AI Becomes Active Participant in Online Purchasing

The Ecommerce News article reports that McKinsey & Company published a study titled "Europe's new e-commerce agenda: How AI is resetting growth and competition," which estimates online revenues in Germany, the United Kingdom, Spain, Italy and France could reach 600 billion euros by 2029 and forecasts ecommerce growth of about 6 percent annually in those markets over the next three years, per McKinsey. The article also cites McKinsey as saying roughly 38 percent of European consumers already use generative AI tools to research products and make purchasing decisions - a figure confirmed directly in the McKinsey report text. A companion McKinsey piece, "Europe's agentic commerce moment," notes that global B2C retail could see $3 trillion to $5 trillion in orchestrated revenue flowing through agentic commerce models by 2030. Separately, PSE Consulting surveyed 4,250 consumers in the United Kingdom, United States, France and Germany and reported that 74 percent prefer an independent AI shopping assistant, 41 percent prefer cross-platform assistants like Gemini, 33 percent prefer specialist assistants, and 10 percent want an assistant embedded in an online store.

Industry-pattern observations show a shift from AI as a discovery aid to AI acting as an active decision agent, which raises technical priorities around interoperability, secure credentials, and dataset provenance. Companies undertaking comparable integrations often need robust APIs, standardized shopping graph schemas, and clear consent signals to support cross-platform assistants without leaking sensitive transaction data.

Observers note that high consumer preference for independent or cross-platform assistants amplifies the value of portable user profiles and reliable pricing feeds across retailers and marketplaces. Increased delegation of tasks like automatic reordering and multi-seller price comparison tends to shift engineering effort toward reliability, latency, and reconciliation of recommendations with real-time inventory and pricing. McKinsey notes that agentic AI is moving from a discovery aid to a transacting agent, which changes how retailers need to structure product data, pricing logic, and fulfillment signals to remain machine-readable and selectable.

Metrics and signals an observer can track include:

• •adoption rates of standardized shopping APIs and credential exchanges
• •growth in anonymized price and inventory streaming services
• •product review and returns rates for AI-assisted purchases
• •regulatory or platform policy changes around data portability and third-party assistant access. Monitoring follow-up publications from McKinsey & Company and PSE Consulting for methodology details will clarify sample frames and segmentation