The term “resiliency” has become shorthand for disaster recovery in the energy world. Utilities are judged by how quickly they can restore power after a storm, wildfire or blackout. Grid modernization, response time and weatherization dominate the conversation, and for good reason. Climate-related disasters have doubled in the past 20 years, and surging demand from that, electric vehicles and AI-powered data centers are placing unprecedented stress on infrastructure that, in many regions, is over 50 years old. These facilities, often clustered near cheap land or water sources, consume vast amounts of energy and can overwhelm nearby transmission and substation capacity if not planned for in advance

But resilience isn’t just about bouncing back. It’s about avoiding failure in the first place. While utilities invest heavily in response strategies, many remain behind when it comes to preventive diagnostics: the quiet, behind-the-scenes effort of monitoring asset health, identifying early warning signs and acting before failures occur. As electricity demand accelerates and equipment procurement becomes more costly and complex, with transformer lead times reaching 12 to 30 months in 2023, a wait-and-see approach is no longer viable.

Resiliency starts long before the lights go out. It begins with visibility — knowing which transformer is nearing the end of its life, understanding how aging equipment is responding to load demands and using predictive diagnostics and historical data to prioritize action over routine maintenance.

The blind spots in grid resiliency

Despite growing investments in grid modernization, one critical element is often overlooked: how utilities assess and respond to asset health before a failure occurs. This omission can introduce avoidable and compounding risks.

Many of today’s systems are operating well beyond their original design parameters. Transformers that once handled predictable base loads are now subject to frequent peaks, higher temperatures and prolonged stress from factors like AI-driven data center demand and distributed generation. These conditions accelerate wear and reduce expected lifespan, but the signs of degradation can be subtle and cumulative.

Annual testing isn’t enough. A single snapshot of dissolved gas or insulation condition may miss the early development of faults. Effective risk detection requires pattern recognition, pulling from dissolved gas analysis (DGA), thermal and electrical profiles and operational history to identify changes in behavior and emerging anomalies. Without this kind of contextual visibility into asset health, utilities risk misjudging priorities like replacing healthy equipment or overlooking units on the verge of failure. Both outcomes lead to higher costs, greater outage risk and missed opportunities to optimize aging fleets.

The problem often lies in reactive or time-based maintenance, where issues are addressed only after alarms trigger. Even when data is available, it’s frequently fragmented — isolated across systems or lacking the context needed for confident decision-making.

In addition to operational risks, there’s growing reputational pressure. Communities affected by repeated outages, particularly in regions prone to wildfires or extreme weather, are demanding greater accountability from their utilities. Regulators are increasingly focused on outage frequency, SAIDI/SAIFI scores and infrastructure transparency. In this environment, relying solely on reactive strategies risks trust, credibility, and in some cases, regulatory penalties, beyond just equipment failure.

Preventive diagnostics can no longer be treated as a secondary function. They must be central to how utilities forecast risk and plan for long-term performance. Knowing where your vulnerabilities are is the first step in preventing failure, and that starts with how you use your data.

From raw data to action: The role of predictive diagnostics

For many, the challenge isn’t collecting test results from data. It’s connecting them, interpreting them and translating them into action before failure strikes.

Every day, asset health data is generated from field tests, condition monitoring devices, maintenance logs and operational systems. But when this information is siloed across departments, stored in disconnected platforms, or missing key historical context, it’s nearly impossible to make confident decisions.

That’s where predictive diagnostics can drive transformation. Unlike traditional assessments based on fixed maintenance cycles, predictive diagnostics combines historical data, real-time monitoring and expert insight to model asset risk dynamically — helping operators anticipate failures before they happen.

The goal is not to flood teams with more alerts or dashboards. It’s to surface meaningful patterns and trends: changes in gas composition, correlations between operating temperatures and test results, emerging stress patterns across transformer fleets, the types of signals that help utilities act before risk turns into reality, rather than simply monitoring.

For example, a rise in acetylene in a dissolved gas analysis (DGA) could be benign or could signal a serious arcing fault; context determines interpretation. If the unit has shown similar signs in the past, if load conditions were atypical, or if thermal indicators are stable, the urgency changes. Predictive diagnostics helps connect these dots faster and more accurately.

The most effective utilities are moving beyond point-in-time assessments and investing in condition-based strategies that let them assess risk dynamically. They’re layering diagnostic data with asset history and operational context to forecast performance more accurately. In many cases, they’re applying machine learning to enhance these forecasts, using models trained on large volumes of field data to identify which assets are most likely to fail and when.

Combining human expertise and intelligent systems

But automation alone is not the solution. For example, a transformer might show early signs of overheating according to a predictive model, but without context, it’s unclear whether this is a sign of failure or just a temporary anomaly. The most effective utilities are those that pair data science with domain expertise. Predictive models can flag unusual behavior, but understanding whether that behavior represents true risk often requires human judgment, especially when data is incomplete or borderline.

This approach doesn’t just avoid failures. It builds confidence in decision-making, in resource prioritization and in long-term planning. When equipment is aging and replacement timelines are measured in years, knowing exactly where to intervene can make the difference between resilience and disruption.

The pressure is mounting, fast

It is no longer a question of whether the grid is under strain. The question is how much longer aging infrastructure can carry the load without a more proactive strategy in place.

Generative AI is fueling a rapid and permanent shift in demand. A single data center project can require up to 100 megawatts of power, the equivalent of the electrical usage of 100,000 homes or hundreds of thousands of electric vehicles. And we’re just at the beginning. By 2030, data centers could account for up to 15% of total U.S. electricity consumption, up from 8% today.

Meanwhile, EV adoption continues to rise, and the re-shoring of American manufacturing is creating pockets of new industrial demand. These trends are converging in areas that often lack the grid infrastructure to support them, driving the need for better planning, earlier investment, and smarter use of what already exists.

At the same time, utilities face long permitting timelines, regulatory hurdles, supply chain challenges and persistent workforce shortages. Many critical components, like large power transformers, now carry multi-year lead times. The margin for error is shrinking.

Preventive diagnostics as a strategic imperative

To navigate these pressures, utilities need more than modernization funding or new technologies. They need to adopt a preventive mindset.

This means shifting from fixed maintenance cycles to condition-based strategies that reflect asset reality, not just time on the calendar. Operators need a dynamic view of where vulnerabilities exist today, instead of relying on static timelines or past load assumptions. It means moving beyond gut-feel replacement planning to risk-informed prioritization. And it means making diagnostics not just an operational task, but a strategic pillar.

At its core, predictive diagnostics is a way to future-proof investment. By combining historical insights with real-time data, utilities can know what’s at risk and when, meaning they can allocate resources with precision, not just guessing which asset might fail, but understanding why, when and how to mitigate it. This kind of foresight helps utilities stay ahead of outages, reduce costly emergency repairs and make the most of limited capital resources.

Building a preventive culture: Four steps to take now

The most resilient utilities are those that treat asset diagnostics as a core capability, not a support function. Here’s how to make that shift actionable:

1. Break down data silos

Diagnostic insights are only as valuable as the visibility they provide. Too often, critical data lives in silos that are split between departments or buried in disconnected systems. Centralizing asset health data and enabling collaboration across engineering, operations and planning teams enables faster decisions and a shared understanding of system risks. Tools alone won’t fix this — organizational collaboration is key.

2. Shift from fixed schedule maintenance to condition-based maintenance

Instead of relying on time-based intervals, utilities should use real-time asset insights to guide maintenance and replacement decisions. Condition-based strategies use diagnostic trends, such as gas levels, load data and thermal performance, to identify which assets need attention and when. This approach reduces unnecessary maintenance, targets the highest-risk equipment and helps prevent failures before they escalate.

Condition-based maintenance also helps stretch limited budgets by targeting capital expenditures to the most at-risk or high-impact components, a critical advantage in today’s resource-constrained environment.

3. Balance Predictive Tools with Expert Oversight

AI and analytics can highlight risk patterns and flag emerging issues, but they shouldn’t operate in a vacuum. Pairing predictive diagnostics with human expertise ensures decisions are grounded in operational context and system-wide priorities.

For example, a machine learning model may flag anomalies in gas levels or temperature fluctuations, but only a field engineer with historical fleet knowledge can interpret whether the change reflects a true fault or operational variance. Pairing both perspectives ensures action is calibrated, not reactionary.

4. Make Resilience a Leadership Responsibility

Preventive planning must start at the top. Utility executives should treat diagnostics programs as essential to business continuity, ensuring they are properly funded, holding teams accountable and reinforcing the value of early action across the organization.

Yet, despite the growing awareness of grid strain, diagnostics programs are still often siloed. It’s time for executive teams to treat preventive maintenance not as an expense, but as a long-term investment in reliability, resilience and reputation.

Planning for a better grid starts now

Grid failure is not inevitable. With the right visibility, the right tools and a shift in mindset, utilities can prevent small issues from becoming major disruptions, starting with how risk is measured, understood and acted on.

Predictive diagnostics and asset health data should be utilized as decision-making tools. And the utilities that embed them into planning, operations and leadership strategy will be the ones best equipped to deliver consistent, resilient power in the years ahead.

The path forward isn’t about replacing everything. It’s about knowing what matters most and acting before it fails.