Transforming Consumer Industries in the Age of AI 2025

Opportunities for collective action5 Key priorities are AI-driven business value delivery, environmental benefits and the need to strengthen society at its weakest points. 5.1 Which priorities, and when? In the near term, as business leaders learn more about AI potential for their companies, they will be focused on where to invest and what to prioritize to earn the returns they expect. The community has the opportunity, meanwhile, to look up and out, and pave the way for industry actors to move from ideas to action as easily and responsibly as possible. This is a chance to harmonize efforts and establish standards and practices that benefit businesses, the environment and society – encompassing both consumers and the workforce. AI for sustainabilitySustainability, particularly managing Scope 3 emissions (which account for over 80% of consumer industries’ emissions), poses significant challenges for consumer industries, particularly for those that rely on complex, multi-tiered supply chains and external partners.45 Fortunately, AI presents new opportunities to interconnect industry supply chains and enable transparent, efficient operations across sectors. Collective action thought starter Support the development of an AI-enabled shared logistics platform where companies can exchange anonymized data about empty trucks, for example, or underused containers. Through enhanced supplier collaboration, this platform would help companies optimize resources and track emissions more effectively. Cross-industry data sharingWith diverse data practices and proprietary formats across companies, seamless data sharing has become a significant challenge for consumer industries. Moving forward, industry-wide standards for data sharing and interoperability would enable industry-wide collaboration, reducing redundancy, ensuring greater efficiency across the entire industry value chain and enabling consistent, high-quality data to inform more robust and accurate AI models. What’s needed is a clear articulation of value, with savvy data use becoming a competitive edge across the industry. Collective action thought starter Define a unified framework for data sharing and auditing, allowing for seamless and secure industry collaboration. By harnessing AI models such as Global Standards One, or using approaches such as data clean rooms, companies can share supply chain and consumer data safely, improving trust and efficiency without creating competitive risks. Empowering workforce transitionDeveloping a skilled workforce capable of supporting AI-driven transformation is essential in the age of AI and genAI. Yet, current reskilling initiatives are often segregated, and industries lack an overarching framework for talent development that addresses rapidly evolving skill requirements. Consumer industries can work together to develop a shared framework for reskilling and upskilling, for example, harnessing AI to enable faster, targeted training. The industry can also pursue public- sector-supported AI initiatives to accelerate efforts. Collective action thought starter Establish a “standardized skills framework” to guide workforce reskilling and upskilling in the age of AI. This framework would ensure alignment on the critical skills required to thrive in an AI-driven landscape, allowing companies to collectively adopt best practices for talent development and workforce transitions. Industry self- governance to build trust and transparencyConsumers remain wary and confused about AI’s role in their daily interactions, with only a third trusting how organizations are implementing it. For consumer industries, this raises the question of how to communicate AI’s value transparently and address privacy concerns to build lasting trust. Given that 90% of AI’s success depends on the quality and reliability of data, an industry-wide approach to transparent communication is essential to cultivate consumer confidence. Collective action thought starter Establish the “rules of the game” to build trust through the harmonization of standards. This includes dimensions around data privacy, security and sharing, mitigation of algorithm biases in LLMs, and supporting consumer adoption by clearly communicating how AI and genAI are being harnessed. Transforming Consumer Industries in the Age of AI 29