How AI Is Powering the Future of Energy
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From Microsoft
The introduction of artificial intelligence (AI) into the energy sector didn’t happen overnight. It evolved from years of digitizing infrastructure, collecting massive volumes of operational data, and embracing automation. Today, AI is a driving force behind cleaner grids, safer plants, and smarter business decisions.
From robotic inspections and predictive maintenance to AI-powered grid simulations, here’s how energy companies are turning AI into action—and why those who hesitate on AI risk being left behind.
From Digitization to Intelligence
The foundation for AI in energy was laid when industrial companies began digitizing their facilities—often starting with CAD drawings and 3D scans. At Environmental Intellect (Ei), for example, early work in scanning plants evolved into digital twin tools that now incorporate AI and machine learning for asset tracking, emissions monitoring, and operational decision-making.
These digital twins are becoming interactive systems. At Shell’s Geismar facility, personnel can virtually walk through the plant, inspect assets in 3D, and retrieve critical data—all before setting foot in the field. This kind of integration—where AI analyzes physical and operational data in real time—is revolutionizing how maintenance is planned and executed.
Real-World AI Use Cases in Energy
Here’s how energy leaders are using AI in concrete, high-impact ways:
1. Predictive Maintenance
Shell, in partnership with Baker Hughes and Bentley Nevada, implemented predictive AI models to monitor valve and equipment performance. These models detect early signs of failure and self-train to understand what “normal” looks like, enabling preemptive fixes without halting operations.
AES developed AI-driven predictive maintenance programs to anticipate component failures, optimize repair costs, and manage demand prediction.
2. Grid Planning and Resilience
At the grid level, AI is compressing years of planning into minutes. ThinkLabs AI developed simulations that can run multi-year power flow analyses on 100+ circuits in under five minutes. That level of speed enables utilities to model demand scenarios, test contingencies, and plan upgrades with unprecedented agility.
Duke Energy uses AI to improve the resilience of its electrical grid, especially in the face of extreme weather. Duke implemented hybrid AI systems that continuously analyze sensor data, historical performance, and external variables to detect early signs of stress or wear. Similarly, Georgia Power uses AI to simulate grid performance under extreme conditions and flag vulnerable components before outages occur.
These tools help utilities navigate the dual pressure of electrification and sustainability, allowing them to meet rising energy demands while improving resiliency.
3. AI-Powered Robotics and Inspections
ExxonMobil Baton Rouge uses a pipe-inspection robot that autonomously crawls through infrastructure to assess integrity. Other ExxonMobil sites deploy robotic dogs equipped with gas detectors and laser scanners. These machines extend human capabilities in hazardous environments and reduce the need to put workers in risky situations.
Shell and ExxonMobil are also exploring how large language models (LLMs) like ChatGPT can be trained to mine legacy documents, assist in code conversion, and even summarize operational data.
Humanoid robots, which rely heavily on AI for performance, are being developed to help with workforce shortages and keep human workers safe from hazardous tasks and environments.
AI for Workforce Transformation
One of the most profound impacts of AI is how it's reshaping the energy workforce. Rather than replacing people, AI is elevating them.
At LSU’s Executive MBA program, students—many from petrochemical companies—are now required to integrate AI into their organizational strategies as part of their coursework.
“There’s a misconception that AI will automate processes and therefore be used to cut jobs. But if you’re using tech to cut jobs, you will be disappointed,” Professor Andrew Schwarz of LSU said. “It’s making your workforce more competitive by allowing them to focus on the insights that matter.”
AI Energy Challenges and Cautions
Despite the momentum, barriers remain. Many legacy systems weren’t built with AI integration in mind. Security is also top of mind—leaders are cautious about uploading sensitive plant data into public models like ChatGPT.
That’s why AI rollouts in critical infrastructure require careful validation and change management. Energy company IT teams are building processes to ensure users are properly trained and that the technology is embedded into daily workflows.
Industrial AI Beyond Energy
Many of these lessons extend beyond energy production. ARC Advisory Group identified 25 high-value AI use cases for industrial environments, including:
Generative Design
Sustainability Analysis
Inventory Optimization
Waste Reduction
Employee Retention
And more
These use cases span the entire lifecycle of energy assets and products, reinforcing AI’s role in long-term sustainability.
A Future Powered by Intelligence
AI isn’t replacing engineers, operators, or planners—it’s augmenting their abilities and allowing them to make better, faster decisions. The energy companies embracing this shift aren’t just deploying new tools; they’re building future-ready systems that are more resilient, adaptive, and sustainable.
Whether you’re retrofitting a refinery or reinventing a grid, AI is a powerful differentiator.
As Henry Hays of DisruptREADY put it: “We always tell people that AI won’t take your job; someone who knows how to use AI will take your job.”
See real-world AI, drone, and robotics solutions in action at the Energy Drone/Robotics & Industrial AI Forum this November! Register now to secure your spot and connect with the innovators turning cutting-edge tech into results!
