Energy Companies Are Improving Asset Management with Drones and Visual AI
/Utility asset management used to be a game of intervals and intuition: Inspect infrastructure every few years, respond when something fails, dispatch crews after storms, repeat.
But that model is becoming less effective as energy infrastructure is under growing strain from rising demand, aging equipment, and severe weather. At the same time, utilities now have access to far more inspection data than ever before thanks to drones, imaging technology, sensors, and other technologies.
Those advances make collecting data easy. But making sense of the vast volumes of data collected—and doing so fast enough to act—is the challenge vortex many maturing drone programs sit in currently.
From ESRI
Visual AI is beginning to step in by speeding up data analysis and therefore reshaping energy asset management. By combining computer vision, image analytics, thermal analysis, and geospatial context, energy companies are turning massive volumes of visual inspection data into prioritized maintenance actions, reliability improvements, and faster operational decisions.
Why Visual AI Is Gaining Momentum in Energy
The utility industry has reached an inflection point.
Kaitlyn Albertoli, CEO and co-founder of Buzz Solutions, explained the challenge energy companies have faced in a recent company podcast, “With the introduction of drones and autonomous robotics, they were starting to collect a much larger amount of data,” she said. “Some utilities were saying it was going to take six months to a year, or maybe even two years, to sift through all of those images. So that was kind of the ‘there must be a better way’ moment.”
The lag between collection and analysis creates a serious operational problem. A cracked insulator identified two years after image capture is not actionable intelligence.
Visual AI is helping close this gap by automating first-pass analysis of inspection imagery, flagging anomalies, and helping utilities prioritize human review.
Automating Operational Intelligence
Many energy drone programs have AI tools that can analyze photos and flag anomalies. That’s a great start, but the more sophisticated visual AI systems are starting to connect image analysis to broader operational workflows.
An AI model that detects corrosion is useful, sure. But a platform that detects corrosion, maps it to a specific asset, compares it against prior inspections, prioritizes risk, and routes that issue into operational workflows is far more valuable.
That is where the technology appears to be heading.
Case Studies: Visual AI in Energy
Energy operators, with the help of industry partners, are working to make sure the extensive amounts of data collected by drones can be used in decision making (without months or years of delay). Here are two such examples.
Dominion Energy: Scaling Visual AI for Distribution Reliability
Dominion Energy’s Image Management and Analytics Program (iMAP) demonstrates how visual AI can support large-scale asset management.
Dominion manages 6,800 miles of transmission lines, 47,000 supporting structures, and 58,510 miles of distribution lines. That scale makes traditional inspection analysis increasingly difficult, if not blatantly impossible.
To modernize its approach, Dominion combined several technologies to make the most of its drone-based image capture and thermal inspections. A centralized image repository with GIS integration stores images and maps them to their corresponding assets. And then visual AI (Dominion uses the Buzz Solutions PowerAI) analyzes that data, which improves and speeds up decision making.
The results suggest meaningful operational gains. According to an Esri case study:
PowerAI analyzed 37,406 thermal images across 2,911 poles and identified every thermal anomaly Dominion had previously found through manual analysis
PowerAI analyzed 41,095 RGB images and correctly identified 120 damaged crossarms requiring closer review or replacement
Image analysis time dropped from months to hours
The analysis helped Dominion Energy prioritize work to improve interruption frequency and duration metrics (SAIDI and SAIFI)
This kind of reduction in inspection turnaround time changes asset management economics. Instead of reviewing images months after capture, teams can act while conditions are still current. And instead of broadly scheduling maintenance, utilities can prioritize interventions based on identified real-time risks.
Ameren: Centralized AI-Powered Inspection Operations
Ameren’s drone and inspection program evolved from a small pilot effort into a centralized operation designed to support resilience, storm response, and proactive asset monitoring.
One major challenge Ameren addressed was fragmentation. Rather than allowing inspection workflows to remain siloed across regions or departments, the utility centralized this infrastructure. Part of this process was integrating drone imagery into its GIS environment to enable geospatially anchored inspection intelligence rather than disconnected image archives.
These adjustments created a strong foundation for scaling visual AI, the impact of which has become especially clear during storm response. Historically, post-event assessments required slow manual inspections and/or costly helicopter deployments. But now, with drones, field teams can rapidly assess affected infrastructure.
And where human reviewers used to have to manually analyze every image from helicopter and drone inspections, AI models can now automatically flag defects, identify anomalies, and surface priority maintenance concerns much faster. This all comes together to mean that after storms, Ameren can quickly dispatch repair crews based on actual damage conditions rather than incomplete field reports.
What Visual AI Means for Energy Asset Management
Visual AI is helping energy companies rethink asset management. Instead of periodic inspections followed by delayed analysis and reactive maintenance, utilities are building systems that support:
Faster defect identification
More scalable inspection programs
Better storm response
Condition-based maintenance
Stronger grid resilience
The strongest examples make sure visual AI is combined with drone imaging, GIS integration, and clear operational objectives. These integrations are what take simple image analysis and turn it into asset intelligence.
