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Dive Brief:
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Globally, 33 percent of utility and energy companies have begun implementing generative artificial intelligence — algorithms that can generate text, images, computer code and other content — in their operations, according to a study released last week by digital think tank Capgemini. .
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Nearly 40% of utility and energy companies will establish a dedicated team and budget for generative AI, while 41% say they have taken a “watch and wait” approach to the technology. But 95% of utility and energy companies said they discussed the use of generative AI in the past year.
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A third of those surveyed in the energy sector are experimenting with the potential of generative AI to generate tangible data that can be used to shorten development time, but early AI adopters say the energy sector industry is just beginning to experiment with AI. Use cases to their maximum potential.
Dive Insight:
Utilities are generally conservative in their adoption of new technologies, but they seem to be in line with most industries when it comes to generative AI growth, said Doug Ross, vice president of information and insights for Capgemini.
Energy and utilities companies that participated in Capgemini’s survey were adopting generative AI along with other industries. The survey results show that 39% of energy sector companies have teams and budgets dedicated to generative AI, compared to a global, global average of 40%.
Although ChatGPT has boosted awareness of generative AI in recent months, the technology itself has been around for at least three years in various ways, Ross said. Still, generative AI has attracted increasing interest from a variety of industries—and utilities are no exception, according to Capgemini’s survey data.
Utilities see generative AI as having the potential to accelerate growth rather than a disruptive threat, Ross said. And their assumptions are consistent with the experience of the technology. He cites a case study of an insurance company that plans to implement AI in its call center to reduce the average call time with customers. The call time did not decrease, but the company showed an overall increase in sales. Ross believes this is because AI has reduced the time spent on tasks such as data collection, allowing customer service representatives to spend more time developing relationships.
52 percent of energy companies showed interest in deploying AI to their sales teams in a Capgemini study. They are also interested in more technical uses of generative AI, such as using it to generate realistic but synthetic data to support IT and development processes.
But the focus on using AI in these capacities means most utilities are still experimenting with AI at a “surface level,” said Raj Chudgar, a consultant at data center vendor AJDicon X.
EdgeConneX began testing EdgeConneX’s service from energy provider Gridmatic in January to test whether artificial intelligence could help the company achieve the 24-7 clean energy standard envisioned by Google. The company has been using annual renewable energy credits to offset its energy use for two years, but wanted to take its sustainability goals to the next level, said Anand Ramesh, senior vice president of Edgecon X Advanced Technology.
The company’s initial goal was to match 80 percent of its electricity use with clean energy by the end of two years without significantly increasing its energy costs. AI has almost achieved its goal in the first few months and should be close to 90% by the end of the year, which is better than expected, Chudgar said. But he said it was unlikely that AIA could achieve 100% renewable energy with its current resources without incurring huge costs.
Providing 24-7 clean energy is one of three AI use cases to facilitate bidding in wholesale energy markets since Gridmatic began working with artificial intelligence six years ago, said Lisa Lee, Gridmatic’s chief marketing officer. The company has seen success using AI to optimize the operation of energy storage assets and manage demand-side efficiency programs. While there is a place for AI chatbots and similar areas in customer service, the biggest potential benefits of AI will be realized elsewhere, he said.
“Customer-facing, front-line impacts are probably the most obvious and the most immediate,” Lee said. But there will be deeper things that can be hidden and have a greater impact.
Ross agreed that there are two potential use cases for AI in the utility sector – faster, lower-risk opportunities like AI for generating posts on social media, and higher-risk tasks like grid planning for utility core tasks. The latter, Ross says, can take longer to implement simply because utilities have to get regulators to sign off on these AI uses.
And the full implementation of these latter AI use cases may take longer than many expect — perhaps 10 to 20 years, Chudgar said. Private businesses like EdgeConneX may be able to move a little faster because they don’t face the same regulatory hurdles, he said. But even for competitive markets, the number of skilled professionals who can build and deploy AI is limited, Chudgar said.
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