Why retail's legacy infrastructure is failing the AI era

The traditional e-commerce playbook of driving traffic to a central website is rapidly losing its efficacy. McKinsey estimates that agentic commerce could influence $1 trillion in B2C retail revenue by 2030, with AI agents handling discovery and checkout on behalf of shoppers. As these algorithms replace manual search, product data must be structured, current, and instantly accessible to any surface. Retailers tethered to rigid, monolithic stacks often find themselves unable to adapt, as their systems cannot push real-time information to the chat windows and social feeds where customers increasingly dwell.

Composable architecture offers a path forward by decoupling storefronts, catalogs, and checkout processes. Companies like SportShoes.com have utilized this modular approach to scale, allowing them to swap components without rebuilding entire systems. However, complexity remains a risk for smaller players. For those operating at scale, the priority is clear: audit product data for AI compatibility and optimize checkout flows. With cart abandonment rates hovering near 70%—and potentially 35% of that loss recoverable through better design—the cost of maintaining aging, inflexible technology is no longer just a technical debt, but a direct drain on revenue.