Much of the debate around artificial intelligence and energy has focused on one question: How much electricity AI will consume? The discussion is important: Data centers are expanding rapidly, utilities are reassessing future demand, and governments are debating how best to power the next wave of digital infrastructure — but that tells only part of the story. A more subtle transformation is unfolding inside the energy industry itself. From offshore platforms and LNG plants to refineries, pipelines and electricity grids, AI is beginning to change not only what companies produce but also how they operate. The race is no longer only about discovering bigger reservoirs, building larger facilities or adding more generating capacity. Instead, it is increasingly about running existing assets more intelligently — and the shift is no longer theoretical.In Libya, state National Oil Corp. (NOC) recently used an AI-driven drilling system from SLB to guide a well through the most productive layer of the Al-Khair field. The rate of penetration doubled compared with earlier wells in the same field, and the well came in producing more than 1,000 barrels per day on initial tests. At TotalEnergies, a five-year-old, in-house "Digital Factory" of roughly 300 engineers and data scientists has already built more than 100 digital tools, around 60 of them using machine learning or generative AI; work the company is now scaling up through a new joint lab with the French AI firm Mistral.Nor is the shift confined to upstream oil and gas. Baker Hughes has paired with Repsol to roll out a generative AI assistant that helps engineers interpret production data in real time and separately sells AI-based asset management software that flags equipment problems before they cause downtime. Siemens Energy uses similar tools to watch over power plants and grids: At one California facility, the company’s monitoring service both defends against cyberattacks and catches early signs of equipment failure. At another site in Virginia, computer vision keeps watch over plant operations around the clock. These are not isolated pilot projects. They point to a broader change in how energy companies are thinking about competitiveness.Operational DecisionsAt first glance, these appear to be very different applications. One improves drilling performance, another helps engineers interpret production data, while a third monitors turbines and electricity grids. The common thread is not the individual technologies — it is that AI is helping companies make better operational decisions, whether about where to drill, when to maintain equipment or how to optimize production. That shift, from improving individual processes to improving operational decision-making, may ultimately prove more important than any single application.For decades, the industry's competitive advantage rested primarily on geology, engineering and scale. Companies invested billions of dollars to find larger resources, build more efficient facilities and improve recovery rates. Those fundamentals remain as important as ever. But another source of competitive advantage is emerging alongside them: the ability to make faster, better-informed operational decisions.AI is allowing companies to analyze far greater volumes of operational data than any human team could realistically process. Equipment can be monitored continuously rather than inspected periodically. Maintenance can increasingly be scheduled before failures occur instead of after equipment breaks down. Production systems can be adjusted in near-real time as operating conditions change. Engineers are spending less time searching for information and more time making decisions.None of this is particularly dramatic, but that is precisely the point. Unlike many discussions around AI, the biggest gains in energy are unlikely to come from spectacular technological breakthroughs. They are more likely to come from thousands of incremental improvements repeated every day across thousands of assets.A compressor that fails less frequently; a refinery operating slightly closer to its optimal configuration; an LNG train avoiding an unexpected shutdown; a wind farm producing marginally more electricity because maintenance was better timed: Individually, these improvements may appear modest. However, across a global energy system worth trillions of dollars, they become highly significant.The Paris-based International Energy Agency (IEA), in its landmark Energy and AI report and a follow-up assessment published in April titled Key Questions on Energy and AI, concludes that existing AI applications could reduce energy costs in energy-intensive industries by three to 10 percentage points, and that well-documented AI use cases could save more than 13 exajoules of energy by 2035 — about 3% of global final energy consumption — if adoption barriers are overcome. Those barriers are real. The IEA's own survey of energy companies found that a shortage of digital skills, more than any technical or infrastructure constraint, is the single-biggest obstacle to wider adoption.Those constraints deserve attention because AI is not a plug-and-play solution. Much of the energy industry's infrastructure was designed decades before today's data-driven technologies emerged. Operational data often sits in disconnected systems. Equipment from different vendors may not communicate easily. Integrating AI into critical industrial environments requires careful governance, high-quality data and considerable engineering expertise.There is also a human dimension that is sometimes overlooked. AI is unlikely to replace experienced engineers, geoscientists or operators. Rather, it changes how they work. Routine analysis can increasingly be automated, allowing specialists to focus on interpreting results, managing risk and making more informed operational decisions. In that sense, AI could prove less of a replacement for expertise than a multiplier of it.This distinction matters because many public discussions still frame AI primarily as a labor issue or an infrastructure challenge. Within the energy sector, however, the more immediate questions are often about productivity: Can existing assets operate more safely? Can downtime be reduced? Can emissions be lowered? Can maintenance be planned more efficiently? Can scarce technical expertise be deployed where it creates the greatest value?Commercial ValueFor many companies, those questions have become strategic rather than operational, and the industry's largest players increasingly recognize this. AI now features regularly in corporate strategy presentations, investor briefings and technology partnerships. What was once viewed as an experimental digital initiative is gradually becoming part of core operational planning. Companies are not investing simply because AI is fashionable. They are investing because even relatively small improvements in uptime, recovery, efficiency or maintenance can create substantial commercial value when applied across large portfolios of assets.That does not mean every AI project will succeed. Like previous waves of digital transformation, some initiatives will disappoint, others will struggle to scale, and many organizations will discover that cultural change is harder than technological change. Companies that lack reliable data or clear operational objectives are unlikely to realize AI's full potential.Nevertheless, the direction of travel appears increasingly clear. The competition is expanding beyond who can build the biggest LNG terminal, drill the deepest well or install the largest renewable portfolio. It increasingly includes who can make the best operational decisions consistently and at scale. Those advantages will continue to matter, while another source of competitive strength is emerging — one built not on new physical assets, but on making better decisions about the ones already in operation.With this in mind, the next leaders in energy won’t just be those who build the most — but those who operate the best.Oliver Klaus is Energy Intelligence’s AI product development manager. Prior to that, he served as Dubai bureau chief for more than 10 years, leading coverage of the Mideast region. The view expressed in this article are those of the author.