“AI in utility inspections” is everywhere in the headlines—but few are explaining how to actually implement it. AI isn’t a magic button you plug in and hit “go.” Successful implementation starts by identifying real workflow pain points—where manual processes are slow, repetitive, or prone to error. From there, AI can be applied in targeted ways to reduce workload, improve consistency, and enhance decision-making. In some cases, hiring an AI consultant may help, but only if they understand your industry and data. The best results come when AI is introduced with clear objectives and structured, decision-ready data—not hype.

AI Isn’t the End Goal. It’s a Toolset.

Saying ‘we need to use AI’ is like saying ‘we need to use electricity.’ The question is: what problem are we solving?

Utilities shouldn’t chase artificial intelligence as some futuristic destination. Instead, AI should be treated like any other tool: applied where it clearly solves a problem. That means identifying bottlenecks, inefficiencies, or areas where human effort is stretched thin—and asking if AI can realistically close the gap.

If your team is sitting on terabytes of inspection data that no one has time to analyze, AI can help. If you’re manually triaging thousands of image annotations, deciding what’s critical and what can wait, AI can help. If you’re generating reports manually, copying screenshots into PowerPoint because there’s no structured data to work with—AI can absolutely help.

But if your processes aren’t clearly defined, or your data is inconsistent and siloed, no AI system in the world will magically fix that. It’ll just accelerate the confusion.

Ask yourself:

• Are inspectors drowning in unreviewed imagery?
• Are we struggling to triage fault severity or track long-term risk?
• Are our board reports built on screenshots, not structured insights?
• Is our inspection data helping us anticipate what’s ahead—next year, five years, ten years?

Depending on how you answer those questions, there’s your starting point.(s) Do we need to execute an enterprise AI initiative? Maybe, maybe not. But by taking a hard look at our specific use cases where AI can reduce workload, improve consistency, or deliver better answers—faster. That’s the first step to making AI in utility inspections work in the real world.

Where to Start Using AI—Today.

Getting started with AI in utility inspections doesn’t necessarily require a million-dollar rollout or a enterprise initiative. It starts by relieving pressure where your team feels it most. Here’s how utilities can begin integrating AI in focused, meaningful ways—right now:

🔹 1. Fault Detection & Pre-Tagging
Use AI to scan inspection imagery and flag potential faults—think of it as a first pass, not a final verdict. It doesn’t replace human judgment; it makes it faster.
→ Solves: Review backlogs, inconsistent triage
→ Requires: A structured image repository (like Scopito), and preferably, some historical examples for tuning

🔹 2. Pattern Recognition for Risk Clusters
With 1–2 years of structured inspection data, AI can detect fault patterns—hotspots tied to specific asset types, locations, or seasonal factors.
→ Solves: Better forecasting, proactive planning
→ Requires: Time-series inspection data, component-level tagging, GPS/metadata

🔹 3. AI-Assisted Reporting
AI can pull from structured inspection data to pre-fill compliance or executive reports—replacing the hours spent pasting screenshots into PowerPoint.
→ Solves: Time drain, human error, inconsistent reporting
→ Requires: Consistent annotations, severity scoring, maintenance/follow-up records

🔹 4. Workflow Intelligence
AI can trace how long it takes to resolve issues once they’re found, flagging where maintenance is delayed—or where follow-up breaks down.
→ Solves: Invisible bottlenecks, slow repair cycles
→ Requires: Timestamped follow-up data, resolution logs, integration between inspection and work order systems

Each of these is a realistic entry point. Not theory—practice. AI that works with what you already have, solving problems your team already knows.

Before We Decide to Hire an AI Consultant—Getting Aligned Internally

The reality is, most utilities won’t have in-house AI specialists—and hiring a consultant may potentially make sense. But before taking that step, it’s critical to do the internal work first. Otherwise, you risk paying premium rates to solve a problem you haven’t fully defined.

Start by asking:

• What data do we have?
  Is it inspection imagery, structured logs, GPS data, time-stamped annotations? How clean and accessible is it?
• Where is it stored—and who owns it?
  Are different departments managing separate systems that don’t talk to each other?
• What decisions are we trying to support with this data?
  Think funding justification, risk scoring, asset replacement planning—not just compliance snapshots.
• Where are we losing time or making avoidable mistakes?
  Are inspectors buried in imagery? Are reports being built manually? Are faults slipping through cracks in the process?

These aren’t just IT tech problems—it could be a communication problem between field teams, data managers, and decision-makers.

Getting aligned on these questions creates the foundation for any AI implementation—whether it’s internal, outsourced, or hybrid. Because a great AI consultant can only help if you’ve already clarified what you need AI to do.

When Should We Hire an AI Consultant or Developer?

Perhaps your team doesn’t yet have the technical confidence to implement AI—or you’re hitting roadblocks trying to scale it. There’s no shame in bringing in outside expertise. In fact, it can save time, reduce headaches, and even lower long-term costs.

Just remember: the right approach matters just as much as the right technology.

✅ They start by understanding your operations—not selling tech.
Before talking models or automation, a good consultant should ask about your inspection workflows, pain points, reporting requirements, and goals. If they’re not listening first, they’re not solving the right problem.

✅ They understand infrastructure and inspection data.
Utilities aren’t tech startups. AI for infrastructure inspections requires domain fluency—assets, maintenance cycles, fault types, risk metrics, compliance timelines. If they don’t speak your language, they won’t build tools that work in your world.

✅ They aim for clarity, not complexity.
You’re not hiring them to reinvent your company—you’re hiring them to help you process images faster, reduce annotation workload, generate cleaner reports, or flag faults more consistently. Keep the scope specific and impact-driven.

✅ They tell you what AI can’t do.
No consultant should promise that AI will replace your team, perfectly predict failures, or clean up messy data without effort. The best partners are honest: AI needs structure, oversight, and purpose.

✅ They work with your team, not around them.
AI isn’t something you outsource and forget. It needs ongoing tuning, training, and adaptation. Make sure the consultant is building your team’s capability—not dependency.

And finally:

✅ You still own the roadmap.
Even with outside help, strategy should come from inside. Consultants can build the engine, but you steer the ship. Stay close to the process, define the outcomes you care about, and make sure the AI you implement reflects your real-world needs—not someone else’s playbook.

Scopito’s Role

If you want to explore automation, you need a platform that structures your data first.
That’s Scopito’s lane.

We’re not here to hype AI—we’re here to help you get ready for it.

From consistent tagging and time-series views to fault prioritization and reporting, Scopito provides the foundation for using AI in utility inspections meaningfully.

And because Scopito is built to integrate with external tools, your preferred AI models can easily plug in—for automated analysis, annotation, and deeper insights.

Because when your inspection data is clean, contextual, and aligned with stakeholder needs…
AI doesn’t just work better.
It works for you.

Ready to Make AI Work for You?

You don’t need hype. You need clarity, structure, and a partner that understands the realities of utility inspections.
Scopito helps you get your data—and your team—AI-ready.

📩 Contact us today to discuss how Scopito supports AI in utility inspections, without the noise.

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About the Author: Gayle Godkin is Scopito’s Marketing Representative, specializing in the commercial drone inspection space and helping communicate the value of visual inspection data to infrastructure industries.

For real-world applications of drone, AI, and robotics in energy operations, see the Energy Drone & Robotics News or explore the latest trends in the “Bots, Brews & Energy Ops” feature from the Energy Drone & Robotics Coalition.

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