AI Agents Usher in a New Era of Energy Forecasting

Middle East energy firms turn to AI agents and platforms from SLB, Microsoft, AIQ, and ADNOC to sharpen planning and manage uncertainty

5 Feb 2026

A shift is under way in the Middle East’s energy sector that is less about oil prices or geopolitics than about how producers plan for the future.

Artificial intelligence has moved from pilot projects into daily operations, as energy companies deploy AI agents and automated workflows to improve forecasting, field development planning and risk management. The change reflects a push to cope with uncertainty in markets where long term investment decisions remain costly and complex.

Rather than being driven by large acquisitions, the momentum is coming from targeted platforms and partnerships. These are reshaping how reservoirs are evaluated and how long term strategies are formed, with implications across the energy value chain from upstream production to services and technology providers.

A central appeal of AI systems is speed. Traditional planning models were often built around a single forecast updated periodically. AI agents allow teams to run multiple scenarios in parallel, drawing on large datasets, testing assumptions and revising models as conditions change. Generative AI supports parts of this process, but companies say it sits within broader systems designed for continuous planning rather than one off analysis.

Oilfield service companies are moving quickly to adapt. SLB has expanded its digital subsurface offering through collaborations with AIQ and Abu Dhabi National Oil Company, embedding AI tools that help engineers compare development options and refine field plans. The company says the objective is to reduce manual work and shorten the time between analysis and decision making, rather than to replace engineering expertise.

Technology partnerships are reinforcing the trend. In 2025, Microsoft and ADNOC widened an existing alliance to scale cloud based AI platforms across the national oil company’s operations. The emphasis has been on secure, enterprise grade systems that can support forecasting and planning at scale, signalling that AI is becoming core infrastructure rather than an experimental add on.

National oil companies are giving additional weight to the transition. By linking AI adoption to efficiency and growth targets, ADNOC has signalled to suppliers and partners that advanced forecasting capabilities are now a basic requirement.

Challenges remain, particularly around data quality, governance and transparency. As AI agents take on larger roles in planning, trust in their outputs will be critical. Even so, the direction of travel is clear. Advanced AI tools are becoming a competitive asset, helping the region’s energy producers plan with greater speed and confidence in an uncertain environment.