Artificial Intelligence in Energy Companies
We analyzed the enterprise AI use cases of 10 energy companies to understand trends, impact, and insights.
Energy companies' adoption of AI
Overview
ExxonMobil has steadily adopted AI and machine learning since the 1980s, with significant acceleration from 2020 onwards, leveraging partnerships with Microsoft Azure and Intel to improve operations such as predictive maintenance and production optimization.
Chevron has progressively integrated AI across its operations since 2022, evolving from foundational adoption to advanced AI-driven processes by 2025, including generative AI platforms and agentic AI systems cutting drilling costs by up to 50% and doubling well production.
ConocoPhillips has progressively integrated AI technologies across its operations since 2021, expanding from seismic data processing to portfolio-wide digital twins and AI-driven workflows that optimize decision-making, supply costs, and resource development.
Phillips 66 shows a clear and growing adoption of AI technologies since at least 2023, with advancements in AI-driven operations, cybersecurity, and customer experience enhancements, notably a rollout of AI-powered checkout solutions in early 2025.
Marathon Petroleum has progressively integrated AI technology since 2019, starting with digital monitoring and optimization tools and expanding to extensive AI-driven refinery optimization and safety protocols by 2024-2025.
Valero Energy has progressively increased its adoption of AI technologies focused primarily on energy optimization, operational efficiency, and predictive analytics from 2023 through 2025, notably leveraging AI to reduce energy consumption across facilities and improve renewable fuel initiatives.
From mid-2024 onward, Kinder Morgan (KMI) positioned itself as a critical energy infrastructure player benefiting from the surge in AI and data center energy demand, driving a projected increase of up to 7-16 Bcf/d natural gas demand by 2030.
NextEra Energy has progressively embedded AI across its operations from 2018, with recent AI integration efforts substantially accelerating renewable energy deployment, predictive maintenance, and power market optimization, driven and endorsed by CEO and key partnerships including GE Vernova and Google.
Dominion Energy is actively expanding and modernizing its energy infrastructure to accommodate surging electricity demand driven by the growth of data centers supporting AI workloads, notably accelerating a $50 billion infrastructure plan through 2025 to 2030.
Southern Company has progressively integrated advanced AI technologies from 2019 through 2025, evolving from grid resiliency collaborations (mPrest in 2019) to sophisticated AI deployments in infrastructure, worker safety, regulatory compliance, and customer engagement by 2025.
48 Use Cases in Energy
| Company | Use Case |
|---|---|
| Chevron | Exploration Insight Chevron’s ApEX generative AI platform increases accessibility of exploration and subsurface data, automating manual tasks and enabling faster identification of promising drill sites. generative |
| Kinder Morgan | Project Planning KMI incorporates AI-driven strategic analysis and forecasting into capital project planning, accelerating decision-making for expansions like the Mississippi Crossing and South System Expansion 4, reducing costs and optimizing return on investment. agentic |
| ConocoPhillips | Workforce Automation AI is deployed to automate administrative and operational processes, enabling ConocoPhillips to improve efficiency, reduce headcount by approximately 25% during restructuring, and focus workforce on high-value activities. agentic |
| Southern Company | Nuclear Optimization Southern Nuclear employs Nuclear-Grade AI software, including machine learning algorithms and neural networks to optimize fuel use, predict plant operational risks with high accuracy, and reduce unplanned derates, enhancing nuclear plant safety and efficiency. agentic |
| Chevron | Operational Efficiency Chevron uses AI-driven analysis and predictive analytics to optimize drilling operations, reducing costs by up to 50%, increasing drilling speed by 30%, and doubling well production per rig, particularly in the Permian Basin. agentic |
| ExxonMobil | Emission Reduction Autonomous AI agents are employed by ExxonMobil to reduce greenhouse gas emissions and achieve sustainability goals, integrating AI-driven decision making throughout operations. agentic |
| NextEra Energy | Demand Forecasting NextEra deploys AI for demand forecasting with approximately 95% accuracy, allowing the company to balance grid loads dynamically and anticipate surging AI data center demands to efficiently plan resource allocation and infrastructure investments. traditional |
| NextEra Energy | Energy Trading Using machine learning algorithms, NextEra optimizes energy dispatch and market bidding through tools like Automatic Trader, enabling real-time market participation and maximizing revenue from renewable generation assets. traditional |
| NextEra Energy | Predictive Maintenance NextEra Energy uses AI-driven predictive analytics to detect potential equipment failures before they occur, reducing maintenance costs by 25–30% and decreasing equipment failures by 70–75%. This improves reliability and operational efficiency across its renewable and nuclear assets. traditional |
| Valero Energy | Process Optimization AI models are used by Valero to optimize fermentation processes in biofuel production and refinery operations, improving yields and reducing waste to enhance production efficiency. traditional |