A playbook that explores the power of agentic AI for consumer markets

• AI agents are moving from back-office automation to end-to-end value creation across consumer markets — from pricing and fulfilment to customer service and product launch.
• By 2030, one in three consumers is expected to purchase through an AI agent. The route to market is changing faster than most commercial models.
• The top 20% of firms already capture 74% of AI-driven returns, outperforming peers by 7.2x. Closing the gap requires foundations, ownership and workforce readiness.

Retail and consumer goods companies are being transformed by shifting supply chains, changing consumer confidence and a growing divide between AI leaders and everyone else. Agentic AI is more than an additional technology layer; it is a catalyst for reinvention, embedding intelligence into operations so agents can observe, decide and act across workflows.

This playbook, developed with Microsoft, maps where agentic AI is creating value today, what foundations are required to capture it, and the five leadership priorities that separate scaled programmes from stalled ones.

The gap between AI leaders and the rest in consumer markets isn't about ambition — it's about architecture. Value compounds when organisations stop optimising isolated processes and start connecting them. We identify eight Enterprise Value Loops where agentic AI is already delivering across the consumer value chain: from trend sensing and product launch, through demand planning and pricing, to fulfilment and customer retention.

Each loop is mapped with the redesign required to make it work — because layering agents onto broken processes produces faster failure, not faster results.

Scaling agentic AI requires an agent-ready architecture built around three connected layers: an experience layer where agents engage with customers, employees and suppliers; an orchestration layer where intent is converted into multi-step workflows, handoffs and governance; and an enterprise systems layer where worker agents carry out tasks in core systems using unified, governed data.

Five leadership priorities: portfolio review, business ownership, production-grade deployment and operating model redesign around the emerging Agent Boss role.

One clear message: for consumer market businesses, this is a commercial transformation, not an IT deployment.

Scaling agentic AI requires treating data as a leadership commitment, assigning named business owners to every agent domain, and redesigning team workflows, not just technology stacks.