Power Distributors and Dispatchers
51-8012.00Median wage $106,730/yr8,520 employed (US)Rank #549 of 923 scored · top 59% by substitution
Coordinate, regulate, or distribute electricity or steam.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution25
Exposure27
Augmentation63
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
14 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
7%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%26
panel mean rating 2.1/5 → substitution pressure 26/100
Technical feasibility todayw 20%29
panel mean rating 2.2/5 → substitution pressure 29/100
Cost vs. human wagew 15%32
panel mean rating 2.3/5 → substitution pressure 32/100
Adoption barriersw 20%inverted — strong barriers lower the score14
panel mean rating 4.5/5 (barrier strength) → substitution pressure 14/100
Sector adoption velocityw 10%27
panel mean rating 2.1/5 → substitution pressure 27/100
Task breakdown (14 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Record and compile operational data, such as chart or meter readings, power demands, or usage and operating times, using transmission system maps.
71CI 67–74 · exposure 75 · augmentation 75 · importance 3.9/5 · click for rater detail
Record and compile operational data, such as chart or meter readings, power demands, or usage and operating times, using transmission system maps.
71| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | The electric utility sector has been rapidly digitizing operations over the past decade. SCADA, advanced metering infrastructure (AMI), and real-time monitoring systems are now standard in most large utilities, with accelerating deployment in regional and smaller systems. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Utilities are a moderately conservative, capital-intensive sector; automated data collection is common but full AI-driven compilation and interpretation adoption is still uneven across smaller utilities. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-assisted dashboards, anomaly detection, and predictive analytics on operational data significantly enhance dispatcher productivity by surfacing critical trends and deviations. Dispatchers remain in the loop but gain vastly improved situational awareness and decision support. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI and automated systems substantially reduce manual burden of data compilation and can flag anomalies, letting dispatchers focus on interpretation and decision-making while remaining in the loop. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Recording and compiling operational data from meters and charts into structured logs is largely rule-based data extraction and transcription. Modern AI and data pipeline systems can read digital meter data, parse charts, and populate databases automatically, achieving significant time savings. Some manual verification may still be needed for edge cases or anomalies. |
| Task automatability | claude-sonnet-5 | 4/5 | Recording and compiling meter/chart readings and usage data is largely structured data entry and aggregation, which SCADA systems and automated logging/AI pipelines can already handle with high time savings versus manual transcription. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Regulatory compliance and grid reliability requirements create oversight obligations, but no specific legal mandate requires humans to manually record this data. Many utilities have already shifted to automated systems, though some operational and verification requirements persist. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no law requires a human to manually record readings, grid operations are regulated (NERC/FERC compliance) and errors in operational data have safety/reliability implications, creating moderate oversight friction. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated data collection and compilation through existing industrial IoT and SCADA infrastructure costs a fraction of manual meter reading and record-keeping by dispatchers. The per-task cost via inference and integration is orders of magnitude lower than human labor. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated telemetry and data logging systems are far cheaper per data point than manual recording once integrated, though initial SCADA/sensor infrastructure investment is non-trivial. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | SCADA systems, data loggers, and automated telemetry platforms already perform this task in many utilities today. Products like Siemens SCADA, GE DigitalWorks, and cloud-based energy management systems reliably capture and compile operational data at scale in production environments. |
| Technical feasibility today | claude-sonnet-5 | 4/5 | Utilities widely deploy SCADA and automated data historians that continuously log operational data in production, though some manual cross-referencing with transmission maps still occurs in legacy setups. |
Monitor and record switchboard or control board readings to ensure that electrical or steam distribution equipment is operating properly.
49CI 25–74 · exposure 55 · augmentation 75 · importance 4.5/5 · click for rater detail
Monitor and record switchboard or control board readings to ensure that electrical or steam distribution equipment is operating properly.
49| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 4/5 | Utilities and power distribution companies have been early and deep adopters of SCADA automation and remote monitoring. The sector shows high digitization, with widespread deployment of automated monitoring systems reducing manual dispatcher workload in many organizations. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are traditionally slow-moving, highly regulated, and risk-averse regarding automation of safety-critical control functions, resulting in incremental rather than transformative AI adoption. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-driven dashboards, predictive alerts, and automated anomaly flagging significantly enhance dispatcher productivity by filtering signal noise and highlighting critical conditions, allowing dispatchers to focus on exception handling and decision-making rather than routine observation. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enhanced SCADA systems, predictive analytics, and anomaly detection significantly aid dispatchers by flagging issues and trends, improving situational awareness while humans retain control authority. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can monitor sensor readings, detect anomalies, and log data with high reliability. Computer vision and sensor integration can automate the core monitoring and recording function, though some judgment calls about equipment state may still require human oversight, achieving substantial time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | While sensor data monitoring can be automated via SCADA systems with alarms, the full task including judgment-based anomaly detection and response coordination still requires human oversight for grid reliability and safety.4 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Regulatory frameworks (NERC, FERC) and utility standards require documented monitoring and human operator sign-off on critical decisions, creating oversight and compliance friction. However, automation of recording itself faces no strict legal prohibition, though organizational preference for human judgment on interpretation remains. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Grid operation is heavily regulated (e.g., NERC reliability standards) and typically requires certified operators to monitor and respond to control systems, with significant liability for outages or safety incidents. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated monitoring via sensors and software costs far less than maintaining human dispatchers for 24/7 surveillance. Once infrastructure is in place, the marginal cost per reading cycle is negligible compared to loaded labor costs. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Automated monitoring infrastructure exists but requires significant capital investment, integration, and redundant human oversight due to safety-critical nature, keeping all-in costs comparable to or higher than staffing. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed SCADA systems, IoT platforms, and AI-powered condition monitoring products already perform continuous monitoring and anomaly detection in power and utility distribution at scale. Industrial monitoring solutions with automated alerting are mature and reliable in production environments. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | SCADA and automated monitoring systems are mature and widely deployed in utilities, but human dispatchers remain in the loop for interpretation and decision-making rather than being replaced entirely. |
Calculate load estimates or equipment requirements to determine required control settings.
32CI 20–45 · exposure 38 · augmentation 75 · importance 4.3/5 · click for rater detail
Calculate load estimates or equipment requirements to determine required control settings.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Utilities have deployed load forecasting and analysis tools widely, but full automation of control-setting decisions remains rare; adoption is at the pilot and assisted-decision stage rather than autonomous replacement, reflecting both regulatory constraint and risk aversion in critical infrastructure. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are conservative, safety-critical, and slow to adopt full automation in control rooms; while forecasting tools are used, deep AI-driven dispatch automation is still largely pilot-stage. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments dispatcher productivity by automating data ingestion, scenario modeling, and recommendation generation, allowing humans to focus on judgment and validation. Modern power systems use AI-assisted planning tools that raise dispatcher efficiency materially while keeping humans accountable for final decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based load forecasting and optimization tools meaningfully assist dispatchers in calculating estimates and suggesting settings, improving speed and accuracy while humans retain final control authority. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Current AI can perform parts of load estimation using data-driven models and historical patterns, but requires significant domain expertise setup and human verification of control settings that have safety implications. Achieving 50% time savings on the full task (including all edge cases and safety validation) is feasible in routine scenarios but unreliable without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Load estimation calculations can be partially automated via SCADA/EMS software with forecasting algorithms, but translating this into control settings for a live grid requires real-time judgment and accountability that current AI cannot fully replace end-to-end.imating factors. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Power system operations are heavily regulated and require human accountability for control settings; liability for system failures, blackouts, or equipment damage falls on licensed operators who must sign off on critical decisions. Regulatory standards (NERC, FERC) typically mandate human responsibility and real-time decision authority. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Power grid operations are highly regulated (NERC/FERC), require certified dispatchers, and carry severe liability for miscalculations that could cause outages or equipment damage, mandating human sign-off on control settings. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI-powered calculation tools have lower marginal cost per analysis than manual computation, but integration costs, infrastructure, and mandatory human oversight (especially for critical control decisions) keep total cost-per-task near parity with experienced human dispatch staff. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While forecasting software reduces some manual calculation cost, the need for continuous human oversight, redundant verification, and specialized control-room infrastructure keeps the all-in cost comparable to or higher than incremental AI cost savings. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Industry-grade software exists for load forecasting and equipment sizing (e.g., SCADA integration tools, load-flow analysis packages), but these typically require substantial configuration and human interpretation rather than end-to-end automation. Production deployments rely heavily on human dispatchers validating outputs before implementation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Grid management software with load forecasting exists and is used, but final control setting decisions remain human-driven due to safety-critical real-time operational constraints; no product autonomously performs this task fully today. |
Track conditions that could affect power needs, such as changes in the weather, and adjust equipment to meet any anticipated changes.
25CI 20–30 · exposure 30 · augmentation 75 · importance 4.1/5 · click for rater detail
Track conditions that could affect power needs, such as changes in the weather, and adjust equipment to meet any anticipated changes.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Power utilities are traditionally conservative and heavily regulated. While smart grid pilots exist, actual autonomous AI-driven dispatch at scale remains rare. Adoption is hampered by regulatory conservatism, safety criticality, and organizational inertia in a capital-intensive sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are conservative, highly regulated adopters; AI forecasting tools are being piloted but full operational deployment of adaptive control automation is slow and limited. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI weather monitoring, predictive models, and alerting systems significantly enhance dispatcher productivity by surface threats early and recommend adjustments. Dispatchers retain decision authority, but AI-driven dashboards and forecasts materially improve their situational awareness and response time. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based weather and load forecasting significantly improves dispatcher situational awareness and anticipatory planning, meaningfully boosting productivity while humans retain control authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Weather tracking and basic equipment adjustments can be partially automated via sensors and control systems, but anticipating power demand changes and making complex operational decisions requires human judgment. Current systems handle routine adjustments only; the interpretive layer of predicting impact and deciding response remains difficult. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can forecast weather-driven load changes and support decision-making, but real-time grid equipment adjustment requires physical control actions and split-second judgment under safety-critical constraints that remain human-supervised today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Power grid operations are heavily regulated; dispatchers must be licensed and maintain legal responsibility for grid stability. Liability for outages or service failures creates a strong legal requirement that a qualified human sign off on or actively make critical dispatch decisions, limiting substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Power dispatch is a safety-critical, heavily regulated function typically requiring NERC certification and licensed operators, with legal accountability for grid reliability preventing full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Specialized grid monitoring and control infrastructure is capital-intensive and requires skilled integration and maintenance. The cost of building and maintaining AI-driven anticipatory systems rivals or exceeds the cost of dispatcher oversight, especially given liability concerns. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Forecasting software is cheap to run, but the overall task requires maintaining licensed dispatcher staff for equipment control and liability, keeping all-in costs comparable to or only modestly below human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | SCADA and grid management systems exist that monitor weather and automate some equipment responses, but these are narrow-scope and typically require human oversight for non-routine scenarios. Deployed products handle alert and logging; end-to-end autonomous anticipatory adjustment is not reliably production-ready. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Load forecasting tools using weather data are deployed in utilities, but the full task of monitoring conditions and actively adjusting equipment is still performed by certified human dispatchers with AI as a decision-support input. |
Implement energy schedules, including real-time transmission reservations or schedules.
23CI 20–25 · exposure 25 · augmentation 75 · importance 4.3/5 · click for rater detail
Implement energy schedules, including real-time transmission reservations or schedules.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utility companies are conservative sectors with slow digital transformation. While some pilot AI scheduling tools exist, production adoption remains limited to advisory roles rather than autonomous control, reflecting regulatory, safety, and organizational inertia. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utility and grid operations sectors are traditionally slow adopters of full automation for safety-critical real-time control functions, though software-assisted scheduling tools are increasingly common. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist dispatchers by forecasting demand, optimizing dispatch plans, flagging anomalies, and recommending schedules in real time. Such tools meaningfully raise dispatcher productivity while preserving critical human judgment and oversight for real-time transmission decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based forecasting, optimization, and decision-support tools meaningfully help dispatchers manage complex real-time scheduling and contingency analysis while humans retain final authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data analysis and optimization algorithms for energy scheduling, implementing real-time transmission reservations requires dynamic decision-making under uncertainty, coordination with multiple stakeholders, and handling of physical grid constraints that current systems handle only partially. End-to-end automation with 50% time savings at equal quality is not yet demonstrated. |
| Task automatability | claude-sonnet-5 | 2/5 | While some scheduling calculations can be automated, real-time transmission reservation implementation requires continuous judgment about grid conditions, contingencies, and safety that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Critical infrastructure regulations (NERC, FERC) typically require licensed operators to authorize and sign off on transmission schedules. Liability for grid failures, blackouts, and safety incidents creates strong legal and organizational barriers to full automation without qualified human accountability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | NERC certification requirements mandate licensed reliability coordinators/dispatchers for real-time grid operations, and transmission scheduling is heavily regulated with strict liability for grid failures. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI scheduling tools have capital and integration costs, but the human dispatcher wage remains competitive for the complex decision-making involved. Cost-per-task is roughly comparable, not significantly lower, given integration and monitoring overhead. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized grid AI tools require significant integration with SCADA/EMS systems and human oversight for safety-critical decisions, keeping costs comparable to or higher than trained dispatcher labor when reliability is factored in. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Scheduling software exists and some utilities use algorithmic aids, but deployed systems typically require substantial human oversight, real-time judgment calls, and manual intervention during anomalies. No mature product reliably automates the full task of real-time transmission reservation implementation without human operators in the loop. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Grid management software assists with scheduling optimization, but no deployed AI product autonomously implements real-time transmission reservations without certified human dispatchers in control rooms. |
Inspect equipment to ensure that specifications are met or to detect any defects.
21CI 16–25 · exposure 17 · augmentation 50 · importance 4.2/5 · click for rater detail
Inspect equipment to ensure that specifications are met or to detect any defects.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Power utilities are traditionally slow-moving sectors with entrenched processes and high compliance overhead. While pilot drone inspection projects exist, most utilities still rely on manual field inspection. Adoption of automated inspection remains in early stages across the sector. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are traditionally slow adopters of new technology due to safety-critical infrastructure, regulatory approval processes, and legacy systems, though some progress is being made with predictive maintenance pilots. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-assisted tools (automated image analysis, anomaly flagging, scheduling optimization) can help dispatchers prioritize inspections and review field images, modestly increasing productivity. However, the inherent requirement for human judgment and field verification limits the transformative potential of augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-powered sensors, thermal imaging analysis, and predictive analytics can help flag potential defects or anomalies for human dispatchers to investigate, improving efficiency of the inspection process without replacing human judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical inspection of equipment requires visual assessment and sometimes tactile feedback in real-world conditions. While AI vision systems can detect some defects in images, inspecting distributed power equipment in operational environments with variable lighting, occlusion, and the need to make real-time safety judgments remains largely manual. Current systems cannot reliably perform the full end-to-end task at 50% time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | Physical inspection of power distribution equipment requires on-site sensory judgment, hands-on checks, and contextual assessment that current AI cannot perform end-to-end without extensive robotic/sensor infrastructure. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Power distribution is regulated (FERC, NERC, state PUCs) with mandated inspection schedules and documented compliance. Liability for missed defects that cause outages or safety hazards is high, creating asymmetric error costs. Human operators often must sign off on safety-critical inspections, forming a legal barrier to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Power grid equipment inspection is subject to strict regulatory oversight (NERC reliability standards, utility commissions) and liability concerns for grid failures, often requiring certified personnel to verify and sign off on safety-critical findings. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Implementing automated vision systems with sufficient hardware (drones, cameras, edge devices) and integration overhead to monitor distributed networks, plus required human oversight, remains costly relative to dispatch and field inspection wages, especially for the infrequent inspection intervals typical in this domain. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Deploying sensors, drones, and AI analytics systems requires significant capital investment and integration costs that often exceed the marginal cost of human inspection for many facilities, though at scale cost may decrease. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Computer vision products exist for defect detection in controlled settings, but deployed solutions for power distribution equipment inspection are narrow in scope and typically require human verification. No production system reliably performs autonomous field inspection of distributed power equipment to specification without significant human oversight. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-assisted inspection tools exist (drone imagery, thermal camera analysis, sensor anomaly detection) but they are narrow-scope add-ons, not full replacements for comprehensive equipment inspection performed by dispatchers. |
Tend auxiliary equipment used in the power distribution process.
21CI 16–25 · exposure 17 · augmentation 50 · importance 4.1/5 · click for rater detail
Tend auxiliary equipment used in the power distribution process.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utilities are early-stage with AI-assisted monitoring and predictive maintenance but remain cautious on autonomous field operations due to safety and regulatory risk. Actual replacement or autonomous tending in production remains rare; most adoption is in data analytics and alerting layers, not task displacement. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are traditionally slow adopters of new technology due to safety, regulatory, and legacy infrastructure constraints, though SCADA and monitoring systems have been adopted gradually over decades. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools for predictive maintenance, sensor-based anomaly detection, and work-order optimization do meaningfully assist dispatchers and field technicians by flagging issues and reducing manual monitoring burden, though the assistant remains human-dependent for final decisions and actions. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-enabled sensors and predictive maintenance tools can alert dispatchers to equipment issues and optimize maintenance schedules, meaningfully assisting but not replacing the physical tending role. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Tending auxiliary equipment involves physical inspection, real-time monitoring, and reactive troubleshooting in dynamic field environments. While AI can assist with anomaly detection and predictive maintenance on sensor data, the physical manipulation, live field response, and safety-critical decision-making in variable conditions remain largely manual tasks that current AI cannot execute end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a hands-on physical monitoring and maintenance task involving auxiliary equipment (transformers, switches, capacitor banks) that requires physical presence and manual intervention, which current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Power distribution is heavily regulated; equipment operation and maintenance often require licensed technicians, and safety standards (OSHA, NERC, local utility codes) mandate human verification and sign-off on critical equipment changes. Liability for grid faults or safety breaches creates strong legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Power grid operations are heavily regulated (NERC, utility commissions) and often require certified personnel for safety-critical equipment tending, creating strong regulatory and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Monitoring and alerting tools reduce human workload incrementally, but the cost of deploying comprehensive robotic or autonomous systems for outdoor equipment tending, combined with integration and reliability oversight, exceeds the loaded cost of human technicians who can adapt to variable field conditions. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While remote monitoring systems can reduce some labor costs, the physical tending component still requires human labor, keeping overall cost comparable to or only modestly cheaper than human-only operation. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some narrow components (monitoring telemetry, alerting on fault codes) have deployed solutions in utility SCADA systems, but comprehensive autonomous tending of the full range of auxiliary equipment—including physical adjustments, equipment calibration, and contingency response—lacks reliable production automation systems today. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Sensor-based monitoring and predictive analytics products exist for grid equipment, but actual tending (physical inspection, adjustment, maintenance) still requires human operators on-site; deployed products only partially cover the task. |
Coordinate with engineers, planners, field personnel, or other utility workers to provide information such as clearances, switching orders, or distribution process changes.
20CI 15–25 · exposure 20 · augmentation 50 · importance 4.8/5 · click for rater detail
Coordinate with engineers, planners, field personnel, or other utility workers to provide information such as clearances, switching orders, or distribution process changes.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utility companies are digitizing dispatch but remain conservative with safety-critical coordination functions. Most AI adoption in this sector focuses on monitoring and predictive maintenance; real-time operational coordination remains human-centered due to regulatory and liability constraints. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are traditionally slow-moving, highly regulated, and physically grounded, with AI adoption in dispatch control rooms limited mostly to decision-support pilots rather than autonomous coordination. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist dispatchers by summarizing field reports, flagging communication patterns, or pre-drafting routine notifications, moderately raising their coordination efficiency. However, the task's core—validating clearances and authorizing changes—remains human-driven, limiting transformative impact. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help by summarizing status updates, generating draft switching order documentation, or flagging anomalies, providing useful assistance while humans retain full coordination responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | This task requires real-time coordination, judgment about safety clearances, and contextual communication with multiple stakeholders. While AI could draft routine switching orders or compile information, the decision-making and accountability for clearances—which affect public safety—requires human expertise and cannot be fully automated to meet a 50% time-savings bar today. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves real-time coordination, safety-critical judgment calls, and dynamic communication across multiple stakeholders that current AI cannot reliably replace end-to-end, though it could assist with information logging and drafting. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Power dispatch involves strict regulatory requirements (NERC, state utilities commissions) and federal safety standards that mandate human accountability for clearances and switching orders. Licensed personnel must legally authorize critical grid operations, creating hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Power grid operations are heavily regulated (NERC/FERC reliability standards), require licensed/certified dispatchers, and carry severe liability for miscommunication leading to outages or safety incidents, mandating human sign-off. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI integration for coordination requires significant domain customization, integration with dispatch systems, and human oversight that narrows the cost advantage. The overhead of ensuring safety compliance and human review makes this comparable to or more expensive than direct human coordination. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI could reduce documentation time but the human dispatcher's judgment, liability, and real-time coordination role remains necessary, so cost savings from full automation are not realized. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs end-to-end coordination of power distribution clearances and switching orders in production environments. Chatbots can handle routine queries, but critical power-grid coordination involves liability and safety sign-off requirements that keep AI assistive rather than autonomous. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed products autonomously manage clearance coordination or switching order communication for power grid operators in production; this remains a human-supervised, safety-critical function. |
Distribute or regulate the flow of power between entities, such as generating stations, substations, distribution lines, or users, keeping track of the status of circuits or connections.
20CI 20–20 · exposure 25 · augmentation 75 · importance 4.7/5 · click for rater detail
Distribute or regulate the flow of power between entities, such as generating stations, substations, distribution lines, or users, keeping track of the status of circuits or connections.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utilities are adopting AI-enhanced monitoring and analytics, but adoption of autonomous dispatch remains slow due to regulatory mandates, safety requirements, and the critical nature of grid stability. Most deployments remain pilots or decision-support tools rather than autonomous systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are traditionally slow-moving, highly regulated, and risk-averse, with automation adoption for control-room functions proceeding cautiously and incrementally rather than at pace with software/finance sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments dispatcher productivity through real-time status monitoring, predictive analytics for demand and equipment health, and automated alerts, allowing dispatchers to manage larger grids more efficiently while remaining in control of critical decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | Advanced monitoring, predictive analytics, and automated alarm systems significantly aid dispatchers in tracking circuit status and anticipating problems, meaningfully boosting their situational awareness and efficiency. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can monitor circuit status and provide decision support through real-time data analysis, the task requires judgment in balancing competing grid demands, responding to emergencies, and making live operational decisions that affect critical infrastructure. Current systems cannot reliably handle the full end-to-end responsibility of power distribution without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves real-time control of critical infrastructure with legal responsibility and physical safety consequences; while SCADA and grid management software assist heavily, full end-to-end automation without human oversight is not achievable or permitted today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Power distribution is heavily regulated; most jurisdictions legally require a licensed power systems operator or dispatcher to authorize and oversee flow decisions. Liability for blackouts, equipment damage, and safety incidents creates strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Power grid operation is heavily regulated (NERC/FERC in the US), requires certified operators, and carries severe liability for outages or safety failures, creating hard legal and organizational barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI monitoring and analytics tools cost significantly to deploy and maintain, and they augment rather than replace dispatcher salaries. When factoring in infrastructure integration, cybersecurity, and the need for human oversight, the all-in cost remains comparable to or exceeds retaining experienced dispatchers. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | SCADA/EMS software is already integrated and relatively low marginal cost, but the human dispatcher's oversight role remains mandatory, so AI doesn't yet displace the labor cost meaningfully. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed products exist for monitoring and alerting (SCADA systems, AI-enhanced grid analytics), but no production system today fully automates the dispatch and regulation decisions without a licensed human operator making final calls. Real-world grid operations still require humans in the loop due to unpredictable events and safety criticality. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Automated grid management systems exist and handle much load balancing, but licensed human dispatchers remain required in control rooms for decision-making and anomaly response, so no product fully replaces this task in production. |
Control, monitor, or operate equipment that regulates or distributes electricity or steam, using data obtained from instruments or computers.
19CI 18–20 · exposure 25 · augmentation 63 · importance 4.7/5 · click for rater detail
Control, monitor, or operate equipment that regulates or distributes electricity or steam, using data obtained from instruments or computers.
19| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Power utilities are highly regulated and conservative; AI adoption for autonomous control remains extremely slow, with most deployments still in pilots or limited advisory roles rather than production replacement of dispatcher functions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are traditionally slow-moving, highly regulated, and cautious about adopting autonomous control systems, resulting in incremental automation of monitoring tools rather than deep AI-driven operational control. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist dispatchers via predictive analytics, anomaly detection, and real-time data visualization to flag issues and suggest load-balancing actions, improving human decision-making without yet replacing the operator's judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-driven analytics, anomaly detection, and predictive maintenance tools substantially help dispatchers monitor systems and respond faster to anomalies, even though final control decisions remain human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While monitoring and data collection from instruments/computers can be partially automated, the control and operation of critical energy distribution systems requires real-time human judgment for fault response, load balancing decisions, and safety-critical interventions that current AI systems cannot reliably perform end-to-end without human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Real-time grid/steam control involves safety-critical decisions with physical consequences, high situational variability, and regulatory oversight requirements that current AI cannot fully replace end-to-end despite decision-support tools reducing some monitoring time. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Critical infrastructure regulation, grid reliability standards (NERC), utility licensing requirements, and legal liability for blackouts or safety failures create hard barriers; human operators must legally maintain control and accountability over distribution systems. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Power grid operation is heavily regulated (e.g., NERC certification requirements), demands licensed operators, and carries severe liability for outages or equipment damage, making full automation legally and organizationally very difficult. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration and oversight costs for AI control systems in critical infrastructure are high due to redundancy, validation, and regulatory requirements; the all-in cost remains comparable to or higher than skilled human dispatchers. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Grid control software and sensors already carry significant infrastructure cost, and replacing certified human dispatchers with AI would still require redundant safety systems and human oversight, keeping costs comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | SCADA and power management systems exist and perform narrow monitoring functions, but no deployed AI product autonomously controls power distribution or steam systems at scale; operations remain human-in-the-loop with rule-based automation support rather than AI-driven control. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | SCADA systems and automated alarms exist widely, but full autonomous control of distribution equipment without a human dispatcher in the loop is not deployed in production due to safety and reliability requirements. |
Respond to emergencies, such as transformer or transmission line failures, and route current around affected areas.
18CI 11–25 · exposure 17 · augmentation 75 · importance 4.7/5 · click for rater detail
Respond to emergencies, such as transformer or transmission line failures, and route current around affected areas.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utilities are adopting monitoring and analytics tools, but emergency response automation remains limited to pilots. The sector is risk-averse, heavily regulated, and moves slowly on critical infrastructure changes. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are conservative, highly regulated adopters; AI is being piloted for grid analytics but emergency response control remains largely manual with slow, cautious rollout of automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools for real-time grid visualization, fault prediction, and automated route suggestions can significantly assist human dispatchers in analyzing options and responding faster. These augmentation tools are increasingly deployed in utility control centers. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-based fault detection, predictive analytics, and decision-support dashboards meaningfully speed up dispatcher situational awareness and response planning during emergencies. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with fault detection and route optimization, responding to emergencies requires real-time decision-making under uncertainty, coordination with field crews, and handling novel failure scenarios. Current systems cannot reliably handle the full end-to-end task with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Emergency grid rerouting requires real-time judgment, physical system knowledge, and accountability for safety-critical decisions that current AI cannot autonomously execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Heavy regulatory requirements (NERC standards, grid reliability mandates) and liability concerns mean human dispatchers must legally authorize and sign off on critical routing decisions. Power grid automation is heavily regulated, creating strong adoption barriers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Grid operations are heavily regulated (NERC reliability standards), require certified operators, and carry severe liability for outages or safety incidents, mandating human authority over emergency switching. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI infrastructure, integration, and continuous monitoring cost is substantial, but human dispatchers must remain in control and oversight loops. The all-in cost remains comparable to or higher than the human labor for reliable, safety-critical emergency response. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI decision-support software has licensing and integration costs comparable to or exceeding marginal dispatcher labor cost per incident, especially given required redundancy and human oversight infrastructure. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed AI systems exist for grid monitoring and predictive analytics, but no production systems autonomously manage emergency response and current rerouting end-to-end. Systems remain in pilot or decision-support stages rather than autonomous operation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Decision-support and anomaly-detection tools exist and are used in SCADA/EMS systems, but actual switching and rerouting decisions during emergencies remain human-executed with AI only advising. |
Manipulate controls to adjust or activate power distribution equipment or machines.
18CI 11–25 · exposure 17 · augmentation 63 · importance 4.7/5 · click for rater detail
Manipulate controls to adjust or activate power distribution equipment or machines.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Utilities have adopted remote monitoring and decision-support AI, but actual automated control of distribution equipment remains limited to narrow scenarios (substation automation, SCADA). Human dispatchers retain primary control authority due to safety and regulatory requirements. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are conservative, slow-moving adopters of new control-room AI due to safety-critical infrastructure concerns, though automation of monitoring/analytics is increasing gradually. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI augmentation is strong in this domain: predictive analytics, anomaly detection, and decision-support systems significantly enhance dispatcher productivity and situational awareness, allowing faster, safer decision-making while the dispatcher remains in control of actual commands. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-assisted decision support, anomaly detection, and predictive load forecasting can meaningfully help dispatchers optimize control actions, though the actual physical/electronic manipulation remains human- or automation-system-directed. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Physical manipulation of controls on distributed equipment requires embodied robotics at scale, which is not yet reliably deployed. While remote monitoring and decision-support systems exist, the actual control actuation across varied physical layouts remains a significant barrier. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically manipulating controls on power distribution equipment requires real-time integration with SCADA systems and physical/electronic switching; current general AI cannot end-to-end operate these controls safely without dedicated control-system automation, not general AI capability transfer. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory barriers exist: operators must be licensed, grid reliability is safety-critical, and liability for incorrect control manipulation is severe. NERC and utility regulations typically require a qualified human to authorize or execute critical power distribution actions. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Power grid operation is heavily regulated (NERC reliability standards, utility commissions) and typically requires licensed/certified operators with legal accountability for grid stability and safety, creating hard regulatory and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Fully autonomous robotic systems capable of physically manipulating distributed equipment are capital-intensive and require significant infrastructure investment, making them more expensive than dispatchers per task-unit today. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Existing automation (SCADA, remote terminal units) already handles routine switching cheaply, but full AI-driven decision-making replacing dispatcher judgment would require costly integration, redundancy, and certification, keeping near-term costs comparable to or above human labor. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI systems can analyze grid data and recommend actions, but no mature product reliably manipulates physical controls across diverse power distribution hardware in production. Remote operation exists but requires human oversight and handling of edge cases. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | SCADA and automated switching systems exist and perform some control functions, but full autonomous manipulation of distribution equipment by AI without human dispatcher oversight is not deployed at scale due to safety-critical grid stability requirements. |
Prepare switching orders that will isolate work areas without causing power outages, referring to drawings of power systems.
16CI 13–20 · exposure 20 · augmentation 50 · importance 4.7/5 · click for rater detail
Prepare switching orders that will isolate work areas without causing power outages, referring to drawings of power systems.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Energy utilities are digitizing, but switching-order automation remains minimal in production. Risk aversion, regulatory conservatism, and the critical safety nature of the task mean adoption velocity is slow even for well-developed solutions; no sector-wide trend of AI substitution is evident. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are conservative, highly regulated adopters of automation for grid operations; AI tools are being piloted for grid analytics but core switching order generation remains manual and slow to change. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist dispatchers by analyzing drawings, flagging risky switching sequences, simulating outcomes, and suggesting candidate orders—reducing manual planning time. However, the human dispatcher must retain final decision-making authority and accountability, limiting augmentation to workflow support rather than full productivity transformation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help visualize system diagrams, flag potential conflicts, or suggest draft sequences from historical patterns, providing moderate assistance while the dispatcher retains full responsibility for correctness and safety. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can analyze power system drawings and generate candidate switching sequences, the task requires real-time situational awareness, contingency reasoning, and validation against live grid state to avoid outages. Current AI cannot reliably handle the full end-to-end safety-critical logic without substantial human oversight and redesign of workflows. |
| Task automatability | claude-sonnet-5 | 2/5 | Drafting switching orders requires precise, safety-critical understanding of live grid topology and real-time conditions; AI could assist in drafting but cannot reliably handle end-to-end without expert verification, so time savings well below 50% at equal quality/safety today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Power distribution is heavily regulated; switching orders must be prepared and signed by licensed dispatchers, and any automation that affects grid safety is subject to NERC and FERC oversight. Liability for outages is enormous, and regulatory frameworks explicitly require qualified human judgment and accountability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Switching orders are safety-critical and typically require certified/licensed dispatchers and utility sign-off due to outage and safety risk, with strict regulatory and reliability standards (e.g., NERC) mandating qualified human authorization. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for analysis may reduce some planning time, but integration into dispatch workflows, continuous monitoring, and mandatory human verification mean that all-in deployment cost (including validation and liability infrastructure) likely exceeds the labor cost of a dispatcher preparing orders. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Even if AI could draft candidate switching sequences, the cost of verification, liability, and specialized system integration to avoid outages keeps costs comparable to or higher than human dispatcher time for this narrow but critical task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed production system independently generates switching orders for live power grids. Research prototypes and simulation tools exist, but operational grids require licensed dispatch centers with human sign-off; no AI system substitutes for this approval process in practice. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed commercial product autonomously generates certified switching orders for grid isolation; this remains a highly specialized, safety-critical task performed by trained dispatchers with utility-specific procedures. |
Direct personnel engaged in controlling or operating distribution equipment or machinery, such as instructing control room operators to start boilers or generators.
7CI 0–15 · exposure 8 · augmentation 38 · importance 4.7/5 · click for rater detail
Direct personnel engaged in controlling or operating distribution equipment or machinery, such as instructing control room operators to start boilers or generators.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Power utilities are conservative, heavily regulated, and require qualified licensed personnel; adoption of AI-driven dispatch remains negligible despite decades of automation opportunity, reflecting structural and legal barriers. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Utilities are conservative, slow-moving adopters of AI in core grid operations due to safety and regulatory constraints, though monitoring/analytics tools are creeping in. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist dispatchers with data analysis or alarm prioritization, but the core task of directing personnel in real-time control decisions remains human-centric with limited scope for AI augmentation without undermining accountability. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based forecasting, anomaly detection, and decision-support systems can meaningfully assist dispatchers in monitoring and prioritizing actions, even though final directives remain human-issued. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically generate instructions for equipment operation, the task fundamentally requires real-time human judgment, personnel oversight, and accountability in safety-critical settings. Current systems lack the contextual awareness and legal authority to reliably direct human operators in high-consequence environments. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires real-time authoritative direction over safety-critical grid equipment, situational judgment, and accountability that current AI cannot end-to-end replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Power distribution is a heavily regulated sector where dispatchers must be licensed professionals with legal accountability for safety-critical decisions; regulations explicitly require qualified human operators to make and execute control decisions. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Power dispatch is heavily regulated (e.g., NERC certification) requiring licensed personnel to make and be accountable for grid control decisions, a hard legal barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI inference cost for directing operators would be minimal, but the infrastructure required for safe deployment, validation, oversight, and liability coverage would far exceed the cost of employing a dispatcher. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While software cost is low, the high stakes require redundant human oversight and certified operators, so all-in cost is comparable to or higher than current staffing given liability. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI products reliably perform this task end-to-end in production; the task requires legal authority, safety accountability, and real-time human supervision that AI systems do not possess in regulated power distribution operations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously directs control room personnel on live grid operations; decision-support tools exist but do not issue binding operational commands. |
Related occupations — Production
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.