AI in Action Beyond Experimentation to Transform Industry 2025

4 Advancing sustainability: AI could play a growing role in other sustainability efforts by offering tools for optimizing energy use, managing waste and supporting companies during their sustainability transformations.66 AI-driven systems, for example, could enable real-time monitoring of environmental data, predictive modelling for climate scenarios, efficient resource allocation and integration of renewable energies.67 The increased energy demands as a result of increased AI adoption is an impact that needs to be carefully considered and managed.68 Transforming aviation sustainability: By using AI to create digital twins of engines, Rolls-Royce can now analyse real-time data to optimize performance and reduce unnecessary maintenance. This approach has already saved 22 million tons of carbon and extended maintenance intervals by up to 50%, helping airlines avoid costly downtime and reduce parts waste.69 Future of consumer industries: SupPlant, a technology firm focused on precision agriculture, uses extensive data from thousands of growing seasons to enhance irrigation commands and optimize water use. The firm efficiently manages 1.5 billion sensor data points across 2,000 crop seasons. This collaboration promotes a proactive approach that helps farmers prevent plant stress, reduce fruit loss, maximize crop potential and manage water resources much more efficiently than with alternative techniques.70AI mapping of icebergs: Accurate mapping of icebergs is crucial to tracking the effects of climate change. Researchers at the University of Leeds recently unveiled a neural network that can accurately map large Antarctic icebergs with the help of satellite images. More impressive still, their AI can do this in just 0.01 seconds – compared to the hugely time-consuming manual efforts that were needed previously to complete the same task.71 Data-centricity yielding energy generation: Using an AI-enabled digital twin platform, a leading multi-energy company optimized wind turbine alignment in large wind farms where traditional machine learning had limitations. The platform combined physics and data to analyse wind farm dynamics, optimizing yaw alignment to increase power output without disrupting airflow. This solution generated $15 million in annual value at a single site, with scalable potential across other farms.72CASE STUDY 16 AI’s role in sustainability and efficiency AI Governance Alliance: Transformation of Industries in the Age of AI 23