Artificial intelligence in the energy sector is undergoing a fundamental transformation. Rather than relying solely on traditional generative tools and copilots to assist with isolated tasks, the industry is looking toward agentic AI—autonomous systems designed to plan, coordinate complex workflows, and take targeted actions under varying degrees of human supervision.

This technological shift carries major implications for the management and execution of oil and gas operations. In an episode of the Oil & Gas Journal ReEnterprised podcast, host Chris Smith explored these developments with Gino Hernandez, head of global digital business at ABB Energy Industries, discussing how autonomous systems are positioned to reshape the sector.

Moving Beyond Basic Copilots

For several years, digital transformation in oil and gas has heavily featured assistive AI technologies that summarize data, draft reports, or answer routine queries. Agentic AI represents a distinct leap forward. Instead of simply advising a human operator, agentic systems are engineered to handle multi-step processes.

According to Hernandez, the industry is poised to transition from AI that merely assists in single tasks toward fully coordinated systems capable of managing entire workflows. These advanced models can evaluate operational data, formulate plans, and execute decisions within strictly defined parameters, fundamentally altering daily operational routines.

Balancing Autonomy, Risk, and Governance

Introducing greater machine autonomy into critical infrastructure requires careful navigation. Oil and gas companies face the challenge of determining precisely where higher levels of AI independence generate real economic and operational value without introducing unacceptable levels of risk.

A successful deployment relies heavily on robust frameworks surrounding:

  • Governance: Establishing clear operational boundaries and rules for automated actions.
  • Accountability: Maintaining transparent lines of responsibility when AI systems execute coordinated workflows.
  • Human Judgment: Ensuring that human oversight remains central, particularly for critical high-stakes interventions.

Future Implications for Operations

As energy firms evaluate these technologies, the focus shifts toward safe, scalable implementation. Successfully integrating agentic AI will demand rigorous testing, clear safety guardrails, and a deep alignment between digital capability and operational expertise. By striking the right balance between automation and human supervision, oil and gas operators can unlock new efficiencies while maintaining strict control over safety and reliability.