Use cases by sector (continued) TABLE 4 Moeve: “Green AI” Situation/context Approach Results Moeve uses ML and generative AI to accelerate the energy transition and empower employees and customers.They monitor and optimize the carbon footprint of ML and generative AI models. They then support the generative AI factory to develop fast, secure use cases, ensuring efficient resource use. Optimal large language models (LLMs) are selected based on cost, accuracy and energy efficiency. As a result, Moeve saw cost optimization (50%), as well as reductions in development time (65%) and electricity consumption (15%) using optimal LLMs. Aker BP: Data-driven carbon efficiency Situation/context Approach Results Aker, a large oil company, aims to be among the world’s most carbon-efficient operators.They partnered with a software as a service (SaaS) company to deploy an advanced AI platform for safer, more efficient offshore operations with data-driven, autonomous capabilities.Aker BP aims for autonomous operations at Yggdrasil. These would be periodically unmanned and remotely managed by two onshore operators with real-time data integration. US energy provider: Transforming energy analytics Situation/context Approach Results Visual inspection of electrical distribution systems is typically manual. The company uses ML for efficient electrical infrastructure inspections, enabling drone imagery storage, data attribution, and model evaluation for faster corrective actionsReduction in company’s end-to-end cycle time to build and deploy new computer vision models by over 50%. Sector: Energy Enel collaboration Situation/context Approach Results Enel had a business challenge around the processing and accessibility of operational intelligence KPIs delaying control over worldwide operational performance.Enel collaborated with a tech company to provide real-time insights for company stakeholders. The conversational AI solution was adopted globally in eight countries, available in five languages, and deployed across 400 generation units.Conversational AI with 90% lower capital expenditure, increased business efficiency, and 50% savings in storage and computing power. Source: Community consultations. Artificial Intelligence’s Energy Paradox: Balancing Challenges and Opportunities 13

Artificial Intelligences Energy Paradox 2025