The Quiet Crisis in Oil & Gas: When Experience Walks Out the Door 

The oil and gas industry does not have a hiring problem. It has a judgment problem. 

Across operations, the most valuable asset walking out the door is experience rather than production capacity. 

According to this year’s survey of 9,000+ energy professionals across 143 countries, 48% of the workforce is now over 45, and nearly half is expected to retire within the next decade as fewer younger workers enter the industry.  

The knowledge gap is the real risk 

In oil and gas, critical knowledge is rarely documented. It lives in the instincts of operators, technicians, and engineers, the subtle signals that prevent failures, optimize uptime, and protect safety. When that knowledge leaves, it does not transition cleanly. It disappears. 

Operators are already seeing the consequences. In mature assets, missing records and fragmented data can delay projects by months or years as teams try to reconstruct what was once understood. 

Across refining, offshore, and LNG operations, fewer experienced workers are being asked to manage increasingly complex systems while carrying less historical context. This reflects more than a workforce shortage. It signals a breakdown in knowledge transfer. 

AI as the bridge, not the replacement 

The industry cannot hire its way out of this challenge. It must scale expertise. That’s where industrial AI is becoming critical, serving as an amplifier of judgment rather than a replacement for people. 

When embedded directly into workflows, AI can capture patterns across sensor data, maintenance logs, and operational history, then deliver real-time guidance to frontline workers. 

The impact is already visible in early deployments. McKinsey estimates that generative AI and other AI applications could unlock USD$390–550 billion in additional value across the energy and materials sectors in the coming years, with oil and gas capturing a substantial share through higher productivity, greater reliability, and better decision‑making.

Success, however, depends on execution. AI must live where the work happens, in the field, in control systems, and inside daily workflows, instead of standalone dashboards. 

The companies that lead the next decade will be those that capture what their experts know before it disappears and put that knowledge into the hands of the next generation.