Artificial intelligence is being tested as a means to improve both the resilience of energy infrastructure and the environmental, social and governance (ESG) performance of oil and gas companies. In a recent eight‑week innovation sprint, the Abu Dhabi National Oil Company (ADNOC) worked with Bain & Company and the World Economic Forum under the Leaders for a Sustainable MENA initiative to develop two AI‑enabled prototypes.

The first prototype, the Resilience Intelligence Suite (RIS), uses geospatial data on more than ten climate hazard types—rainfall, wind, heat, and others—to assess operational risk under evolving climate scenarios. RIS evaluates potential losses across interconnected infrastructure and recommends adaptation measures that are ranked by cost and return on investment. The design emphasises a system‑level view, recognising that disruptions can cascade through a network of assets in ways that traditional, asset‑by‑asset tools miss. User testing with ADNOC’s climate teams confirmed that RIS consolidates risk information across assets, giving clearer visibility into how disruptions could propagate and which resilience investments should be prioritised.

The second prototype, the Sustainability Performance Navigator (SPN), applies generative AI to interpret sustainability disclosures and benchmark performance against leading ESG rating frameworks. Within ADNOC’s multi‑subsidiary structure, SPN maps performance against rating criteria, identifies material gaps and translates findings into prioritised action plans for both the enterprise and individual subsidiaries. Testing showed that SPN reduced manual benchmarking effort and improved coordination across business units. Executive adoption was driven by the quality of the analytical outputs and the traceability between data inputs, rating criteria and recommendations.

These prototypes illustrate how AI can move beyond operational efficiency to support structured, forward‑looking decision‑making on climate risk and sustainability performance. The International Energy Agency estimates that AI could unlock up to $550 billion in operations and maintenance cost savings by 2030 and free up 175 gigawatts of existing transmission capacity, accelerating the integration of renewable sources and easing grid congestion.

The insights from ADNOC’s sprint provide a blueprint for the broader MENA energy sector. AI‑enabled platforms such as RIS can help companies understand portfolio‑level risk exposure and allocate resources toward the most effective resilience measures. Tools like SPN can support stronger alignment with external ESG expectations, potentially improving market perceptions and long‑term value creation.

Scaling these solutions will require continued collaboration among companies, technology providers and sustainability specialists, as well as stronger data integration and governance frameworks. While AI’s application to sustainability and resilience in the energy sector remains early, the potential is clear. The RIS and SPN prototypes show that focused experimentation can translate emerging AI capabilities into practical, decision‑ready tools.

ADNOC’s experience demonstrates that realizing this potential requires investment in innovation and data integration and highlights the importance of strong governance and cross‑disciplinary collaboration. With these requirements met, AI‑enabled tools can play an increasingly important role in building energy systems that are not only more efficient but also more resilient and sustainable, for the region and for the world.