M C K I N S EY Q UA RT E R LY 140 home goods, and other complex purchases, del egation is selective and situational. Agents may autonomously handle research, comparison, mon itoring, and assembly while escalating decisions that involve meaningful trade-offs. An AI travel agent, for example, might assemble an itiner ary, optimize for loyalty benefits, and monitor for disruptions but still surface choices that require judgment—time versus comfort, cost versus flexi bility. A home electronics agent may narrow options based on specifications and reviews but defer to the human when design, compatibility, or brand preference becomes decisive. - - - - In these categories, trust is built not through perfect execution but through explainability and reversibility. As autonomy increases, consum ers want to understand not just what the agent did but why it behaved in that manner. Why did it choose a particular option? Why did it make a substitution? Why did it escalate an exception? Graceful handling of edge cases matters more than success on the happy path. Margins are shaped by service guarantees, fulfillment reliability, and clarity of policies. This is where metadata becomes strategy. Humans infer meaning intuitively, considering factors such as fit, feel, mood, and suitability for a particular occasion. AI agents, of course, do not. They rely on structured, contextual signals. Prod ucts that are emotionally legible to people but semantically opaque to machines risk becoming invisible in agent-mediated flows. This requires retailers to invest in rich, machine-readable attri butes that enable agents to act with nuance—and to know when to pause and elevate questions to human shoppers. - - HOW VALUE POOLS SHIFT WHEN AGENTS MEDIATE COMMERCE Across these patterns, one shift is consistent: the compression of the traditional funnel. Search, com parison, and consideration collapse into a single agent-mediated moment. Continuous commerce replaces episodic decisions. Loyalty becomes less about sentiment and more about policy. As a result, value pools migrate. Advantage accrues to merchants that can reliably execute against agent constraints, not just those that attract human attention. Margins are shaped by service guarantees, fulfillment reliability, and clar ity of policies. For some players, this will unlock efficiency and scale. For others, particularly those dependent on discovery-driven traffic, it introduces the risk of disintermediation. - Importantly, this does not imply a single end state. The automation curve does not prescribe where every category should end up. Instead, it describes the instances where delegation creates value and where it does not. Retailers that recog nize these contours early can invest accordingly, - pushing toward higher autonomy where it reduces friction and deliberately preserving human moments where they matter most. The future of commerce is not about maxi mizing automation. It is about placing autonomy where it enhances experience, economics, and trust. The automation curve offers a practical lens for making those choices. Retailers that use it to guide capability investment, category strat egy, and agent readiness will be best positioned to compete as AI agents become an increasingly central interface of commerce. - - » Deepa Mahajan, Hannah Mayer, and Roger Roberts are partners in McKinsey’s Bay Area office, where Katharina Giebel is a consultant; and Katharina Schumacher is a partner in the Munich office. » The authors wish to thank Eli Stein, Holger Harreis, Jack Trotter, Marcus Keutel, Philipp Kluge, Tara Balakrishnan, and Zamir Lalji for their contributions to this article. - - Automation Curve