It seems like everywhere you go people are talking about AI, and if you’re like most of us, you’re using AI in almost every aspect of your life. Similarly, organizations are trying to figure out how AI can make workers more productive, and this is obviously true in the utility technology industry.

Utilities are exploring how AI can be used for preventative maintenance, strategic capital planning, source data extraction, and load forecasting. Now more than ever, utilities want to understand the dynamic needs of its customer base to modernize the grid and address quickly growing demand from things like AI data centers and EV‘s.

There are many types of technologies that can be classified as AI, but the most viral excitement and perceived promise is from GenAI; however, as Gartner has indicated, we are currently in the “trough of disillusionment” of the hype cycle phase of GenAI technology. Many GenAI projects are still failing to achieve the value they were set out to provide. But this doesn’t mean AI can’t work for you, and it certainly doesn’t mean you don’t need to prepare for this “inevitable” future.

At SSP, hopefully you’ve seen for a while now that we’ve been thinking about the most efficient use of AI, not only in our own jobs, but also how to make our utility customers more efficient. We spent the last several years building/prototyping technology like our computer vision enabled mobile tool for collecting assets in the field, as well evaluating Esri’s new GeoAI  and AI Assistants. We’ve learned a lot of lessons along the way and one thing is still apparent (like with many new, exciting technologies – e.g., see ADMS) – data is still King.

AI (especially GenAI) backed by fragmented or inconsistent data will produce noise. There may be a few early signs of promise, but as data limitations are reached, the technology will fall flat. And as you’ve probably seen in the latest hilarious, viral Instagram posts, it will confidently lead decision makers in the wrong direction. A disaster for any utility.

Utilities continue to face fundamental problems where the infrastructure in the field doesn’t match their source documentation and system data doesn’t match what their operations see in the field. None of these powerful, modern tools can realize their value without a strong data foundation that is interconnected among systems like GIS, asset management, and operational technologies like SCADA and ADMS. Further, there are tons of critical data that have gone uncollected or improperly documented.

SSP has a number of proven services helping utilities build and strengthen their GIS and the system’s it’s connected to that will help utilities to prepare for the AI future. We provide services like:

• Data QAQC to determine if data is ready for modernization (leveraging the full capabilities of network modeling)

• Source record validation

• Determining enterprise asset models and building connected integrations

With assets modeled accurately and consistently throughout utility systems and a high-quality GIS tightly coupled with operational technology (like hydraulic models, SCADA or ADMS), AI solutions can be a powerful decision maker. Without that foundation, AI will just amplify the weakness of your system data.

But with a strong data foundation in place, AI can start doing the really cool (and valuable) things, like:

• Capital planning that blends spatial data, failure history, and environmental risk

• Demand forecasting that uses network topology, consumption patterns, and climate trends

• Anomaly detection that spots issues long before a human would