Artificial Intelligences Energy Paradox 2025

Use cases by sector TABLE 4 Sector: Building and space heating/cooling AI-enabled building management Situation/context Approach Results This solution enabled macro-optimization of HVAC operations across multiple buildings. This autonomous AI solution extended beyond simple sensors, incorporating internal and external data (energy cost, weather, occupancy, etc.) to co-optimize locations simultaneously.Using individual forecast models for each HVAC zone enabled electricity consumption reductions of 9-30%, and annual cost savings of $100,000-150,000. Sector: Communications Comcast: AI-driven network transformation for energy efficiency Situation/context Approach Results Comcast implemented a network transformation to virtualized, cloud-based technologies, with AI/machine learning (ML).Comcast implemented a comprehensive network transformation initiative, harnessing cutting-edge cloud, AI/ML technology, virtualization and digital optics, and revolutionizing network operations.As a result, there has been a 40% reduction in the amount of electricity required to deliver data across the network. Sector: Manufacturing Johnson & Johnson: Enhanced manufacturing Situation/context Approach Results To address growing energy demands and reduce environmental impacts, Johnson & Johnson constructed a state-of-the-art manufacturing site.Johnson & Johnson implemented advanced capabilities, including AI algorithms for process control, internet of things (IoT)- based intelligent cleaning and digital twins.There has since been a 47% reduction in material waste, 26% decrease in greenhouse gas emissions and 23% reduction in electricity consumption. Schneider Electric: Site emissions reduction Situation/context Approach Results Schneider ‘s Hyderabad site aims to be zero carbon for Scope 1 and 2 emissions by 2030.The system is powered by real-time data generation and cloud analytics for facility assets that interlink with shop- floor operations using industrial internet of things (IIoT) capabilities and AI-based predictive monitoring.As a result, there has been a 59% reduction in electricity consumption, 61% decrease in emissions, 57% water consumption reduction and 64% reduction in waste generation. Siemens: Facility energy management Situation/context Approach Results To become a zero-carbon pioneer, Siemens’ Chengdu factory deployed advanced technologies and capabilities.The company deployed a digital energy management system, predictive maintenance capabilities, AI-based automation and applied eco-design features, improving circularity and dematerialization.This reduced unit product electricity consumption by 24% and production waste by 48%.This paper highlights select AI use cases for improving energy efficiency. These examples, however, are not intended to represent a comprehensive inventory of all potential AI applications.2.2 Sample use cases Artificial Intelligence’s Energy Paradox: Balancing Challenges and Opportunities 12