AI Insights: IFS’s Hannes Liebe 

Energy security and operational efficiency have moved from strategic considerations to immediate utility priorities. Recent geopolitical instability has exerted significant pressure on global energy markets with Brent crude, the international benchmark, climbing more than five percent to hit $107 per barrel, while West Texas Intermediate (WTI) jumped to $94 per barrel. During such challenging times, organisations that will thrive in this environment are those that can make faster decisions, optimise constrained resources, and balance supply and demand despite external shocks. 

Industrial AI is proving to be the difference between reactive crisis management and proactive operational control. 

The pressure points 

Rising energy costs affect every link in the value chain. When crude oil prices spike, refineries face compressed margins. When supply becomes uncertain, field operators must extract maximum value from existing assets. At the same time, aging infrastructure demands more maintenance, production complexity increases, and the workforce capable of managing these systems approaches retirement age. 

Traditional systems were built for a world that no longer exists. Manual scheduling cannot optimise field crews across hundreds of wells when conditions change hour by hour. Reactive maintenance cannot prevent unplanned outages that cost millions of lost productions. The gap between what energy companies need and what their systems can deliver is widening. Industrial AI bridges that gap. 

Operational efficiency through intelligence 

Energy security refers to the ability to maintain a steady supply independent of forces outside of your control. Industrial AI supports this independence by embedding decision-making capability directly into operational workflows. 

Agentic AI and digital workers automate routine processes, from monitoring equipment health to coordinating maintenance schedules to optimising production parameters. These agents do not require constant human oversight. They operate autonomously within defined parameters, escalating only when human judgment is required. 

This level of automation becomes critical when workforce constraints meet operational complexity. As experienced employees retire, AI-augmented systems help newer workers access institutional knowledge and make informed decisions faster. 

From data to decisions 

Industrial AI does not replace experienced operators. It amplifies their capability. By ingesting real-time data from sensors, equipment, and external systems, AI creates a continuous optimisation layer that adjusts how assets run, how crews deploy, and how resources allocate. 

The impact shows up in three core areas that directly affect energy security and operational resilience: 

• Predictive maintenance reduces unplanned outages and extends asset life. AI analyses performance patterns and predicts failures in advance, replacing reactive fixes and routine, schedule‑based maintenance. This matters most when spare capacity is limited and every barrel counts. Companies like Total Energies and Noble Corporation have seen measurable improvements in uptime by deploying these capabilities. 

• AI-driven scheduling and resource orchestration optimises field service operations. IFS customers have seen an average 37.1 percent reduction in total travel distance for field operations. That translates directly into lower fuel costs, reduced emissions, and faster response times. When crews can reach critical sites faster and complete more work per shift, costs stay under control. 

• Unified asset lifecycle management for better capital and risk decisions. The IFS Asset Lifecycle Management (ALM) solution brings together AIP, EAM/APM, FSM, and ERP into one connected framework. This gives leaders a clear view of where risk, cost, and performance intersect, helping them schedule maintenance outages, brownfield turnarounds, and capital projects with data‑backed precision. In volatile environments, that linkage between strategy and execution is a resilience multiplier. 

AI has moved from pilot projects to production deployment across the world’s largest energy enterprises. The organisations leading this transition share a common characteristic: they view AI not as a feature set but as an operational backbone. They are standardising AI workflows, validating and trusting AI-driven recommendations, and measuring impact in terms of uptime, productivity, and resilience. 

Rising prices and geopolitical uncertainty are not temporary challenges to be weathered. They are the new operating environment. Industrial AI gives energy companies the tools to maintain operational control, protect margins, and deliver reliable production regardless.