For years, the holy grail of utility asset management has been to maintain or replace equipment before it breaks. A failure can mean an outage, missed performance targets and a crew dispatched at short notice. Predicting problems allows teams to schedule work rather than respond on the back foot.

AI is bringing that ambition closer. There is also a wider opportunity in connecting asset information with grid data. Two-time horizons matter: the short-, medium- and long-term management of equipment and the real-time decisions needed to keep electricity flowing reliably.

Seeing assets in the context of the grid

Predicting when an asset might fail depends on more than its age. Historical performance, geography, operating conditions and usage patterns all help determine how equipment behaves. AI can analyze those signals and give utilities a better basis for deciding what needs attention and when.

But assets today extend beyond poles and wires. Sensors across the network send operational data to substations and smart grid control centers. Those signals reveal changing conditions that affect the wider system, allowing AI to connect information about individual assets with what is happening across the network.

Utilities can consider an asset’s condition alongside demands on the grid, judge the significance of a problem and prioritize the response. That context is more useful than treating every alert separately.

Helping operators respond to a changing grid

Real-time grid operations present a different challenge. Supply and demand must remain balanced, while voltage and frequency need to stay within acceptable limits. Conditions can change extremely quickly, sometimes requiring responses in fractions of a second.

AI can analyze incoming signals and give control room teams a clearer picture of emerging imbalances. The next step is to make those insights actionable: what has changed and what should the operator consider? The Electric Power Research Institute’s 2026 transmission operations research priorities reflect this direction, including improving operator decision-making through AI, strengthening real-time situational awareness and improving voltage control tools

Agentic AI could eventually coordinate selected responses to changing network conditions. I would be cautious, though, about treating that as permission to give an autonomous system direct control over electricity flows. The technology and its safeguards need to mature before utilities entrust it with decisions where an error could destabilize the grid.

Connecting detection to field action

There is a more immediate application for Agentic AI in the work that follows an operational insight. Consider an asset whose condition data suggests that maintenance is needed. A traditional analytics system might raise an alert or present a recommendation on a dashboard. An agentic workflow could take that process further.

Within approved parameters, an agent could create a maintenance work order, schedule the job and arrange for a crew to be dispatched. It could then follow the work through completion and update the work management and customer information systems. The result is a connected sequence of activities, with fewer manual handoffs between identifying a problem and resolving it.

Such workflows can be defined, monitored and governed. Utilities can decide where an agent may act and when a person must approve the next step. That gives teams a practical route to greater automation without making the same assumptions about risk as they would in real-time grid control.

Building the data and communications foundation

None of this works without timely, trustworthy information from the field. Smart grid control centers need data from meters, sensors and other devices and that puts demands on the networks carrying it.

As a result, advanced metering infrastructure (AMI) 2.0 and telecommunications modernization need to progress together. Utilities are investing in private LTE and 5G networks to support higher-speed, lower-latency communications. Older meters and networks may lack the responsiveness more demanding AI applications need. Upgrading meters without connectivity would limit the benefits.

A 2026 US Department of Energy report provides a useful illustration. Y-W Electric plans to install voltage and current sensors on 70 capacitor banks to improve real-time visibility into grid conditions and enable intelligent local control during communications outages. The project demonstrates how investments in field monitoring can support more responsive grid management.

Edge analytics is important as well. It is not always practical to send every reading back to a central control room before processing it. Some analysis can happen near the equipment, so systems can identify relevant conditions locally and pass on the information operators need. That becomes increasingly valuable as the volume of field data grows.

Deciding where autonomy is safe

Utilities cannot assess autonomy in the same way as a typical business application. This is not a shopping cart application where an error prevents someone from completing a transaction. In grid operations, a wrong intervention could cut power to 10,000 homes. That is a very different level of responsibility.

Security standards and strong operational guardrails must be built in from the start. A maintenance workflow, an operator recommendation and a direct intervention in grid control should not all be governed by the same rules. Each needs to be assessed against the consequences of failure and the level of human involvement required.

The International Energy Agency's 2026 report, Modernising Grids in the Age of Electricity, reinforces this distinction. It highlights how AI adoption is more advanced in maintenance and planning, while real-time grid control demands greater explainability, auditability and accountability before autonomous capabilities can be deployed safely.

I expect the industry to define these boundaries more clearly as Agentic AI develops. Some activities will remain human-in-the-loop. Others, once tested and shown to be dependable, could progress toward supervised or more autonomous execution. Autonomy must be earned, use case by use case.

The biggest advances

We can already see faster AI adoption in utility customer service and back-office processes such as billing, collections and contact centers. Those functions are important, but they do not directly control the flow of electricity. Core grid operations demand a more careful progression.

Over the next four or five years, I expect AI to take on a larger role in condition monitoring and the workflows that support preventive maintenance. I also expect significant progress in the insights available to grid operators. Knowing the status of the network is useful; understanding what has changed and what action makes sense is much more valuable.

As confidence in these systems grows, utilities will be able to extend automation into selected operational activities. Progress will come from joining asset intelligence with the real-time picture of the grid, allowing better-informed, coordinated responses. For critical infrastructure, that combination of speed, reliability and control is what will determine how far Agentic AI can go.