Automate
Integrity engineer defines asset population: pressure vessels, heat exchangers, piping, storage tanks
Source context: Asset Integrity and Inspection Planning · AVEVA
Automate
Populate equipment data: design conditions, materials of construction, corrosion allowance, service conditions
Source context: Asset Integrity and Inspection Planning · AVEVA
Automate
Conduct risk-based inspection (RBI) assessment per API 580/581 methodology
Source context: Asset Integrity and Inspection Planning · AVEVA
Automate
Calculate probability of failure based on damage mechanisms, corrosion rates, and inspection history
Source context: Asset Integrity and Inspection Planning · AVEVA

How we work with energy operators

Co-sponsored model. COO, VP Field Ops, Chief Reliability Officer, CTO plus your transformation office in the room. The 6 to 8 week AI Bootcamp prepares people for agreed workflow responsibilities through simulations and assessments. Transformation Engagement scales across the operating model, same OT/SCADA, same NERC/FERC envelope.

AI Bootcamp
6 to 8 weeks
One operating area, workflow practice and assessment
Transformation Engagement
Custom
Operating-model redesign across field service, grid ops, asset reliability, procurement
Implementation
Same OT stack
Same crews, same OT/SCADA, AI cowork layer on top

Want an energy-specific walkthrough?

20 minutes. We pull your top three task patterns from the dataset and show you the redesign live, with your role mix and your reliability frame.

Book a time