Understanding the invisible shelf 

Brand equity, physical availability, and disruptive packaging still matter. But shoppers will also embrace AI agents that discover products, compare options, negotiate prices, and complete purchases on their behalf. Agents working for merchants will handle everything from personalizing product recommendations and managing inventory to offering promotions, negotiating deals, and even processing transactions with consumer agents. 

At Google, we anticipate two primary interaction models in this new commerce landscape:

Consumer-to-Merchant (C2M): In this model, a consumer’s personal AI agent interacts with merchant agents on their behalf. This is personal shopping at its most efficient.  For example, a consumer could instruct their agent: “I’m struggling to find a good moisturizer. Can you recommend a couple of beauty products for my sensitive skin?” The consumer agent, armed with knowledge of the user’s preferences, budget, and needs, evaluates options across merchants. It interacts with marketplace agents to discover products, inventory agents to confirm availability, and payment agents to complete the purchase. 

Merchant-to-Merchant (M2M): This model involves a merchant’s AI agent interacting with other merchant agents to allow CPG brands to offer a path to retail conversation without losing the user from their properties. Say a consumer asks a retailer’s agent to purchase a product that’s out of stock or not in the catalog. Instead of losing the sale, the retailer’s agent could interact with other agents to source the item, complete the transaction, and fulfill the order, resulting in a happy consumer. Agents become collaborators, and brands capture more revenue.

Stocking the invisible shelf

In 2026, CPG leaders must be ready for consumers who browse store shelves, search online, and use AI agents as personal buyers. Your goal is to capture these consumers by making sure their AI agents can find and recommend your brands. This means the product data you share with agents — the specs, certifications, and attributes — is now a new, valuable marketing asset.

Treat your product data as your new packaging. Attractive packaging wins in traditional shopping environments. In agentic commerce, product data wins. For example, if your product uses sustainable packaging, an AI agent searching for “verified sustainable packaging” won’t find it unless that information is structured and tagged. With “catalog and content enrichment,” Google Cloud helps CPGs apply advanced machine learning and computer vision to automatically tag, categorize, and enhance your product data with verifiable attributes that retailer agents can read and prioritize.

Shift from search engine optimization (SEO) to generative engine optimization (GEO). If your brand doesn’t sell direct-to-consumer, you can’t rely on SEO alone. You need a two-part strategy: use Gemini Enterprise to analyze latest trend data for brand sentiment, consumer interests and prompt trends, then leverage those trends to create content and fine-tune messaging for agentic optimization. Ensure AI agents can find and recommend your products with messaging that speaks directly to what each shopper wants.

Prepare for agent-to-agent commerce. As B2B procurement shifts to automated transactions, your business needs digital agents that can negotiate on your behalf. AI agents can respond to inventory queries and approve promotional price adjustments in real time, without waiting for a human. Google Cloud’s Vertex AI Agent Builder lets you deploy agents that manage complex tasks autonomously, while our Agent-to-Agent (A2A) protocol provides the standardized framework these agents need to communicate securely with retailer and consumer agents — ensuring transactions happen without friction.