Transforming Consumer Industries in the Age of AI 2025

Components of a digital core FIGURE 20 To derive true value from AI, genAI and other advanced technologies, leaders must assess how their digital core supports their strategic goals. For example, consumer industry companies aiming to enable hyper-personalized consumer profiling and segmentation need to be sure that each layer of their digital core is equipped to support this level of personalization at scale.Combining applications into platforms that generate new sources of value.Digital platforms Data: Information about a business and its customers that is accessible and usable.AI: Artificial intelligence used to boost productivity, create content and more, including enhancing cloud and edge computing.Data and AI backbone Cloud-first infrastructure: Flexible infrastructure that can be a mix of public cloud, private cloud and edge computing. Security: Embedding cybersecurity practices to maximize resiliency.Continuum control plane: A command-and-control centre that provides visibility into key parts of the entire tech stack. Composable integration: The ability to connect all parts of the digital core so they can work together.Digital foundation Source: Accenture. 4.3 Responsible AI High standards of trust, transparency and sustainability in every AI- and genAI-related initiative are non-negotiable – they are the bedrock of trust. However, although 95% of businesses recognize the potential impact of regulations such as the EU AI Act, only 6% have taken steps to establish responsible AI foundations or implement guiding principles. Automated ESG tracking and optimization already have a 35% adoption rate across consumer industries, indicating businesses are increasingly recognizing the importance of responsible AI and moving towards its widespread implementation.42 Even so, this gap is particularly concerning given that only 35% of consumers currently trust how organizations are implementing AI technologies.43 Focusing on three areas will help companies operationalize responsible AI: Trust, bias and transparency: AI models have the potential to reflect biases inherent in their training data, such as existing social inequalities and stereotypes. For example, AI’s dominance in English- speaking markets marginalizes non-English users, especially in the Global South. This is particularly risky for consumer industry companies, where biased AI outputs can directly impact consumer trust, damage brand reputation and lead to regulatory scrutiny. Integrating human-by-design principles – such as fairness, transparency, explainability and accuracy – is crucial to mitigating unintended consequences and biases. Data and governance: Consumers expect meaningful value in exchange for sharing their data, but growing concerns over privacy and accuracy can quickly lead to trust issues. Maintaining trust requires reflection on data governance through a consumer-centric lens, and an emphasis on transparency and safety. Educating consumers about the amount and type of data collected can support them in making informed choices. Sustainability: Even though AI technologies require an enormous amount of energy, they can also help companies mitigate those needs. AI has the potential to address sustainability challenges confronting business and society by optimizing processes and reducing resource consumption. It can act as an accelerator in achieving net-zero goals across multiple dimensions (Figure 21). Only 35% of consumers currently trust how organizations are implementing AI technologies. Transforming Consumer Industries in the Age of AI 27