Essential leadership practices Two practices can help leaders move more effectively through this reinvention: Demonstration: Findings from interviews conducted for this white paper reveal that while many leaders recognize the importance of AI, they also lack the time or willingness to engage directly. This behaviour hinders broader adoption. Senior leaders must role-model AI adoption to drive organizational transformation by proactively and frequently collaborating with teams to cultivate best practices and identify new opportunities. They must become adept at using automation, data and algorithms to guide their decision-making. Empowerment: Enabling employees to experiment with AI in a risk-free environment and offering comprehensive (and increasingly personalized) training will build confidence and cultivate innovation. Regular surveys, pulse checks and open channels of communication – with responsive actions – will help employees feel heard and supported as they navigate the changing landscape of work. One Asia-based company within the community implemented an adaptive AI training programme to ensure all employees, including non-tech staff, are “AI-ready”. The company ran a “Monday practice, Friday feedback” cycle, where employees learned about AI tools and provided feedback on their usefulness. After completing the programme, only 40% of employees feared that AI could displace their roles, down from 85% before the programme. Training that cultivates critical thinking, adaptability and creativity – skills that are crucial in an AI-driven environment – is also important. AI academies, seminars and hands-on workshops can be instrumental in equipping employees with the knowledge they need to thrive. AI allows people to bring their magic and build extraordinary results beyond average outcomes. When measuring productivity gains, in some areas the company is seeing double-digit increases when AI is embedded into workflows. Raheel Khan, Senior Vice-President, Foresight and Growth Intelligence, The Estée Lauder Companies 4.2 Digital core As leaders race to scale AI across their operations, they are running into a common barrier: outdated data strategies and patchy information technology (IT) infrastructure that can’t handle the demands of modern AI. Despite 45% of consumer goods and retail executives believing they can scale genAI enterprise-wide in 6-12 months, only 13% are extremely confident that their current digital foundations can support AI deployment at scale.39 In fact, at least 30% of genAI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls and escalating costs, according to Gartner.40 What they need is a cohesive, AI-ready digital core (a term coined by Accenture to describe the technological capability needed to empower reinvention ambitions). Reduced to its essentials, a digital core comprises sophisticated digital platforms, a seamless data and AI backbone, and a secure foundation based on radical new engineering principles (Figure 20).41 of genAI projects will be abandoned after proof of concept due to poor data quality, inadequate risk controls and escalating costs.30% Transforming Consumer Industries in the Age of AI 26

Transforming Consumer Industries in the Age of AI 2025