Harmonized data is the key enabler of the real-time integration of autonomous robotics into a utility’s overall systems architecture. But autonomous robots are far from the only justifications for data harmonization: It’s also vital in getting the most out of all kinds of AI. Historically, harmonizing data would have meant unifying physical databases (not possible) or data warehouses (the typical approach). Today, enterprise data analytics platforms, also called data fabric platforms, enable a virtual unified data model on top of existing systems. AI is driving its rapid uptake across many industries, with utilities among them.

3)      Tighten the utility’s insights-to-action loop

A typical asset health and management approach involves feeding operational and asset-status data – some IoT/machine delivered, some human-collected – into an ERP data analytics platform. The platform then does the analytics and sends results back to operational systems. But often, getting those analytics back to operational systems quickly and in actionable ways has proven to be a challenge.

Given the data volumes involved, combinations of AI agents and autonomous robots are putting even more pressure on this feedback loop. Part of the answer is to delegate to AI agents what previously required analytics. But given the diversity of assets a given utility manages and the number of AI agents that soon will be working in concert to assess those assets, utilities are finding a need to reassess their asset-related analytics strategies.

4)      Build trust with the utility’s human intelligence

The value of any tool, be it a wrench or an autonomous drone, depends on the trust it engenders among those who use it. O&M staff – and, more broadly, staff across the organization – must learn to trust AI. Utilities are helping them build that trust in a few ways:

·       Starting AI with recommendations and suggestions (of a maintenance work order or a vegetation-management prioritization) rather than having agents actually execute actions. Percepto, for example, uses autonomous drones but leaves the actual maintenance decisions to humans.

·       Benchmarking against the performance of a utility’s experts. AI agents make decisions and simulate actions, but leave the actionable decisions to human talent. Evidence of AI’s performance on a par with, or better than, experts builds trust.

·       Having the AI explain its recommendations. If an autonomous robot is inspecting a transformer, rather than simply reporting a high risk of failure, the system could describe rising oil temperature, elevated dissolved hydrogen levels in that oil, and a history of previous failures as justifications.

·       Making the AI report not only confidence levels (i.e., vegetation encroachment risk of 80%), but also supporting evidence and alternatives, such as suggesting additional drone inspection.

·       Building in audit trails and traceability with respect to the data and models used, the reasoning paths, and what system(s) made the final decision.

These investments in the workforce’s confidence in AI are as important as the investments in agentic AI and autonomous robotics. Because without trust, staff will work around or override decisions that otherwise would yield greater organizational benefit.

Integrated autonomous robots are the future of utility asset management.

Empowering agentic AI with the real-world capabilities of autonomous robots has enormous potential to improve the performance of a utility’s assets and, by extension, the utility’s overall reliability and cost structure. Customers, shareholders, and utilities themselves stand to benefit. Physical AI pilots abound, and, in vegetation management and beyond, operations are already reaping gains.

Utilities have recognized that closed-loop operational systems feeding off harmonized data are indispensable in exploiting physical AI’s potential. Given agentic AI’s decision-making capabilities, analytics strategies also merit reassessment. And a utility’s people must trust the system. Getting all this right will take effort, but it’s worth doing considering the payoffs across efficiency, uptime, and the ability to meet growing electricity demand.  The cause is noble indeed.