Scaling the Grid & the Art of What’s Possible With AI

Electric utilities must build and operate more generation, transmission, and distribution infrastructure—faster, more reliably, and under greater public scrutiny. Data centers, electrification, advanced manufacturing, and reshoring are driving demand, while utilities also face aging assets, severe weather, workforce constraints, customer expectations, and complex regulation.

The challenge is not simply building more infrastructure. It is building the operational capacity to manage it.

The Capacity Constraint Is Operational, Not Just Physical

Grid expansion often focuses on substations, transformers, conductors, generation, storage, and interconnections. All matter. But physical investment alone does not create a scalable operating model.

Every new asset brings more inspections, work orders, condition data, vegetation risk, customer interactions, compliance evidence, and field decisions. Every storm tests whether the organization can see what is happening, prioritize accurately, dispatch effectively, and close work with confidence. Delayed handoffs, duplicate truck rolls, incomplete work orders, and unclear asset records compound the challenge.

Core platforms such as SAP, Maximo, Oracle, ArcGIS, OMS, ADMS, and mobile workforce systems remain essential systems of record. They carry transactions, controls, and history. But many were built for recordkeeping and stable workflows—not real-time field orchestration.

They do not replace the judgment of a crew leader beside damaged infrastructure, reconcile field reality with system data in real time, or turn completed work into faster learning. Operational AI can.

Operational AI Is Not Another Dashboard

Utilities have reason to be skeptical of AI conversations centered on chat interfaces, pilots, and distant promises. Operations runs on safely completed work, rapidly restored assets, prepared crews, satisfied regulators, and informed customers.

Operational AI applies AI to the work itself: planning, prioritizing, dispatching, executing, documenting, validating, and improving field operations. It works alongside existing data and systems while respecting governance, security requirements, business rules, and operating policies.

The goal is not to replace core platforms. It is to make them more operationally effective. Traditional enterprise software is optimized for structured processes; Operational AI is optimized for urgent, changing conditions where decisions must happen in minutes.

AI Is a Security Concern—Unless It Is an Operational Discipline

Utility leaders are right to be wary of uncontrolled AI. Ungoverned tools can expose sensitive data, produce inaccurate recommendations, obscure decisions, and create operational risk. In critical infrastructure, “move fast and break things” is irresponsible.

But the answer is not to ban AI or leave it to shadow experimentation. It is to deploy AI as an operational discipline.

Operational AI should use only the governed, cross-platform data required for a defined outcome: restoring service faster, prioritizing high-risk assets, reducing unnecessary truck rolls, improving work-package quality, or directing the right crew to the right job. Not every source must connect, every decision must be automated, or every user must see every signal.

A disciplined approach works within existing data fabrics, identity controls, governance policies, approved rules, and systems of record. It supports role-based access, traceability, human review, and auditable workflows.

The Scoreboard Matters

The question is not whether AI is interesting. It is whether it improves operational performance.

Utilities should establish a baseline and track truck rolls per completed work order; first-time completion; outage-to-dispatch, arrival, restoration, and closeout times; mean time to repair; outage duration; SAIDI, SAIFI, and CAIDI where applicable; crew utilization; rework; backlog age; inspection coverage; customer-update accuracy; and O&M cost.

These are not vanity metrics. Fewer truck rolls reduce cost, fleet wear, emissions, and crew exposure. Better work packages reduce repeat visits. Smarter dispatch can shorten outages. Accurate field capture improves asset records and future maintenance and capital decisions.

Reliability Must Be Built Into the Daily Work

Storms, wildfires, flooding, heat, cyber risk, supply constraints, and aging equipment are creating a more volatile environment. Utilities need to prioritize work based on safety, customer impact, critical facilities, asset condition, crew capability, location, materials availability, and changing field conditions.

Operational AI supports that response without sacrificing control. It does not replace policy; it operationalizes policy. Human accountability remains central, especially for high-consequence decisions.

A New Operating Model—Starting Now

Utilities can begin with a high-volume, high-friction workflow: storm damage assessment, pole inspections, vegetation management, corrective maintenance, service restoration, make-ready work, or work-order closeout. Connect relevant governed data, preserve systems of record, define approved rules, establish baseline metrics, measure results, and scale what works.

This is not a strategy for replacing skilled people. It makes crews, dispatchers, planners, supervisors, and asset managers more effective.

The grid will not meet tomorrow’s demand through more software complexity. It will meet it through better operations: measurable, secure, responsible, and scalable.