Oil and gas companies are adopting increasingly capable AI tools at the same time experienced engineers, operators, and subject matter experts (SMEs) are retiring. Much of what these employees know — how equipment behaves under different operating conditions, which process deviations warrant attention, and how previous issues were resolved — isn’t captured in formal procedures or reports. So when they leave, that knowledge can leave with them.

Human-in-the-loop (HITL) AI offers a way to preserve that expertise while keeping employees involved as AI agents take on more analysis and decision support. By capturing the context and reasoning employees contribute during their work, HITL AI can make valuable operational knowledge available to the teams that follow.

Preserve Operational Expertise as Industrial AI Scales

Oil and gas operations generate extensive process and equipment data, but interpreting that data often requires knowledge built through years of working with specific assets and operating conditions.

For example, an AI system might flag increased compressor vibration as an anomaly, while an experienced engineer knows the unit recently restarted and recognizes the pattern from previous startups. AI surfaces the signal quickly, but the engineer provides the context needed to determine whether it warrants further investigation.

HITL AI brings those strengths together by matching human involvement to the needs and risks of the workflow. Routine analysis can move forward with less oversight, with agents bringing engineers in when findings require domain expertise or could lead to consequential operational decisions.

With the right technology in place, organizations can also capture the knowledge that informs these decisions before it leaves with retiring employees. Preserving that expertise helps companies build what Seeq calls Decision Capital: accumulated operational knowledge, context, analytics, reasoning, and action history.

Decision Capital becomes especially valuable when similar issues emerge across wells, equipment, or facilities. An engineer investigating recurring equipment behavior could draw on how another team diagnosed comparable conditions, including the data they reviewed and why they chose a particular response. Instead of recreating the investigation, the engineer can build on proven knowledge while accounting for the conditions of the specific asset.

As industrial AI becomes more capable, its value isn’t in removing people from the process. It’s in extending the reach of human expertise and making that knowledge available to the workforce that follows.

Read more to learn how HITL AI can help preserve the expertise behind critical equipment and process decisions. Or if you’re ready to put that approach into practice, schedule a demo to learn how Seeq can help make operational knowledge accessible across assets, facilities, and teams.

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