Artificial intelligence is driving transformation across nearly every industry. In energy, the impact runs deeper than most people realize.
According to Cody Brown, Sr. Director of Client Services at Eliassen for the Energy industry, AI is not just another wave of innovation, it’s creating a ripple effect that is fundamentally reshaping both demand and operations across the grid. And for Brown, this perspective comes from more than just time in the field.
With nearly a decade of experience supporting technology initiatives across the energy sector, the backing of a firm with a 15-year presence in the industry, and a personal connection through his father's 35-year career engineering electric grid infrastructure, Brown brings a rare, grounded perspective on how this traditionally stable industry is evolving in real time.
“What we’re seeing is a dual effect,” Brown explains. “AI is increasing demand for electricity while also helping utilities and energy organizations operate more efficiently.” That tension between rising consumption and improved efficiency is where the real story begins.
Most conversations about AI focus on efficiency, automation, and innovation inside businesses. What’s often overlooked is the massive infrastructure required to power it.
“AI adoption is contributing to increased electricity demand through the rapid expansion of data centers and compute-intensive infrastructure,” Brown explains. “Every AI tool we use relies on hardware and data centers, and that infrastructure consumes a significant amount of energy.”
As organizations accelerate AI adoption, the demand for the energy required to support compute power is rising rapidly. Recent forecasts from the IEA suggest electricity consumption from data centers could more than double globally by the end of the decade, driven largely by AI workloads. "What's changed is that AI-driven data center demand is growing at double-digit rates in many markets, creating new pressure on utilities and grid infrastructure."
That shift is forcing utilities and power providers to expand at a pace and scale unprecedented in the industry. Many states and local governments are competing to attract data center investment, and utilities are under pressure to expand capacity, often with long lead times and major infrastructure investments. For an industry once defined by predictability, that’s a fundamental change.
Brown points to industry forecasts from organizations such as the International Energy Agency (IEA), U.S. Department of Energy (DOE), and major utilities, which increasingly identify data center growth as one of the most significant emerging sources of electricity demand over the coming decade.
While AI is increasing demand externally, it’s also accelerating transformation inside energy organizations.
For decades, utilities have operated on legacy systems given the regulatory scrutiny and the critical nature of power infrastructure. But that model is starting to shift. “Because of reliability requirements, regulatory oversight, and long asset lifecycles, utilities have often modernized more cautiously than many other industries,” says Brown. “But now, major utilities are expanding their use of cloud platforms, advanced analytics, and AI to improve operational efficiency, reliability, and decision-making."
What’s changed isn’t just technology readiness but also risk tolerance. “For many utilities, the operational and cybersecurity risks associated with aging systems are increasing the urgency to modernize,” he explains. Between escalating cybersecurity threats and the need for more agile systems, utilities are rethinking long-standing approaches to infrastructure and operations.
The utilities that successfully combine modernization with AI-enabled decision-making will be best positioned to navigate the growing complexity of the energy landscape.
Brown believes some of the most valuable AI applications will emerge in operational decision-making, where utilities are constantly balancing safety, reliability, and efficiency. Emergency response in natural gas operations is one example.
“When customers report potential gas leaks, AI can analyze the urgency of each situation and help prioritize response times,” he explains. “A high-risk scenario gets immediate attention, ensuring safety while improving efficiency across the system.”
In these applications, AI serves as a decision-support tool, helping employees prioritize and evaluate information more quickly while retaining human oversight.
While increasing generation capacity is part of the long-term solution, it’s not something that can happen overnight. “As electricity demand grows, utilities can't rely solely on adding new generation capacity. Those investments often take years to plan and deploy,” Brown notes. That’s why many organizations are focused on a different lever: efficiency.
By leveraging AI and modern control systems, utilities can better manage demand, incentivize off-peak usage, and optimize energy distribution, effectively stretching existing capacity further before new infrastructure comes online. It's not just about adding capacity. It's about optimizing how existing capacity is managed and utilized.
In contrast to narratives around AI replacing jobs, the energy industry is seeing a different trend: workforce evolution and expansion.
Brown points to growing demand for professionals who can implement and integrate AI into complex, highly regulated systems, which is work that requires both technical expertise and deep industry understanding. In many areas of the industry, AI is reshaping how work gets done while increasing demand for specialized talent in areas such as AI, data, cybersecurity, cloud, and grid modernization.
The energy industry is entering a new phase that is defined not just by modernization, but by necessity. AI is accelerating demand. It’s forcing infrastructure decisions. It’s driving new operating models. And it’s doing so in a way that connects directly to every individual and organization that relies on power, which is to say, everyone.
From Brown’s vantage point, this isn’t just another technology shift. It’s a structural one.
And for organizations paying attention, the question isn’t whether AI will impact energy, it’s how quickly they can adapt to the ripple effects already underway.
Cody Brown
Senior Director of Client Services, Energy Vertical Sales