By Erik Åsberg, Chief Technology Officer at eSmart Systems. eSmart Systems were shortlisted in the ‘Best SaaS Product for Energy / Utilities / Telecoms‘ category at The 2025 SaaS Awards.

In a world where power infrastructure is both aging and under pressure from rising demand, the energy sector faces an inflection point.

While the industry has made strides in adopting digital tools for inspections and asset tracking, many utilities still rely on fragmented, reactive systems. Enter a new generation of SaaS platforms: not just tools for automation, but intelligence systems designed to transform how infrastructure is managed. At the core of this transformation is grid asset intelligence.

Grid asset intelligence refers to the ability to contextualize, connect, and analyze inspection data over time to inform better decision-making. It shifts the role of software from documentation to insight generation. And for utilities, this shift couldn’t come at a better time.

From compliance to contextual intelligence

For decades, utility inspections were treated as regulatory checkboxes. Data was collected manually, logged in PDFs, and stored in silos. Even modern digital inspections, often captured by drones or helicopters, failed to deliver long-term value because they lacked structure and context.

AI-powered SaaS platforms are closing this gap. Rather than storing snapshots, they create a living, evolving view of grid asset health. By applying time-series tracking, defect recognition, and graph-based asset mapping, these platforms help utilities move beyond one-time assessments toward predictive strategies.

What makes grid asset intelligence different

AI-driven defect detection using imagery from various capture methods (drone, helicopter, ground)

AI models trained on thousands of defect examples can now recognize subtle patterns of deterioration, corrosion, or stress fractures across varied environmental conditions. Integrating drone, helicopter, and ground-based imagery ensures a multi-perspective analysis, improving the accuracy and reliability of defect detection and reducing the need for manual validation.

Time-series databases that track asset conditions over years

By structuring inspection data chronologically, utilities can identify not just the current state of an asset but its historical degradation patterns. This insight enables predictive maintenance, where impending failures can be forecasted based on historical wear trends, seasonal stressors, or past interventions – minimizing outages and extending asset lifespan.

Graph-based mapping to understand structural relationships between components (e.g., poles, insulators, lines)

Graph-based representations allow utilities to visualize and analyze how components interconnect and influence each other. For instance, a fault in a transformer can be contextualized within its relationship to nearby poles or feeder lines, enabling more precise root-cause analysis and targeted remediation strategies.

Geospatial intelligence that correlates environmental factors with asset health

Advanced geospatial tools overlay inspection data with climate, terrain, and vegetation layers to surface environmental stressors affecting infrastructure. For example, proximity to coastal areas may correlate with increased corrosion risk, while wildfire-prone zones may demand more frequent pole inspections. This environmental context sharpens risk assessment and inspection prioritization.

LLM powered querying tools that let utilities ask targeted questions (e.g., “show me high-risk wooden poles within 5 miles of the city”)

Field engineers and planners can retrieve complex, structured insights using plain language queries – reducing reliance on data specialists and accelerating decision-making. These tools bridge the gap between massive data repositories and actionable intelligence.

The value lies in what comes next: smarter capital planning, reduced emergency repairs, and optimized inspection scheduling.

Why the timing matters

This evolution comes at a critical time. According to a report by the American Society of Civil Engineers, much of the U.S. electric grid was built in the 1950s and 1960s, with an expected lifespan of 50 years. Utilities are now managing infrastructure beyond its intended life while simultaneously preparing for climate volatility, regulatory changes, and decarbonization goals.

Meanwhile, experienced field inspectors are retiring, taking decades of institutional knowledge with them. SaaS platforms that embed intelligence into workflows help mitigate this risk by retaining data, patterns, and best practices over time.

Proof in performance

The impact of grid asset intelligence platforms is already being seen across the industry:

• 83% reduction in inspection time – Evergy cut inspections from days to hours using Grid Vision®.
• 52% cost savings – Utilities like ENW and RePower report significantly lower OPEX. (RePower Case Study)
• 60% more defects identified – Xcel Energy saw a 60% increase in defect detection over traditional inspection methods. (Xcel Case Study)
• 28% reduction in CAIDI – WEB Bonaire improved outage management and reliability metrics. (WEB Bonaire Case Study)
• 4x increase in inspection capacity – More infrastructure can be covered without additional field teams.
• Improved safety and team well-being – RePower reduced physical strain on crews and improved job satisfaction while scaling inspections to more assets.

A new role for SaaS in utilities

SaaS in the energy sector is no longer just about digitizing processes. It’s about enabling a new class of strategic decision support. Grid asset intelligence platforms let utilities prioritize based on condition, not just compliance. They help teams understand what matters, when it matters.

As more utilities transition toward predictive asset management, AI-powered SaaS offerings are leading the way. They turn inspections into opportunities for insight, planning, and resilience.