Agentic Commerce: How AI Agents Are Transforming Your Google Ads Campaigns

Agentic Commerce: When Machines Become Your Buyers

A fundamental shift is redefining e-commerce. AI agents—autonomous programs capable of researching, comparing, and purchasing products without human intervention—are beginning to represent a growing share of interactions with your Google Ads campaigns.

According to Gartner projections, by 2028, 15% of daily purchasing decisions will be made by autonomous AI agents. For B2B companies, this shift requires a complete overhaul of advertising strategies that have been designed to convince humans until now.

This guide explains how AI agents are changing the rules of the game on Google Ads, and what technical and strategic adjustments to implement right now to keep your campaigns performing effectively against these new algorithmic buyers.

What is Agentic Commerce and Why It Matters to Your Business

Definition of Agentic Commerce

Agentic commerce refers to all commercial transactions where an AI agent acts autonomously on behalf of a human user. In practical terms, a business executive can ask their AI assistant: "Find me the best electronic components supplier in Europe with 48-hour delivery and negotiate a price for 500 units."

The agent will then browse websites, analyze product specifications, compare prices, verify delivery terms, and potentially complete the order. All of this without the human clicking on a single Google Ads ad.

Major Players in This Transformation

Several technologies are driving this revolution:

• ChatGPT with its shopping plugins: capable of searching and comparing products in real-time
• Google Gemini: integrated into the Google ecosystem, it can interact directly with Google Shopping
• Perplexity Shopping: AI search engine with integrated purchasing functionality
• Microsoft Copilot: AI assistant capable of making purchases through Bing Shopping
• Specialized B2B AI agents: solutions like Salesforce Einstein or SAP Joule automate professional purchasing

Why SMEs and Mid-Market Companies Are Directly Affected

Contrary to common assumptions, agentic commerce doesn't only affect large corporations. B2B SMEs are on the front lines for several reasons: their professional clients are rapidly adopting these tools to save time, B2B purchase cycles involve comparative research that AI agents excel at automating, and technical product catalogs are particularly well-suited to algorithmic processing.

How AI Agents Interact with Your Google Ads Campaigns

A Radically Different Purchase Journey

When a human searches for a product, they see an ad, click, browse your site, hesitate, return, and maybe eventually convert. This journey generates behavioral data that Google Ads uses to optimize your campaigns.

An AI agent functions differently. It analyzes your structured data, product specifications, and data feeds without necessarily generating traditional impressions or clicks. It can extract the information it needs directly from your Google Merchant Center product feed or through your site's structured data.

Signals That AI Agents Read

AI agents prioritize objective and measurable criteria:

• Schema.org structured data: price, availability, delivery times, technical specifications
• Product feed attributes: GTIN, MPN, Google categorization, custom attributes
• Trust indicators: customer reviews, certifications, clearly stated warranties
• Commercial terms: return policies, payment methods, free shipping thresholds
• Data freshness: last update date for prices and inventory

Impact on Your Google Ads Metrics

At AISOS, we observe that companies whose catalogs are regularly consulted by AI agents notice anomalies in their dashboards: declining click-through rates while conversions remain stable, stable impressions but atypical post-click behavior, increased "direct" conversions that are difficult to attribute.

These signals indicate that AI agents are extracting your product information and generating sales through paths not tracked by Google Ads.

Adapting Your Product Feed for AI Agents: Technical Guide

Enriching Required and Recommended Attributes

A product feed optimized for agentic commerce goes beyond Google Merchant Center's minimum requirements. Here are the critical attributes:

• Product identifiers: GTIN (EAN/UPC), MPN, and brand systematically filled in. AI agents use these identifiers to cross-reference information between sources.
• Detailed descriptions: minimum 500 characters with complete technical specifications. Avoid emotional marketing, prioritize facts.
• Custom attributes: use custom_label fields to indicate relevant B2B information like minimum order quantities, volume discounts, or industry certifications.
• Precise availability: beyond "in stock" or "out of stock," indicate restocking times and available quantities.

Structuring Data for Optimal Extraction

AI agents excel at analyzing structured data. Systematically implement:

• Schema.org Product: with all relevant properties (offers, aggregateRating, brand, manufacturer)
• Schema.org Offer: price, currency, availability, delivery conditions
• Schema.org Review: structured customer reviews with rating and author
• Schema.org Organization: information about your company, certifications, delivery zones

Regularly test your structured data with Google's testing tool and immediately correct errors. An AI agent that encounters poorly formatted data will move on to the next competitor.

Feed Update Frequency

AI agents place major importance on data freshness. An outdated price or a product shown as available but out of stock destroys the agent's trust, which learns to avoid unreliable sources.

Concrete recommendations: update prices and inventory every 4 hours minimum, real-time synchronization for fast-moving products, visible timestamps of last updates.

Rethinking Your Bidding Strategies for the AI Agent Era

Limitations of Click-Based Bidding

Traditional bidding strategies optimize to maximize clicks or tracked conversions. However, if an AI agent extracts your product information without generating a click, then recommends your product to its user who subsequently purchases directly, this conversion escapes your Google Ads attribution.

Consequence: your bidding algorithms undervalue the real performance of certain products or keywords, and reduce bids on segments that are actually profitable.

Adapted Bidding Strategies

Several adjustments help better capture the value generated by agentic commerce:

• Expand the conversion window: go from 7 to 30 days, or even 90 days for B2B, to capture delayed conversions initiated by AI agents.
• Integrate offline conversions: import your CRM data into Google Ads to attribute sales that didn't follow a classic digital path.
• Use value-based strategies: prioritize "Maximize conversion value" rather than "Maximize conversions" so the algorithm optimizes on actual revenue.
• Test product-level bidding: in Performance Max and Shopping, segment your products and adjust bids according to their attractiveness to AI agents.

Monitor the Right Indicators

Beyond classic ROAS, track these metrics to evaluate your performance in agentic commerce:

• Impression share on technical queries: AI agents use precise queries with product references and specifications
• Conversion rate by device type: an abnormally high rate on certain segments may indicate AI agent interactions
• Evolution of direct traffic post-impression: correlation between your Google Ads impressions and subsequent direct visits
• Feed quality according to Google Merchant Center: data quality score and product approval rate

Preparing Your Presence in Generative AI Responses

The Link Between Agentic Commerce and GEO

Agentic commerce and GEO (Generative Engine Optimization) are two sides of the same coin. An AI agent searching for a supplier for its user will consult the same sources that ChatGPT or Perplexity use when generating a response.

Being cited as a reference in generative AI responses mechanically increases your chances of being selected by autonomous AI agents. Selection criteria are similar: domain authority, information clarity, structured data, and consistency of mentions across the web.

Concrete Actions to Improve Your AI Visibility

Here are the optimizations to prioritize:

• Create reference pages: buying guides, technical comparisons, detailed FAQs that AIs can cite as reliable sources
• Develop your Knowledge Graph: ensure your company and flagship products appear correctly in Google Knowledge Graph
• Multiply third-party mentions: press articles, customer case studies, testimonials on industry sites strengthen your credibility in the eyes of AIs
• Maintain NAP consistency: name, address, phone identical across all your online profiles

Audit Your Current Visibility

Regularly test how AIs perceive your company. Ask ChatGPT, Perplexity, and Gemini questions that your potential customers would ask: "Who is the best supplier of [your product] in France?", "Compare [your brand] and [competitor] for [specific use]."

AISOS audits reveal that 70% of B2B SMEs don't appear in any generative AI response for their main business queries. This is a major opportunity for companies that act now.

Action Plan for the Next 90 Days

Month 1: Diagnosis and Foundations

Start by evaluating your current situation. Audit your Google Merchant Center product feed by checking attribute completeness, reported errors, and data quality score. Test your Schema.org structured data across all your product pages. Measure your visibility in ChatGPT, Perplexity, and Gemini responses for your top 10 commercial queries.

Month 2: Technical Optimizations

Enrich your product feed with missing attributes. Implement or correct your structured data. Increase your feed update frequency. Adjust your bidding strategies by expanding conversion windows and integrating available offline conversions.

Month 3: Content and Authority

Create two to three reference content pieces optimized for GEO on your main topics. Seek mentions on trusted industry sites. Set up monthly tracking of your AI visibility. Analyze initial results and adjust your Google Ads strategy accordingly.

Conclusion: Act Now to Maintain Your Advantage

Agentic commerce is no longer a futuristic projection. AI agents are already influencing your B2B customers' purchasing decisions, and this trend will accelerate over the next 24 months. Companies that adapt their Google Ads campaigns, product feeds, and overall digital presence now will gain a competitive advantage that will be difficult to catch up with.

The good news: technical adjustments are accessible to SMEs and mid-market companies. They don't require additional advertising budgets, but a methodical approach to optimizing your data and visibility in the AI ecosystem.

At AISOS, we help SME and mid-market executives navigate this transition to agentic commerce and GEO. Request a free audit of your AI visibility to identify your action priorities and transform AI agents into new advocates for your offering.