Top 5 Use Cases for AI in Industrial Applications

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AI is embedded in daily industrial operations at some of the world’s largest energy, utilities, and mining companies. From predicting equipment failures before they happen to optimizing production and modernizing aging infrastructure, AI is helping industrial organizations operate more safely, efficiently, and competitively.

Below are five real-world use cases showing how major industrial players are applying AI at scale to solve complex operational challenges.

1. bp Applying AI Across the Full Value Chain

bp is using AI and advanced digital technologies across its U.S. operations to improve drilling performance, refinery efficiency, maintenance planning, and equipment reliability. The company leverages AI to optimize drilling trajectories in regions like the Gulf of America and the Permian Basin and have cut analysis timelines from months to days.

Beyond drilling, bp has deployed digital twins of offshore platforms that combine laser scans, machine learning, and cloud computing. These virtual models allow teams to plan maintenance remotely, prioritize repairs, and reduce offshore travel. 

AI is also being used at bp refineries to rapidly analyze near-real-time data and deliver actionable insights directly to frontline operators. Machine learning systems further help predict offshore equipment issues before they escalate into production disruptions.

2. Utilities Using AI to Stabilize and Modernize the Power Grid

Utilities are cautiously but steadily adopting AI to manage mounting pressure on the electric grid. Startups like Rhizome are working with utilities including Seattle City Light and Vermont Electric Power Company to model climate-driven grid risks. In one case, a Texas utility reduced storm-related outages by more than 70% using AI-driven risk modeling.

Predictive maintenance is one of the most mature and impactful use cases for industrial AI. By combining machine learning with sensor data, utilities can detect equipment failures earlier and reduce outage risk. Duke Energy is using hybrid AI systems to monitor transformer health and identify high-risk assets more consistently. 

Utilities are also applying AI to grid planning and operations. Microsoft and its partners are helping utilities use AI-powered simulations and digital twins to move from static planning studies to dynamic, data-driven forecasting. At Georgia Power, digital twins allow teams to virtually simulate infrastructure, support workforce training, and plan upgrades more safely and efficiently.

Shell Refining Predictive Maintenance with AI

Shell has built one of the most advanced AI-driven predictive maintenance programs in the oil and gas industry. The company collects approximately 20 billion sensor data points every week from more than 3 million sensors across its global assets. These sensors monitor vibration, temperature, pressure, and flow rates across critical equipment.

Shell’s AI platform uses over 11,000 machine-learning models to analyze this data and generate more than 15 million predictions daily, identifying early signs of equipment degradation. This allows Shell to proactively schedule maintenance before failures occur, which reduces unplanned downtime, improves safety, and lowers maintenance costs.

By monitoring roughly 10,000 critical pieces of equipment, Shell has demonstrated how AI can move maintenance from reactive to predictive—delivering measurable improvements in reliability and operational efficiency.

4. ExxonMobil Optimizing Production and Inspections with AI

ExxonMobil is applying AI and machine learning across upstream production, facility inspections, and digital transformation initiatives. In the Bakken, ExxonMobil deployed an automated machine-learning workflow to optimize gas lift operations across hundreds of wells. The system forecasts production weekly and determines optimal gas injection rates, resulting in average production increases of more than 5%, with some wells achieving even higher gains.

At its Baton Rouge petrochemical complex and other facilities, ExxonMobil is also using robotics, AI-assisted inspections, and 3D data. Robotic crawlers and robotic dogs perform inspections in hazardous areas, capturing laser scans and operational data that are being integrated into digital twins.

ExxonMobil teams are also exploring secure applications of large language models and AI-driven tools to support engineers, improve decision-making, and preserve institutional knowledge.

BHP Applying AI to Mining Safety, Discovery, and Productivity

Mining giant BHP sees significant long-term potential for AI, particularly in resource discovery, equipment monitoring, and operational safety. With more than a century of geological data, BHP is using AI to analyze historical drilling records and geological information to streamline discovery.

AI is also being applied to improve safety and reduce costly failures. One example is using AI-enhanced computer vision on simple camera feeds covering miles of conveyors. These systems can detect early signs of mechanical issues to prevent catastrophic failures, reduce downtime, and improve worker safety.

While BHP acknowledges that AI adoption is still in its early stages, the company sees strong potential for scalable, cost-effective applications that support productivity and safety across its global mining operations.

AI Is Becoming Core Industrial Infrastructure

Across energy, utilities, oil and gas, mining, and other industrial applications, AI is becoming part of the day-to-day. Companies like bp, Shell, ExxonMobil, Duke Energy, Georgia Power, other utilities, and BHP are using AI to move faster, reduce risk, improve safety, and make better decisions at scale.

What these use cases have in common is a practical focus on value: starting with data-rich, operationally critical problems and deploying AI where it can deliver measurable impact. As industrial organizations continue modernizing their systems and building trust in AI-driven insights, these early implementations offer a clear blueprint for what’s possible next.