Artificial intelligence (AI) has quickly moved from the margins to the mainstream in electric utilities. Control room vendors promote AI-based insights, asset platforms promise predictive intelligence, and most major utilities are running at least a pilot or proof of concept. More than 80% of North American utilities already report using AI in some form. Adoption has been widespread, but lasting results have not been achieved. Early pilot projects stall, momentum fades, and return on investment remains difficult to demonstrate within the financial and reliability frameworks to which utilities are held accountable.
COMMENT
In a regulated environment defined by security, reliability, and capital discipline, AI fails when treated as a side project rather than managed with the same rigor as daily operations.
The pilot mentality carries real risk in regulated utility environments. Reliability and capital discipline matter more than speed, and initiatives that are not designed to scale quickly lose credibility. Pilots who remain without a clear path to operational use do more than halt progress; They create skepticism among leaders, regulators and frontline teams. Several failure modes appear repeatedly:
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AI isolated from capital planning and rate cases. When initiatives are funded as discretionary innovation rather than integrated into approved investment plans, they struggle to survive budget cycles and regulatory scrutiny.
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Unclear operational ownership. AI is often part of IT or innovation teams without directly reporting to leaders responsible for reliability and performance, leaving initiatives disconnected from the results by which utilities are measured.
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Activity confused with impact. Progress is measured by models built, data sets explored, or pilots launched, rather than by measurable improvements in SAIDI, SAIFI, or operational and maintenance efficiency.
These patterns directly conflict with the regulatory compact under which public service companies operate. Utilities earn trust and recover investment by demonstrating prudence, discipline and measurable performance. When AI is treated as an experiment rather than an operational capability, it falls outside the frameworks that utilities rely on to justify investment and demonstrate value.
Treating AI as an operational capability means moving away from open experimentation and toward disciplined execution. Sustained operational capability is planned and funded through normal cycles, governed with clear ownership and auditability, and integrated directly into trusted operational workflows. The difference is quickly noticeable in practice. In vegetation management, a pilot could analyze images of a subset of circuits and generate insights that fall outside the work management process. An operational capability prioritizes risk across the system, directly feeds into compensation cycles and crew scheduling, and produces results that can be defended in a rate case. In response to an outage, a pilot can predict restoration times during storms. A sustained capability integrates those predictions into post-event dispatch, communications and reporting, shaping decisions before, during and after an event.
Once AI is operational, it will be easier to defend and manage. Investments fit within existing planning and oversight processes, giving leaders a clear basis for regulatory discussion. AI is no longer outside the system of record; operates within the same structures that utilities use to justify spending and manage performance. Daily behavior also changes. Teams stop arguing about potential value and focus on execution. Performance is monitored, gaps are addressed, and non-performing capabilities are corrected or retired. That pressure exposes weaknesses that pilots often hide. Data quality improves because incorrect data presents itself as an operational risk. Governance becomes stricter because accountability is explicit. Workforce readiness advances because operators, supervisors, and planners are expected to use these tools in real decisions, not as optional add-ons. This approach reduces risk rather than increasing it. Industrialized AI is more predictable, easier to monitor, and easier to intervene when conditions change. Controls are clear, oversight is built-in, and decision-making authority remains aligned with reliability responsibilities. The most important thing is that the criterion remains constant. AI is evaluated by its effect on reliability and affordability. When managed as infrastructure, it strengthens cost and service discipline rather than competing for attention as an independent innovation.
AI programs plateau or scale based on a small set of executive signals that appear early and consistently:
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If AI appears in capital planning. When AI is considered alongside network hardening, systems modernization, and reliability investments, it gains staying power. When you’re left out of those conversations, it’s still discretionary and easy to postpone.
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What leaders ask for in reviews. Executives pushing for outcome-based measures, reliability impact, risk reduction, and cost performance force teams to go beyond experimentation. When updates focus on future activity or potential, accountability is weakened.
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How governance is applied. Utilities that define approval thresholds, human approval points, and intervention authority before implementation move faster during audits, incidents, and storms. When governance is reactive, uncertainty arises at exactly the wrong time.
These signals shape behavior long before formal policies or roadmaps are consolidated. Utilities that scale AI do so because leaders make expectations clear through the decisions they prioritize and the metrics they review.
The value comes not from launching more AI initiatives, but from choosing and committing to a small number of operational decisions where AI can materially change the outcomes. The most effective starting points are near the core of utility performance. High-volume workflows tied to reliability, risk exposure, or operational costs provide natural feedback loops and clear evidence of value. These efforts force early alignment between data, governance, and operations, exposing gaps that matter rather than those that are simply inconvenient. Structured coaching helps leaders make these decisions deliberately. It reduces the risk of pursuing well-intentioned but low-impact use cases and prevents capital from being spread too thin across disconnected efforts.
AI is now at a decision point for electric utilities. The technology is present, pilots are common and expectations are rising. What remains unresolved is the extent to which AI is anchored to the operational responsibilities that utilities already have. Utilities that move forward do so by applying familiar discipline to a new capability. They decide where the AI should work, what results it is expected to influence, and how the results will be reviewed over time. That clarity reduces ambiguity for teams and makes trade-offs easier to manage. It also creates a clear line between efforts that deserve continued investment and those that do not. AI earns its place through measurable impact on reliability, risk and cost. Successful utilities treat AI as part of grid operations, with results that strengthen affordability and public trust over time. —Travis Jones is COO and AI Transformation Leader at Logic20/20, and author of AI Playbook for Utility Leaders: Managing Risk, Powering Reliability.
