MIT CSAIL Professor Frédo Durand has joined the RIS Advisory Board, bringing world-leading expertise in computer vision and visual computing. His experience will help guide the continued development of UTELinspect as RIS turns complex utility inspection imagery into structured, actionable operational intelligence.
What happens to inspection data after the inspection is complete? On September 25, RIS President Stuart Rudolph will be at Penn State Harrisburg for the Smart Grid Deployment Consortium’s Street Solutions for a Smarter Energy Future symposium.
BEAD projects end, but distribution assets remain for decades. We explore how RIS helps electric utilities turn BEAD project information into permanent operational records that support future inspections, maintenance and infrastructure operations.
BEAD is generating far more than new broadband infrastructure. Every pole surveyed, photographed and assessed creates valuable information about America’s electric distribution network. This article explores how utilities can turn that information into lasting operational intelligence, that supports future inspections, maintenance, resilience and AI long after BEAD construction is complete.
Routine inspections create far more than photographs—they capture valuable operational knowledge. In the second article of the Future-Ready Utilities series, we explore why context is just as important as the image itself, how connected inspection histories reveal the behaviour of assets over time, and why preserving that context improves operations today while laying the foundation for tomorrow’s AI. Learn how small improvements in structuring inspection data can transform isolated observations into lasting knowledge that supports better decisions for years to come.
Join RIS at NWPPA E&O 2026 in Portland (Booth 548) to see how utilities can turn inspection data into actionable insight with applied AI.
AI is only as effective as the data that teaches it — yet most utilities are already collecting valuable inspection data without realizing how much future insight they’re leaving on the table. In the first article of the Future-Ready Utilities series, we explore why labeled inspection data is the true foundation of applied AI, how industries from aviation to wind energy learned this lesson the hard way, and why utilities don’t need new technology to start preparing today. This article explains, in plain language, how simple data discipline can turn routine inspections into lasting operational knowledge — long before AI ever enters the workflow.
MIT CSAIL Professor Frédo Durand has joined the RIS Advisory Board, bringing world-leading expertise in computer vision and visual computing. His experience will help guide the continued development of UTELinspect as RIS turns complex utility inspection imagery into structured, actionable operational intelligence.
We’re proud to welcome Michael F. Watson, former co-op utility CEO, as Strategic Advisor to Remote Intelligence Solutions. With decades of hands-on experience managing 6,000 miles of line and over 100,000 poles, Michael knows what it takes to run reliable operations — and where AI can make a difference.
This week, we’re excited to share that Remote Intelligence Solutions (RIS) has been featured in a spotlight article by MIT’s CSAIL Alliances. The piece explores how we’re using applied AI to help utilities inspect and manage critical infrastructure more efficiently and safely. But this isn’t just a media moment — it’s a reflection of the relationships, research, and real-world relevance behind our mission.
The energy sector is challenged daily in its mission to deliver crucial services reliably, a task that becomes even more critical during emergencies. Innovations such as unmanned aircraft have transformed the efficiency of both routine and emergency inspections across vast distances and in the face of hazardous conditions. Traditionally, inspection processes have been bogged down by cumbersome steps, including uploading data to unstructured storage solutions like Dropbox, then laboriously converting this data into a structured format for further analysis and integration into geospatial systems....