AI in the oil and gas industry: Use cases, benefits, and challenges (2026 update)

Updated: October 15, 2026

Oil and gas companies have to contend with both harsh operating environments and highly complex engineering and production systems. They face a wide range of challenges, from managing safety risks and maintaining critical assets to coordinating intricate supply chains and making sense of vast amounts of operational data.

The industry has long used established forms of AI, particularly machine learning, to help manage these demands. Applications include:

• Seismic interpretation and reservoir analysis
• Drilling and production optimization
• Anomaly detection, predictive maintenance
• Computer-vision-based inspection and monitoring

The new wave of generative and agentic AI is expanding what oil and gas companies can do with AI. In this article, we explore how these capabilities are being applied across the industry, the value they can create, and the challenges involved in deploying them.

How are generative and agentic AI changing oil and gas?

Unlike the predictive and analytical AI that oil and gas companies have used for years, generative AI is designed for the open-ended creation and transformation of content such as text and images. When connected to internal systems, it can let workers use natural language to query company knowledge, operational data, and the outputs of existing analytical systems. Agentic AI goes further by enabling AI systems to plan and carry out multi-step tasks, using tools and other systems as needed.

Together, these capabilities are expanding existing AI use cases in the oil and gas industry, as well as enabling entirely new ones.

Generative and agentic AI use cases in oil and gas

Improving worker safety and incident response

Oil and gas field work often involves heavy machinery, high-pressure equipment, flammable or toxic substances, extreme weather, and other serious hazards. In the US alone, 65 fatal occupational injuries were recorded across private-sector oil and gas extraction operations in 2024. Generative AI can help workers assess these risks and respond to incidents by giving them faster access to the information they need.

For example, a field worker can use natural language to describe an incident or potential hazard to an AI assistant. The assistant can then draw on sources such as historical incident records, safety procedures, relevant regulations, and current field conditions to provide situation-specific guidance.

One of our customers is already using AI this way. When a technician arrives at a chemical spill, they tell their AI assistant their location and the chemical involved. The assistant then provides relevant steps for responding safely.

Enhancing predictive maintenance and asset reliability

Oil and gas operations rely on critical assets where even a small fault can lead to costly downtime, environmental damage, or safety risks, which is why it’s so important to identify problems before they lead to failure.

The industry already uses predictive maintenance systems that rely on machine learning to analyze sensor data and flag equipment at risk of failure. Some systems can also diagnose likely causes and recommend maintenance actions. Engineers can use generative AI alongside these systems to query relevant information, such as maintenance logs, inspection reports, equipment manuals, and parts records. Agentic AI can go further by carrying an investigation through multiple steps without needing a new prompt for each one.

For example, if a predictive maintenance system flags abnormal vibration in a pump, an engineer could use an AI assistant to ask whether the same pattern has occurred before. Based on the answer, they could then ask an AI agent to investigate further. The agent might review what maintenance was performed previously, check recent operating data and parts availability, and suggest next steps for the engineer to review.

Preserving and sharing operational knowledge

Over time, oil and gas companies build a great deal of operational knowledge about particular sites, assets, and processes. But that knowledge can be scattered across the organization or concentrated among experienced employees, making it difficult to preserve and pass on.

Generative AI can help make documented lessons from past operations easier to reuse, giving newer or less experienced workers better access to knowledge that might otherwise remain buried in old records or depend on knowing the right person to ask.

For example, companies could use generative AI grounded in their existing knowledge systems to pull together lessons from sources such as field reports, post-job reviews, and operator logs. This can help workers learn from similar past situations and avoid starting from scratch each time.

Accelerating engineering and operational analysis

Engineering and operational analysis help oil and gas teams understand conditions across their assets and operations, forecast performance, and evaluate possible changes before implementing them.

For example, reservoir engineers work with numerical models built from geological and other subsurface data, then calibrate them so simulated production rates and pressures match historical field observations. They can use these models to simulate how the reservoir might respond to different field development options, such as changes in well placement or injection strategy. Generative AI can reduce some of the manual work involved by helping engineers prepare and edit the input files for reservoir simulators. An agentic system could extend that workflow by setting up and analyzing multiple development scenarios, comparing the resulting production forecasts, and summarizing the results for an engineer to assess.

Automating administrative workflows

In addition to field activities, oil and gas operations involve a lot of administrative work, such as completing reports, processing requests, scheduling activities, and arranging resources. Generative AI can help with many of these tasks, including turning field notes and transcribed voice updates into draft reports or handover summaries for workers to review.

Agentic AI can also help with logistics and scheduling. For example, in marine logistics, an agentic system could use information such as a vessel’s size, expected arrival time, berth availability, and weather conditions to identify a suitable docking slot. The marine operations manager could then review and approve the recommendation before the agent makes the booking in the appropriate system.

What are the challenges of deploying AI in oil and gas?

Implementing generative and agentic AI presents some key challenges for oil and gas companies. Some of the main ones include:

Integrating AI with operational data and systems

Oil and gas companies typically have data distributed across legacy systems and specialized applications. Field and process-control networks are also commonly segmented from corporate IT systems for safety, reliability, and security purposes. Some AI applications need data from both environments, so these data silos and system boundaries can make integration difficult.

Companies can make the relevant data available to AI through controlled interfaces or gateways that permit only approved data flows. This can give AI applications access to what they need without removing the boundaries between the underlying systems.

Choosing the right AI deployment model

Oil and gas companies need to decide where and how their AI systems run. Private deployments can keep models, data, and processing within a controlled environment, making it easier to enforce access restrictions, maintain audit logs, and preserve clear boundaries around operational systems. This can be especially important for use cases involving sensitive data or critical infrastructure.

Regulatory and data-sovereignty requirements are another consideration. They can affect where data and models are hosted, who has administrative access to the underlying systems, and which jurisdiction governs them.

The operating environment also shapes deployment choices. Offshore platforms, remote production sites, and pipeline facilities may have limited or intermittent connectivity. Running suitable models at the edge can keep AI capabilities available when cloud connectivity is unreliable, while also reducing the amount of sensitive data that needs to leave the site.

Ensuring reliable outputs and appropriate human oversight

AI errors can have serious consequences in oil and gas. Grounding AI in trusted internal sources can reduce some risks, but it does not guarantee correct answers, particularly if the source information is incomplete or outdated. In agentic AI systems, errors can also carry through into actions.

Relevant safeguards can include:

• Testing and monitoring AI systems
• Keeping source information current
• Limiting what agents are allowed to do
• Requiring human review or approval for higher-consequence actions

Further reading: AI governance challenges: How to support safe, scalable AI adoption

Moving from pilots to routine use

A successful pilot can show that an AI system can work in a controlled setting without proving that it will fit people’s actual ways of working or deliver enough value at scale.

According to McKinsey’s 2026 oilfield services and equipment (OFSE) report, fewer than 25% of companies represented in its survey had moved beyond the pilot stage with generative AI. Only 1% said their company had achieved significant scale. The report identified unclear strategies, workforce resistance, difficulty quantifying ROI, and the cost of moving from pilot to production as barriers to scaling.

Technical feasibility is therefore only part of what it takes to move AI into production. Companies also need a clear business case for adoption, defined ownership and funding, and, in certain cases, a redesign of existing workflows so the technology has a clear role in day-to-day work.

Final thoughts

As AI adoption matures across the oil and gas industry, the focus is shifting from experimenting with new use cases to selectively scaling those that offer the most operational and competitive value.

Of course, successfully deploying AI in production requires overcoming significant challenges. But that work should not be viewed as a mere cost. Integrating AI with proprietary operational data, internal expertise, and company-specific ways of working can itself become a source of differentiation — one that competitors cannot replicate by simply adopting the same underlying technology.