Transforming Consumer Industries in the Age of AI 2025

Examples of AI tackling sustainability challenges FIGURE 21 Ability to identify more sustainable ingredients or materials with quality and experience superiority to reduce climate impacts Rapidly generating new product and packaging designs to attract more customers and reduce wasteHelping suppliers set greenhouse gas reduction or water efficiency targets, using their data to understand their local context Generating implementation guidance and assisting with completing data collection requestsAnalysing the performance of specific brands or products to determine their contributions to achieving sustainability goals Identifying high-risk areas to improve supply chain resiliency Tailoring sustainability messaging to break through a crowded, fractured landscape Designing a campaign to appeal to new and untapped customer groupsDrafting sustainability reports for disclosure and regulatory frameworks (CSRD, EUDR, ISSB, etc.) and to generate insights and monitor metrics across supply chainsAnalyse equipment data to predict failures, reducing downtime and energy wastage from malfunctioning machinery Harnessing traffic, fuel and route data to optimize product delivery and reduce consumption and emissionsEngage and upskill suppliersPortfolio analysis and management Sustainable R&D Consumer interactions Reporting and communicationsPredictive maintenance and logistics Source: Accenture. AI is critical to sustainability, according to Athina Kanioura, PepsiCo’s Chief Strategy and Transformation Officer. “As a company, we are making significant commitments in the sustainability space,” she notes. “But with this ambition, we need the right enablers, and AI becomes an enabler of how we protect the environment.” Kanioura highlights farming communities as a key area for AI- driven transformation, with PepsiCo helping farmers use AI in the field. Potato growers in North America, Latin America and Europe have gathered over a million data points, from seed selection to water use. Through machine learning, farmers improve productivity and optimize yields while promoting sustainability by reducing water, pesticides and greenhouse gas emissions. As more data is collected, PepsiCo’s farming practices become increasingly sustainable.44 While harnessing AI for environmentally positive outcomes is essential, responsible AI adoption that integrates sustainability as a core principle will ensure that AI systems are developed and deployed with a focus on minimizing ecological impact, especially in light of the high energy demands of AI systems. To that end, “sustainable-by-design” approaches include: 1. Improving energy efficiency: Prioritizing energy-efficient AI designs through algorithm optimization, advanced silicon technologies and AI support itself to enhance energy management across operations 2. Advancing low-carbon materials: Integrating sustainable materials into AI infrastructure, including green construction components, decarbonized supply chains and innovative fuels, to reduce overall carbon footprints 3. Creating sustainable data centre operations: Adopting efficiency and circularity innovations, with a focus on renewable energy, minimizing water use and implementing systems for carbon capture and water replenishment Another vital input to ensuring responsible AI use is collective action, covered in the final chapter. Responsible AI adoption that integrates sustainability as a core principle will ensure that AI systems are developed and deployed with a focus on minimizing ecological impact. Transforming Consumer Industries in the Age of AI 28