As power utilities increasingly face the dual pressures of aging infrastructure and climate-driven risk, the need to modernize inspections has never been more urgent. Across the industry, drone aerial image capture is supplementing images captured via helicopters, fixed wing aircraft and manual ground inspections, dramatically improving safety and efficiency. But these advancements also introduce a new challenge: a bottleneck in image analysis.
The question is no longer whether utilities should automate their inspection workflows — but when and how they should begin. However, the decision to adopt AI can be daunting and confusing in a market filled with AI-powered solutions. Two key steps guide this journey: (1) assessing your utility’s readiness for AI-enabled inspections, and (2) evaluating AI providers through structured, performance-based trials.
Step 1: Determine Readiness for AI Automation
Adopting AI for inspections should not be a leap of faith. Instead, it should be a data-driven decision grounded in image volume and organizational readiness. If your utility processes more than 50,000 inspection images per year, it’s time to consider automating image analysis. But this threshold can be lowered significantly, down to 25,000 images or fewer, if your organization already demonstrates a high level of AI readiness.
Quantifying Readiness
To refine this crossover point and further define what AI readiness means, here are seven readiness questions across key operational areas to consider. Each affirmative response lowers the total number of inspection images per year required to justify investing in AI.
Here’s a quick look at the readiness factors that matter most:
● Metadata Integration: Are your inspection images tagged with GPS location, timestamp, or asset ID? This foundational data accelerates AI analysis and integration with GIS systems.
● Image Quality: High-resolution images, especially those captured by drones at close range, improve detection accuracy and minimize the need for model tuning.
● Centralized Image Storage: Consolidated cloud-based image repositories improve data access and boost system scalability.
● Experience with AI and Cloud Deployments: Past projects involving AI or cloud-based software lay the groundwork for smoother adoption of AI-enabled inspection tools.
● Engaged and Experienced SMEs: Support from your subject matter experts, especially those nearing retirement, ensures successful training and tuning of AI models using human-in-the-loop feedback.
● Strategic Initiatives: Ongoing efforts in wildfire mitigation, storm resilience, or drone program expansion indicate a clear alignment with AI-enhanced workflows.
● Executive Buy-in: When leadership views inspection modernization as a strategic priority, your AI readiness accelerates.
The takeaway? Don’t just consider image volume in isolation. Combine it with organizational factors to develop a more accurate view of your utility’s readiness to automate inspection workflows.
Step 2: Evaluate AI Vendors with Grounded, Real-World Criteria
Once you’ve established the need for automation, the next step is selecting the right AI provider. The best route is a rigorous evaluation approach: grounded in real performance, not just promises.
Begin with the End in Mind: Build a Detection Roadmap
Before evaluating AI software, clearly define the detection types you need and the use cases they support. Are you focused on detecting broken insulators, damaged crossarms, or bird nests? Are your inspections conducted via drone or helicopter? Is your priority asset management, forestry, or operations?
By creating a detection roadmap, you ensure that your evaluation criteria reflect real operational needs - today and in the future. This also encourages buy-in from multiple stakeholders across your organization and helps avoid vendor lock-in based on short-term use cases.
Understand the "Ground Truth" Behind AI Accuracy
While many AI solutions for utility inspections boast high detection accuracy, those numbers can be deceiving if the models weren’t trained on data that does not reflect your specific infrastructure or environment. Before deciding to adopt an AI platform, it’s worth taking a closer look at the quality of the training data, what’s often called the ground truth.
Ask how many images were used to train the model, and whether those images came from a variety of utilities, especially ones with similar terrain and structures. It’s also important to know how the data was collected. Was it gathered by drone, helicopter, or some other method? Most importantly, consider whether the data truly represents the kinds of assets and environmental conditions your utility deals with every day.
AI models that rely too heavily on synthetic data or were trained on limited geographic samples may struggle to perform accurately when applied to your real-world operations, particularly when faced with regional vegetation types or specific conductor setups.
Conduct Interactive Evaluations: Bake-Offs, Not Blind Trust
The best way to evaluate AI software for utility inspections is to see how it handles real-world workflows from start to finish. Start by looking at how easily the platform can take in your existing data and accurately map it to your GIS system.
This first step sets the tone for how well the system will integrate with your operations. Next, focus on how the AI performs right out of the box. Can it reliably detect the types of defects that matter most to your utility, and how quickly does it deliver results? Equally important is how the model improves over time.
A strong platform should be able to incorporate feedback through human-in-the-loop processes, for accuracy to improve significantly over multiple rounds of testing. Ideally, you’ll see the model go from solid baseline performance, somewhere in the 70 to 90 percent range, to consistently reaching 95 percent or higher.
These evaluations should use a standardized dataset (500–1,000 images is sufficient), a controlled timeframe, and objective performance benchmarks. By comparing vendors on both speed and accuracy, utilities can ensure they’re choosing the most scalable and reliable partner.
The Bottom Line: AI Is Not Just a Tool, It’s a Strategic Investment
AI for inspections is no longer experimental, it’s essential. From wildfire mitigation and storm resilience to reducing operational risk and scaling inspection programs, AI unlocks new possibilities for power utilities. But to realize its full potential, utilities must adopt a disciplined, forward-looking approach that aligns technological capabilities with operational needs.
