Data and CO 2 reduction are key success metrics for AI within the energy transition Figure 5 Companies have deployed AI- powered systems that combine advanced sensors, computer vision, and deep learning to monitor large operational areas for methane and other greenhouse gas emissions. These systems can identify leaks and emission sources quickly, supporting both regulatory compliance and proactive environmental stewardship. A key application of AI is in flare management. By integrating AI with real-time video feeds and sensor data, companies can continuously assess combustion efficiency and detect flare events as they happen. This allows for immediate adjustments to minimize emissions and optimize operational performance. ADNOC, for example, uses AI-driven flare monitoring to provide reliable, real-time data that supports emission control strategies and aligns with environmental regulations, ultimately reducing emissions in a cost-effective manner. 16 Beyond detection and operational optimization, AI is playing a growing role in supporting carbon capture and storage (CCS) initiatives. AI algorithms can help identify new materials for more efficient CO capture, simulate storage conditions, and optimize injection rates and site selection. These capabilities are essential for scaling CCS projects and decarbonizing hard-to-abate sectors, such as heavy industry and hydrogen production. By integrating AI across emissions detection, monitoring, and operational optimization, energy companies are setting new benchmarks for digital innovation in sustainability. 4 AI for emissions management becomes mainstream Energy companies are increasingly turning to Artificial Intelligence to enhance their emissions management strategies. AI enables organizations to move beyond traditional, manual monitoring by providing real-time, high-accuracy detection and rapid response capabilities. Grid Stability by Audience: Business Leaders: 26% Business Decision Makers:12% Most Important Metric for Assessing AI's Success in the Energy Transition Data Utilization/Accuracy of Predictions CO 2 Reduction Grid Stability/Reliability Cost Savings Transparency and Accountability Speed of Deployment 25% 19% 19% 16% 11% 9% Key Shifts from 2024 to 2025 Greater emphasis on AI for emissions control: more companies (and regulators) prioritized AI-based emissions monitoring especially methane whereas in 2024 it was an emerging topic. New tools launched in the past year (e.g. AI-enabled satellite methane trackers) and stronger industry commitments have made emissions-focused AI solutions mainstream rather than experimental. Watch For in 2026 Regulatory mandates for AI- based methane monitoring. Keep an eye on government or industry standards that require AI in leak detection and reporting. AI applications expanding into carbon capture optimization and real- time corporate carbon accounting. 2025 SURVEY INSIGHT AI is a tremendous enabler for sustainability. It helps customers use less energy, shift to greener sources, and optimize operations. The time to scale AI for energy transition is now - the technology is ready, and the impact is real. Philippe Rambach Chief AI Officer, Schneider Electric 16 ADNOC, https://adnocgas.ae/en/sustainability/environment Our industry has been using AI techniques to model the physical world for many years. From resolving the structure of the subsurface through seismic and well log data to the behavior of artificial lift systems, AI continues to provide new insights that unlock greater operational efficiencies and lower costs that translate into tangible business value. Oxy is expanding our use of AI to address emissions and ensure we can deliver lower carbon intensity oil and gas the world needs through direct air capture and enhanced oil recovery. Vicki Hollub CEO, Occidental Petroleum 29 Powering Possible 2025 28