How agentic commerce is becoming the new front door to retail

2026-02-09 · Source: The Microsoft Cloud Blog · Field: Retail & Consumer Goods — Retail Technology & Operations, Customer Experience & Engagement, Retail Analytics & Intelligence · Depth: Intermediate, medium

Summary

Agentic commerce is transforming retail by replacing traditional search with AI-guided conversations, where AI assistants interpret shopper intent, evaluate constraints, and recommend products. Bain & Company estimates 30% to 45% of US consumers already use generative AI for product research. This shift creates a "decision layer" between shoppers and brands, with McKinsey projecting up to $1 trillion in orchestrated revenue in the US B2C retail market by 2030, and $3 trillion to $5 trillion globally. Brands must ensure discoverability on third-party AI platforms like Microsoft Copilot and build owned conversational experiences to capture valuable customer intelligence. This intelligence informs merchandising, pricing, inventory, and content strategy, creating a feedback loop that strengthens with every interaction and builds lasting competitive advantages.

Key takeaway

For CMOs navigating the evolving retail landscape, your strategy must now center on guiding decisions at the moment of choice and capturing the intelligence those interactions produce. Prioritize optimizing product data for AI engine optimization (AEO) and generative engine optimization (GEO) to ensure discoverability, and deploy owned conversational experiences to build trust and gather proprietary insights that will inform future product development and marketing efforts.

Key insights

Agentic commerce uses AI conversations to guide retail decisions, creating a new front door for product discovery.

Principles

• Influence shifts to the moment of decision.
• Conversations generate actionable business insights.
• Trust is critical for owned agentic experiences.

Method

AI agents consider weather, style, inventory, pricing, and reviews to recommend products, while simultaneously generating demand signals for marketers to create targeted promotions.

In practice

• Optimize product data for AI discoverability (AEO/GEO).
• Launch owned AI shopping assistants.
• Design for product intelligence portability.

Topics

• Agentic Commerce
• AI Agents
• Retail Innovation
• AI Engine Optimization
• Conversational AI

Best for: AI Product Manager, Investor, Entrepreneur, Executive, Marketing Professional, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Microsoft Cloud Blog.