The energy sector’s focus on efficiency gives hope to both AI and decarbonization

The energy industry is focused on efficiency above all else. This clarity is an unmissable opportunity for energy firms to align their technology suites—including artificial intelligence in its various forms—toward shared goals throughout their organizations and ecosystems, such as optimization and decarbonization.

HFS Research, in partnership with Infosys, recently hosted a roundtable of senior energy industry decision-makers. The gathering considered the sector’s progress on the energy transition and AI adoption (see Exhibit 1). After a lengthy discussion of the energy sector’s continuing talent challenge to recruit tech and sustainability-minded professionals—you can read about that separately here —the core of the discussion turned to how a traditionally lagging industry is finding its AI feet.

Overwhelmingly targeting efficiency is helping energy companies across the value chain to navigate the uncertainty of global markets and geopolitics. However—perhaps surprisingly, given the current news cycle—it is pushing forward the decarbonization of existing oil and gas assets (though not quickly enough). The margins of renewable energy do not currently match those of fossil fuels for the major energy companies. Sustainability needs systemic change—we cover that here—but in parallel, while we aim for positive tipping points, decarbonizing existing energy assets is a must.

Exhibit 1: “Despite everything, there’s still time to lead on AI and the energy transition—while it’s still an option. What’s stopping us?”—Josh Matthews, practice leader at HFS Research, at a recent energy roundtable supported by Infosys

Source: HFS Research, 2025

Energy spending on AI is up, but many enterprises have yet even to reach ‘pilot purgatory’

After a lull in 2023 across all sectors, technology spending rebounded in 2024, including in the energy and utilities industry (see Exhibit 2a). Energy spending on emerging technology, encompassing AI, is outpacing IT and business process services (see Exhibit 2b), with our broader data suggesting AI—both generative (GenAI) and machine learning (ML)—is well ahead in the technology mindshare ranking of energy enterprises. However, many energy firms have not even reached the piloting phase (see Exhibit 2c). There’s a tremendous amount of work to do.

It appears most energy companies have pivoted to a two-pronged strategy—what we call CORE and NEXT—to take advantage of market opportunities. Energy companies have the onerous task of navigating a duality of making money from current fossil fuel investments while preparing and investing for a sustainable future through an energy transition. In Europe, that transition is going to be driven by regulations and policies, while in the US, it is driven by market economics.
 
AI is playing a massive role in core scientific areas of the transition, like material sciences, including metallurgy and energy management. AI is also driving efficiencies across three dimensions for our oil and gas customers: asset intelligence, decision intelligence, and intelligent agents/co-pilots.

—Sriram Sundar, VP & business head, Energy Core, Infosys

Exhibit 2: Energy spending on technology is rising, but AI is not yet fully operational

Source: HFS Research; 56 energy, resources, and utilities executives from the broader HFS Pulse study of 605 Global 2000 enterprise executives, 2025

Several energy enterprise use cases stand out despite the lack of fully operational AI

In our ongoing Energy and Utilities Horizons study (see launch details here), positive AI applications in energy under the efficiency banner include:

• Incorporating AI, digital twins, and plant optimization: Computer vision and document processing in tandem are mapping operations in 3D. At the same time, ML analyzes integrated plant data, and GenAI produces new recommendations (although operators remain the main decision-makers).
• AI for customer experience (CX): As energy firms enter the retail market, CX quickly becomes top-of-mind. Oil and gas firms lack the long-standing experience of managing large CRM databases and call centers that utility firms do. However, GenAI shows promise in dissecting customer contacts and helping enterprises respond. Creating ‘net-positive proactive’ CX is our 3-5 year vision at HFS. Can the sector incorporate value-added services such as solar and heat pump installation rather than merely providing an adequate CX once a problem has occurred?
• Decarbonization, including spotting methane leaks: Global oil and gas commitments to cut methane emissions amount to 55% reductions by 2030. Specific policies amount to only 20%. However, investment is underway, and despite cuts in capital spending for clean energy (which was limited anyway at $30 billion vs. $560 billion in fossil energy investment in 2024), methane emissions reduction plans continue. Efficiency, in part, drives this: Reducing natural gas losses cuts both costs and greenhouse gas emissions. AI is spotting leaks, analyzing flaring, and contributing to broader process optimization.
• AI is also helping energy firms reduce the burden of ESG reporting, find value beyond disclosure, and enable professionals to ‘find their life’s work.’ Refer again to our report on energy’s ongoing talent crisis.

With advancements in AI, the energy industry has the power to reimagine its entire value chain. AI is enhancing efficiency in oil and gas exploration, reducing carbon footprints, and simultaneously optimizing the generation, distribution, and management of renewable energy. AI is accelerating the energy transition and shaping a more sustainable future for the industry.

—Joseph Alenchery, SVP & business head,(Energy Next), Infosys

Energy has not suddenly embraced AI—but the sector has clarified its approach by aligning technology toward a goal of radical efficiency to insulate against a highly uncertain global context

However, plenty of energy enterprise debts remain as barriers.

Despite spending plans and encouraging use cases for AI and emerging technology in energy, barriers plague energy firms. These include a range of enterprise debts, from culture to technology (digital and physical in energy, especially), data, process, strategy, and skills. A lack of data readiness came up for discussion at the roundtable. To summarize:

Without good data, there is no AI.

—A collective sigh from the enterprise executives at our recent energy sector roundtable

The Bottom Line: Energy must commit to collaboration aligned with efficiency goals for the sector to thrive, whether through AI or the energy transition.

Our energy innovation assessment (outlined in detail here) examines the need for aligned goals and a clear connection between business and technology teams. Also needed is alignment between innovation teams across time horizons from the day-to-day to the five-year-plus applications of technology.

Our research has also suggested that fewer than half of senior energy transition enterprise leaders collaborate internally toward their goals, and collaboration drops significantly throughout the ecosystem. That disconnect is improving, as the energy sector’s drive for hyper-efficiency helps align the use of technology toward clear goals. However, it is not improving quickly enough.