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 expanded its AI initiatives from foundational studies in the 1980s to advanced AI applications in 2025, encompassing operational efficiency, predictive maintenance, data analytics, and energy production optimization.
Chevron has progressively integrated AI across its operations from 2022 through 2025, focusing on improving operational efficiency, safety, and environmental stewardship while expanding into AI-powered energy solutions.
ConocoPhillips has consistently expanded its application of AI and digital technologies from 2021 through 2025, integrating AI-enabled platforms like seismic HPC, cloud-based DELFI environment, and digital twins to optimize exploration, drilling, and production operations, leading to operational efficiency gains such as drilling optimization by over 60 feet per day.
Phillips 66 has progressively integrated AI technologies since at least 2018, leveraging them to enhance operational efficiency, cybersecurity, data management, and customer experience across multiple divisions.
Marathon Petroleum has progressively integrated AI and machine learning technologies since 2019, evolving from digital monitoring and process optimization to leveraging AI for refinery operations, acquisitions, and large-scale alert management across 4,000+ wells.
Valero Energy has progressively integrated AI technologies since 2023, focusing on optimizing energy consumption, enhancing operational efficiency, and exploring renewable and battery storage initiatives, with notable increases in trading volume in 2025 reflecting market interest.
Kinder Morgan's AI-related opportunities have evolved from minimal mention in 2015 to becoming a significant growth driver by 2025, particularly due to AI-induced power demand from data centers and natural gas usage supporting AI infrastructure.
NextEra Energy has progressively integrated AI since 2018, evolving from use in nuclear plant reliability and work management (Q2 2021) to a comprehensive AI strategy by 2025, leveraging machine learning for grid load balancing, predictive maintenance, and market trading optimization.
Dominion Energy is experiencing significant growth in electricity demand driven by the rapid expansion of AI data centers, prompting an accelerated investment plan exceeding $50 billion focused on infrastructure enhancements, including nuclear energy projects and grid modernization.
Southern Company has progressively adopted AI technologies since early collaborations in 2019, moving from grid resiliency enhancements with mPrest to advanced AI integrations by 2025 such as digital twin infrastructure management (Aetos), regulatory data management, and generative AI for customer engagement.
47 Use Cases in Energy
| Company | Use Case |
|---|---|
| Dominion Energy | Load Forecasting Dominion Energy uses AI-driven predictive analytics and machine learning algorithms to forecast electricity demand from AI-intensive data centers, enabling efficient grid management and optimal resource allocation. traditional |
| NextEra Energy | Customer Savings Through its NextEra 360 platform, AI-based predictive analytics enable commercial clients to reduce operational costs, delivering measurable savings exceeding $700,000 per client. traditional |
| NextEra Energy | Grid Balancing NextEra leverages real-time AI algorithms to balance solar, wind, and conventional energy load on the electrical grid, improving reliability and responsiveness to demand spikes, especially from AI data centers. agentic |
| NextEra Energy | Predictive Maintenance NextEra applies predictive analytics and AI algorithms to monitor equipment health, enabling proactive maintenance scheduling that reduces equipment failures by 70–75% and cuts maintenance costs by up to 30%. traditional |
| NextEra Energy | Demand Forecasting NextEra utilizes AI-driven machine learning models to analyze historical data, weather patterns, and market trends, achieving approximately 95% accuracy in forecasting electricity demand to optimize power generation and grid management. traditional |
| ConocoPhillips | Decision Automation Using agentic AI models and large language models, ConocoPhillips automates data extraction and integrates multiple sources to support rapid, economically sound field development and operational decisions. agentic |
| ExxonMobil | Autonomous Operations ExxonMobil deploys autonomous AI agents to autonomously plan and execute actions aiming to cut operational costs, reduce emissions, and lead energy transition innovations. agentic |
| Valero Energy | Energy Storage Valero is developing battery storage and renewable fuel initiatives, supported by AI analytics to manage energy transition strategies and improve renewable energy integration. traditional |
| Southern Company | Infrastructure Monitoring Southern Company employs digital twin technology (via Aetos) and virtual inspections to remotely monitor and manage power plant and infrastructure assets, accelerating decision-making, reducing on-site inspections, and improving safety. traditional |
| Marathon Petroleum | Enterprise Monitoring Through enterprise-scale AI monitoring and alerting systems, Marathon Petroleum enhances process awareness, operational decision-making, and human-centric innovation. agentic |