Assess and Prioritize Agentic AI Use Cases in Oil and Gas

Align autonomy with value, risk tolerance, and viable domain to maximize impact.

Analyst perspective

The industry won’t wait – it’s time to get involved.

Agentic AI is a rapidly expanding frontier within the broader landscape of AI development. As AI competencies become more advanced, mature organizations can deploy increasingly robust multi-agent systems that perform not just individual tasks, but entire workflows of related activities with minimal human guidance. Oil and Gas leaders can no doubt appreciate the potential for efficiency and productivity gains such systems provide but are right to be cautious about the suitability of highly autonomous systems within their technology environment. Some capabilities are too operationally complicated, require too much regulatory oversight, and have a risk tolerance too low for easy AI implementation.

This is not, however, a strong enough reason to remain in a holding pattern. The longer you operate in catch-up mode to industry peers, the bigger the gap will become in business outcomes. With the correct framework for understanding both the needs of your organization and the landscape of agentic AI use cases, it is possible to find a fit that will yield tangible results without overstepping your capabilities or tolerance. Starting tomorrow might be comfortable, but starting today is practical.

Evan Garland
Senior Research Analyst, Industry Practice
Info-Tech Research Group

Executive summary

Your Challenge

• Agentic AI has the potential to yield cost and efficiency improvements in the oil and gas industry but is difficult to implement amid structural boundaries.
• As AI use cases get closer to operational environments, risk tolerance decreases, and the value and viability must increase to counterbalance.
• Determining the optimal level of agentic AI autonomy, based on your organization’s needs and maturity, further complicates the decision process.

Common Obstacles

• Cost-center domains where technology has the highest potential value are often tightly coupled with OT, creating ceilings for automation potential.
• Data and process fragmentation across OT, engineering, and enterprise systems makes explainability and auditability difficult to achieve.
• Realizing the full benefits of agentic AI often requires complex multi-agent architectures with persistent memory and learning loops, raising technical and governance barriers to early deployment.

Info-Tech’s Approach

• Align agentic AI evaluation with organizational business drivers and key operational capabilities to the visibility and magnitude of benefits realized.
• Establish autonomy ceilings and agentic role fit for each capability domain, identifying where regulatory operational constraints limit defensible autonomous decision-making, and what activities are most readily supported by AI.
• Evaluate use cases using a structured value-vs.-viability framework, allowing organizations to prioritize initiatives where agent behavior is both operationally useful and risk-appropriate.

Info-Tech Insight

Oil and gas organizations should prioritize agentic AI to strengthen production, operational efficiency, and commercial performance, while establishing boundaries to maintain auditability and alignment with safety, OT, and regulatory constraints.

Your challenge

Implement agentic AI through initiatives that strengthen operational performance without compromising safety or control.

• Finding the right starting point is difficult. Agentic AI can improve efficiency across the oil and gas value chain, from maintenance coordination to production support and compliance work. But leaders still need a defensible way to determine where autonomy fits operational reality and where it creates more risk than value.
• Risk tolerance drops as AI moves closer to operations. Use cases that interact with field processes, production workflows, or operational decisions carry far greater consequences than back-office automation. In these environments, organizations must justify not only the value of AI, but also why its level of autonomy is safe, bounded, and appropriate.
• Your organizational reality impacts your ideal portfolio. Some opportunities appear valuable on paper but are difficult to implement because of weak data, fragmented systems, limited governance, or low organizational readiness. Selecting the right initiatives requires balancing business impact with technical feasibility and the context of your specific organization.

(Sources: 1 Dataiku, 2026; 2 IBM, 2025)

74% — of CIOs state that their role could be at risk if AI can’t deliver measurable business gains in the next two years (Source: Dataiku, 2026)

85% — of CIOs report that traceability or explainability gaps have delayed or stopped AI projects from full deployment (Source: Dataiku, 2026)

29% Upstream
23% Midstream — Percent of organizations at least piloting agentic AI for upstream and midstream operations (Source: IBM, 2025)

Common obstacles

Structural barriers limit the speed at which agentic AI can be safely adopted in energy operations:

• High-value domains are often tightly coupled with OT. Many of the most attractive use cases sit close to industrial systems where safety, reliability, and process discipline limit how much autonomy can be introduced. This creates hard ceilings on what agents can do in operational environments.
• Fragmented data limits trust in AI outputs. Operational, engineering, and enterprise systems are often disconnected, making it difficult for agents to assemble the context needed for reliable recommendations or auditable actions.
• More powerful architectures are harder to deploy. Advanced agentic systems can coordinate tasks, retain context, and work across multiple steps, but they also introduce more integration, oversight, and governance complexity. This multiplier effect can make planning difficult, especially when attempting to demonstrate value and feasibility in the short term.

54% of CIOs have discovered unsanctioned AI use for tasks or projects
3 in 10 CIOs have been asked in the past 12 months to justify an AI outcome they could not fully explain (Source: Dataiku, 2026)

Agentic AI Use Cases for the Oil and Gas Industry

Oil and gas organizations need a defensible way to identify where agentic AI can create operational value without introducing unacceptable safety, regulatory, or financial risk.

Oil and gas organizations should prioritize agentic AI to strengthen efficiency and performance while establishing boundaries to maintain auditability and alignment with constraints.

Insight summary

Valuable autonomy must also be defensible autonomy.

Oil and gas organizations should prioritize agentic AI to strengthen production, operational efficiency, and commercial performance, while establishing boundaries to maintain auditability and alignment with safety, OT, and regulatory constraints.

Autonomy is constrained by operational complexity and domain risk.

As agents take on broader coordination and decision-making roles, increasing system and workflow complexity raises governance demands and limits where higher autonomy can be safely applied. In oil and gas organizations, these limits are most common around core OT operations.

Tune agent capabilities to match risk and readiness.

Agentic systems can be strengthened through memory, learning, and perception, but added capability increases complexity and risk. Enable these elements only where use cases are stable, and consider simplification where possible to minimize pilot and implementation complexity during early adoption.

Start with controlled use cases, then expand scope over time.

Early value in oil and gas comes from applying agents to well-defined activities such as maintenance coordination, compliance reporting, and production planning. As organizations gain confidence and control, agents can extend across adjacent workflows and systems to enable broader coordination.

Specialization reduces early complexity and improves control.

Align agents to specific roles such as monitoring, coordination, or planning, rather than building general-purpose systems. Combine them later as governance and integration maturity improves.

Info-Tech’s methodology for assessing agentic AI use cases in Oil and Gas

Research deliverable

Each step of this research is accompanied by supporting deliverables to help you accomplish your goals:

Agentic AI Use Case Tool for Oil and Gas

Provides the structure to identify and list agentic AI use cases, determine appropriate autonomy levels and behaviors, score each one across business value and feasibility, and build a prioritized portfolio.

Upon completion, organizations can confidently select the highest-impact opportunities ready to move into the Build Your Agentic AI Prototype phase.

Measure the value of this blueprint

Leverage this blueprint’s approach to ensure your AI use cases align with and support your key business drivers and speed time to value.

Case study

INDUSTRY Segment: Upstream | SOURCE: Boston Consulting Group

Challenge

Oil and Gas operators often struggle to identify, quantify, and prioritize methane-abatement opportunities across large asset footprints. Fragmented field data and manual leak-detection-and-repair workflows make it difficult to move from baseline measurement to coordinated action at scale.

Solution

An on-premises data-agnostic platform (called Methane.AI) combined AI-enabled detection inputs, emissions estimation, forecasting, reporting, and coordination of leak detection and repair activities. The approach used multi-source field data and predictive modeling to estimate equipment-specific emissions and support geo-localized abatement planning.

Results

›8,000 potential emission sources assessed

›700 emission sources identified, measured, and digitized

~100 million m³ natural gas abated

~30% emissions-reduction impact and ~$20M expected value from the abatement program

Info-Tech offers various levels of support to best suit your needs

Guided Implementation

A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.

A typical GI is 6 to 8 calls over the course of 2 to 4 months.

What does a typical GI on this topic look like?

Workshop overview

Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889

Assess and Prioritize Agentic AI Use Cases in Oil and Gas

Phase 1

Identify Use Cases That Align to Your Drivers and Capabilities

This phase will walk you through the following activities:

• Identifying areas of your business where agentic ai is aligned with drivers and goals
• Exploring preliminary use cases that fit with organizational needs

This phase involves the following participants:

• AI initiative lead
• CIO
• Other IT leadership
• Senior business executives and managers accountable for AI initiatives

AI is an innovation in machine learning

Artificial Intelligence (AI)

A system that can make predictions, recommendations, or decisions influencing real or virtual environments.

Machine Learning (ML)

A subset of AI algorithms that parse data, learn from data, and then decide or make predictions.

Generative AI (Gen AI)

A subset of artificial intelligence systems that generate new outputs based on the data the system has been trained on using modalities such as text, audio, visual, and code.

What makes AI different

Traditional programming

Machine learning

Augmented LLMs are the foundation for autonomization

Adding memory, tools, and data access transforms LLMs from simple chatbots into the building blocks of autonomous systems.

Augmented LLM

This LLM is enhanced with external capabilities but still works in a linear, input to output flow. The LLM only acts when prompted and its workflow is a single-pass interaction.

Process

• Input is passed into the LLM.
• The LLM can call retrieval, tools, and memory as needed to enrich its response.
• Output is generated and sent back.

Augmented LLM

This LLM becomes part of a closed-loop system that allows autonomous decision-making and continuous action without constant human prompting.

Process

• Input still flows into the LLM.
• The LLM now also interacts with decision and observation processing modules.
• These modules enable the system to evaluate results, adapt to environmental feedback, and iterate through multiple cycles before producing final output.

Spectrum of automation and AI

From rules to agents

Map AI to business value

When thinking about Agentic AI, it is important to consider the business capabilities, systems, and existing AI that already support the organization. Together, these can be understood through three connected layers:

• Layer 1 – The "What“ – Business Capability Map

  The complete inventory of what the business does, organized into a stable, technology-agnostic hierarchy of capabilities. This is the business framework.

• Layer 2 – The "How“– AI Types and Agentic Capabilities

  Within each business domain, specialized AI types handle specific tasks. Agentic AI orchestrates them into autonomous workflows that operate across multiple systems. The appropriate design of these workflows is shaped by risk tolerance and the level of autonomy within which AI assists, recommends, or acts given operational and regulatory boundaries. This is the intelligence framework.

Every business problem, pain point, and opportunity in this research will map to a capability, giving the team a shared way to locate, prioritize, and evaluate where agentic AI can deliver the greatest value.

Start with controlled use cases, then expand scope over time.

Early value in oil and gas comes from applying agents to well-defined activities such as maintenance coordination, compliance reporting, and production planning. As organizations gain confidence and control, agents can extend across adjacent workflows and systems to enable broader coordination.

Download the Agentic AI Capability Map Templates for Oil and Gas

Agentic AI use cases must drive value, which in oil and gas can be mapped to the primary value drivers

Sources of Value

• Operations Efficiency

  Optimizing cost, cycle time, and resource utilization across operations through improved planning, execution, and coordination of assets, workflows, and data.

• Production Growth

  Increasing output, throughput, and revenue by improving asset performance, reducing constraints, and enabling more consistent and scalable operations across the value chain.

• Health and Safety

  Reducing workforce exposure to hazardous conditions and improving incident prevention, detection, and response, for both safety and compliance purposes.

• Risk & Compliance

  Mitigating operational, regulatory, and financial risks while ensuring adherence to safety, environmental, and operational standards to maintain continuity and avoid disruption.

• Environment, Social & Governance (ESG)

  Improving environmental performance, transparency, and governance practices while supporting emissions reduction, reporting, and stakeholder accountability.