Resiliency has always been the core promise of a utility. When the grid is disrupted, contain the impact and restore power quickly. What is changing is the nature and consequences of those disruptions.
Much of the infrastructure is aging, while demand is expected to grow. The American Society of Civil Engineers graded the U.S. grid a D+ in 2025, and much of it is operating near or beyond its designed service life just as extreme weather and cyber threats test it in new ways. Summer peak demand is projected to grow by more than 224 GW over the next decade, leaving utilities with less room to absorb disruptions when they occur.
Resiliency under these conditions is about more than redundancy. It is about having multiple ways to respond when conditions change, whether that means rerouting power, drawing on storage, shifting load, dispatching distributed resources, or changing field priorities. The technology to create those options is becoming easier to acquire. The question is how utilities can exercise these options when needed.
The technology is no longer the constraint
A decade ago, deploying an AI use case required specialized teams, a clean labeled dataset, and long development cycles. In contrast, today a working pilot can be stood up in days. For CIOs and Chief Data Officers asked to turn resiliency mandates into capability, the constraint has shifted from whether the technology can work to whether the organization can actually operationalize it.
Interestingly, most mature AI applications by utilities are often tied directly to grid reliability, such as predictive maintenance, proactive outage communications, identifying and localizing outage causes, improving ETOR estimates, prioritizing field work, detecting vegetation or asset risks. Utilities have made progress with applications like these. Yet scaling these into production and across the enterprise remain challenging for reasons that are rarely technical.
Why pilots stall short of production
Pilots often succeed by working around the enterprise. Someone extracts the data, cleans it, and hands the team a CSV. That may be enough to prove the use case. However, production requires something different. It needs consistent, secure access to trusted operational data and the context needed to act on it.
Data access is only part of the story. A governance lens raises a broader set of questions:
- Who is accountable when an AI system gets it wrong?
- Which decisions require human review?
- What evidence is needed to validate the model and satisfy cybersecurity, safety, and regulatory requirements?
When those answers are defined early, governance clears the path to production. When they are not, teams face uncertainty, delays, and rework in order to move to production.
Governance extends beyond AI and internal processes. Resiliency depends not only on having options, but on having the authority to exercise them. A battery, flexible load, or automated control may create another way to respond when the grid is under stress. But contracts, operating procedures, or unclear decision rights can make that flexibility unusable.
Utilities are risk-driven organizations by design. They excel at stopping work that has not been shown to be safe, reliable, or compliant. What is often less clear is when enough risk has been addressed to proceed. Without a defined basis for saying yes, pilots tend to stall before production or struggle to scale.
Governance that fits the work already underway
When governance sits outside the normal workflow, it becomes a checkpoint. When built into the workflow, it helps teams navigate risk as part of the work.
For instance, AI-specific questions can be added to vendor procurement, risk and impact assessments incorporated into existing IT intake, and the level of review matched to the risk. Low-risk use cases may clear a short questionnaire, while higher-risk ones follow a more structured review grounded in NIST’s AI Risk Management Framework. That reduces the chance of governance becoming a late-stage hurdle.
It can also speed things up by addressing risk and compliance as the work progresses rather than after the fact.
“When we embed governance into existing processes, it speeds things up. It makes it consumable, and it doesn’t feel like more work because it’s already part of what they’re used to doing”
– Lauren Malik, Pariveda
What a solid foundation makes possible
The payoff shows up in what becomes possible afterward. A major electric delivery utility in high-growth Texas was running a transmission construction budget that doubled roughly every two years on a platform more than 25 years old. Rebuilding it around how work actually needed to flow through planning, scheduling, estimating, and execution produced a system that now serves more than 2,500 users, supports over 50,000 construction projects, and has enabled the utility to scale transmission construction activities by 250%.
What made that scale possible was not the technology alone. It was the foundation around it in the form of data people could rely on, workflows that connected, and a system the organization trusted enough to use. The same holds for AI. The International Energy Agency has cautioned that without policies to support AI applications, energy-sector use is likely to stay limited to small-scale pilots.
Utilities that embed governance into the work from the start gain more than a faster approval cycle. They build the organizational capacity to say yes as the technology evolves. They turn technological optionality into operational resiliency, which means not just having more choices, but being able to act on them when the grid is under stress.
Go deeper: AI, Modernization, and the Governance Gap
Our white paper explains how utilities turn governance from a checkpoint into the path to production. It covers the pressures driving modernization, the AI use cases already delivering results, and what production-grade AI really requires.
