AI use cases in the energy industry: Insights from research and company filings

Implementing artificial intelligence has become an imperative for companies across industries, including those in energy. Identifying the right use cases is a critical step in maximizing the value of this technology. This is where sector-specific use cases are especially important to understand.

While some companies have successfully deployed AI and machine learning use cases—from the front and back offices to plant and field operations—challenges remain. This calls for leaders to ask key questions such as:

• Which AI use cases warrant funding?
• How can those use cases be scaled?
• What underlying issues need to be resolved first to maximize the value and likelihood of success for AI implementations?

There are no easy answers to these questions, but an RSM analysis of how middle market energy companies are deploying AI offers valuable insights, including lessons learned and an understanding of how AI use cases vary across energy subsectors.

This analysis provides a data-backed view of how middle market energy companies are deploying AI and what others can learn from their experience.

Here are some of the key takeaways:

• AI adoption is becoming increasingly common across the energy sector. Nearly half (45%) of the 307 middle market energy companies analyzed had at least one verified AI or machine learning use case in operation.
• Utilities appear to be deploying AI at a higher rate than oil and gas companies. Confirmed AI deployment rates reached 90% among electric utilities, compared with lower adoption levels across most oil and gas subsectors.
• Operational use cases are leading adoption. Companies are most frequently applying AI to field operations, asset integrity, maintenance and process optimization, where measurable business value is often easier to demonstrate.
• Predictive maintenance has emerged as the most common use case. It was the most frequently identified AI application across both oil and gas companies and utilities, reflecting the industry's focus on reliability and asset performance.
• Many successful deployments rely on third-party technology providers. Half of the verified AI implementations identified in the research used external vendor platforms, suggesting companies do not necessarily need extensive in-house AI development capabilities to begin generating value.

Take a closer look at the findings below.

Comparing AI deployments across energy subsectors

Across the 307 companies, 45% have at least one AI or machine learning use case running in operation. Because the analysis excluded unverified announcements and use cases without specific evidence, actual adoption is likely higher. Our conversations and work with energy companies on their AI journey anecdotally point higher still.

Confirmed use of AI varies widely across the energy industry, from 90% of electric utilities to 15% of biofuels producers. In fact, electric utilities appear to be deploying AI at more than twice the rate of oil and gas companies. Oil and gas may be at an inflection point, however, as mentions of AI in earnings calls by Russell 3000 energy companies tripled in the first half of 2026 compared to the same period in 2025.

Grouped more broadly, power and utilities companies showed a 77% AI deployment rate, compared with 43% for renewables and 33% for oil and gas companies. Some of that gap may be because utilities more often operate under a regulated filing regime and provide more disclosures.

The next natural question is what specific use cases are most often named, and what value they are delivering.

AI adoption in the energy industry: Real use cases

In our analysis of AI use among energy companies, some of the common use cases companies identified were:

• Drilling and completions automation
• Grid operations and outage management
• Predictive maintenance and asset health
• Wildfire risk and detection
• Load, weather and price forecasting
• Process control and plant optimization

Overlap between the oil and gas sector and the power and utilities sector varied by use case.

Other insights from our analysis include:

• Field and industrial operations use cases dominate adoption, although the nature of operations varies in oil and gas vs. power and utilities.
• Predictive maintenance is the most popular use case across the board, cited by 22% of oil and gas companies and 29% of utilities.
• Companies don’t need to build everything themselves; 50% of confirmed deployments named an external vendor for AI deployments.

Translating AI use cases into business value

More AI deployments have documented business outcomes now than when we last analyzed industry use cases in 2023.

The examples below show how companies across energy subsectors are turning use cases into business value.

Importantly, these are company- and vendor-reported figures, so we interpret them not as a promise but as a directional signal of where AI is heading and what a well-executed deployment can achieve. Notably, we’re now seeing agentic AI showing up in use cases, and we only expect its presence to grow.

What is slowing companies down in their AI adoption

When it comes to slow AI adoption, the obstacle is rarely the AI itself. In our client work, echoed by surveys from the Massachusetts Institute of Technology, the most common constraints relate to data availability and governance, lack of trust in the AI output, cybersecurity and privacy/regulatory concerns, lack of executive sponsorship, and difficulty managing organizational change.

Scaling AI use cases from pilot to implementation is also a key challenge. Among respondents to RSM’s Middle Market AI Survey 2026: U.S. and Canada, only 36% of respondents have AI fully embedded across core processes. Among those who conducted AI pilots in the prior two years, about half (51%) described the success of those pilots as moderate or limited—and among those respondents, data quality issues (53%) and integration challenges (47%) were the leading reasons.

Further, the survey showed that while technology-related items are the more common inhibitors to AI deployment, two-thirds of respondents noted AI governance was established before implementing AI pilots or production.

Actions to consider

Energy companies looking to accelerate AI adoption should:

• Prioritize high-value use cases. Identify and focus on use cases that are most applicable to the given subsector and have a proven track record.
• Build the data foundation first. If AI is the engine, data is the fuel.  Establish and strengthen data ownership, governance, quality and accessibility before scaling AI initiatives.
• Leverage existing technology platforms where possible. Start with AI capabilities already embedded in vendor solutions rather than defaulting to custom development.
• Begin the organizational change discussion now. Successful adoption goes beyond individual projects to foster a culture of trust and employee empowerment. This shift is often just as difficult to achieve as the technological one.
• Evaluate available tax incentives. AI and technology investments may qualify for federal or state research and development tax credits.

Over the coming years, we expect the constraint on AI adoption among energy companies to be less about access to the technology and more about whether a company’s data is in a condition to use it. Those that make strategic investments now to organize and govern their data will be better positioned to capture value as adoption accelerates.