AI, BVLOS Drones, and Utilities: A Self-Reinforcing Loop Linking Data, Data Centers, and the Grid

AI, Utilities, and Drones: The Feedback Loop Operations Leaders Need to Manage

Every sector is adopting AI to reduce tedious work and make sense of large datasets. Utilities sit in a unique position: they use AI like everyone else, and they must also keep the data centers that run these systems online.

This creates a loop. More AI requires more compute, which increases electricity demand. That stress on the grid drives more inspections and automation-then even more data-and the cycle intensifies.

Where Inspections Stand Today

For major utilities, inspections are a mix of drone flights, helicopter patrols, and ground imagery. According to Vik Chaudhry, co-founder and CTO of Buzz Solutions, roughly 60% of inspection imagery for many large utilities is already captured by drones.

Scale is the issue. One California utility gathers around 20 million images per year. Manually reviewing that volume is a poor use of highly skilled engineers. As Chaudhry put it, "These are highly specialized field engineers… who shouldn't be looking at images eight hours a day, five days a week. That's just a mundane task, so let the machine handle that."

Why BVLOS Changes the Volume Equation

Beyond Visual Line of Sight (BVLOS) waivers are expanding. Some large utilities, including organizations like the New York Power Authority, are already conducting these flights. Pair BVLOS with drone-in-a-box systems and docks, and inspection frequency can jump significantly.

Expect a 10x increase in imagery volume from these advances. More flights, more assets scanned, more frequent revisits-all great for reliability, but a major lift for data operations. For context on rulemaking, see the FAA's BVLOS ARC report here.

Operational Implications You Should Plan For

• Data pipeline and storage: Standardize ingestion from drones, helicopters, and ground teams. Enforce metadata schemas and retention policies from the start.
• Model strategy: Use computer vision for defect detection with human-in-the-loop review. Target the highest-failure components first.
• Workflow integration: Push AI findings into your EAM/CMMS (e.g., SAP PM, Maximo). Auto-create prioritized work orders with location, severity, and asset ID.
• Exception management: Triage by risk score, asset criticality, and proximity to customers or sensitive terrain.
• Governance and compliance: Align data practices with reliability standards such as NERC CIP. Track model versions, data lineage, and reviewer sign-off. Reference NERC standards here.
• Compute and energy planning: Size GPU/edge capacity for inference at scale. Schedule heavy processing during off-peak hours to reduce strain on your own grid.
• Field readiness: Update SOPs for BVLOS flights, dock maintenance, and failover plans. Pre-plan outage and maintenance windows informed by AI risk flags.
• Vendor management: Evaluate drone docks, BVLOS service providers, and AI platforms based on accuracy, false positive rates, integration time, and SLAs.
• KPIs that matter: Time to insight, true positive rate, reduction in truck rolls, MTBF improvement, inspection cycle time, and avoided incidents.
• Change management: Train engineers to review AI outputs, calibrate thresholds, and give structured feedback that improves models over time.

90-Day Playbook to Reduce Risk and Prove ROI

• Select two high-priority circuits or corridors with diverse terrain and known trouble spots.
• Define a defect taxonomy (by component and severity). Label 5,000-10,000 images to seed and validate models.
• Run AI in shadow mode for one inspection cycle; measure precision/recall and human review time saved.
• Integrate the top three defect classes into your EAM/CMMS for automated work order creation.
• Stand up a lightweight MLOps process: versioning, dataset registry, periodic re-training, and QA gates.
• Quantify avoided events and field-time savings; set thresholds for scaling beyond the pilot.
• Complete a security and compliance review focused on data retention, access control, and audit trails.

The Loop You Must Manage

More AI means more compute. More compute means higher electricity demand. That increases stress on the grid, which drives more inspections and further AI adoption to keep assets healthy. The loop reinforces itself.

Operations leaders need to treat AI inspection workflows, BVLOS flight programs, and data center load forecasting as a single portfolio. Decisions in one area impact the others.

Compliance and Safety Considerations

• Track the Part 108 NPRM and plan for waiver paths until rules are finalized.
• Maintain flight risk assessments, lost-link procedures, and geofencing policies for BVLOS.
• Standardize pilot training and recurrent checks; log all anomalies and corrective actions.

Skills and Team Enablement

Upskill engineers and ops analysts on computer vision QA, prompt-driven analysis, and data workflows. The goal isn't to turn them into data scientists; it's to make them effective reviewers and process owners.

If your team needs structured learning paths, explore role-based AI courses here.

Questions to Ask Your AI and Drone Vendors

• What's the model's precision/recall on my specific asset classes and terrain? Show validation, not just a demo.
• How does the system reduce false positives without missing critical defects?
• How fast from image capture to prioritized work order?
• What are the data retention, encryption, and audit capabilities? How is reviewer feedback captured and used?
• What happens when docks fail, links drop, or weather interrupts flights? Show the fallback plan.
• What is the end-to-end cost per inspected mile and per actionable defect?

Bottom Line

BVLOS operations and automated collection will multiply your data volume. As Vik Chaudhry notes, a 10x increase is realistic. Teams that industrialize their data pipelines, model governance, and work-order automation now will stay ahead as the loop tightens.

Act now: pick your pilot corridors, define the taxonomy, stand up human-in-the-loop review, and connect findings directly to maintenance workflows. That's how you turn AI imagery into fewer outages and faster field response.