Festive-season planning is beginning, and the platform playbooks have started arriving.
Microsoft has published a new agentic-commerce playbook. Google describes 2026 as the first AI-powered holiday season and places rich product feeds among its foundational priorities. TikTok is also encouraging advertisers to connect festive discovery with catalogue-led conversion.
The platforms are promoting different products, but a common signal sits underneath their recommendations: product data is becoming more important to how shoppers discover, compare and select what to buy.
This matters particularly as brands prepare for compressed timelines, changing inventory, promotional pricing and high-intent demand. Feed weaknesses that remain manageable during quieter months can become expensive when availability and offers change quickly.
For years, performance marketers have treated the product feed as campaign infrastructure. It supplied titles, prices, images and availability to Shopping ads, marketplace listings and dynamic catalogue campaigns. Feed optimization usually meant improving ad eligibility, reducing disapprovals and helping platforms match products with relevant searches.
That job is not disappearing. But it is becoming much bigger.
As consumers use AI assistants to discover, compare and choose products, structured product data becomes an input into the recommendation itself. A feed no longer serves only an advertising platform. It increasingly helps an AI system understand what a product is, who it may suit and whether it can be confidently recommended.
For performance marketers, this turns product-feed quality from a technical maintenance task into a discoverability and growth issue.
Three shopping behaviours now coexist
Microsoft’s playbook describes three eras of the web that shoppers currently move between. A practical way for marketers to interpret them is:
- Human-led research: the shopper searches, opens pages and compares products personally.
- AI-assisted choice: an assistant evaluates options and presents recommendations, but the shopper still completes the purchase.
- Agent-led action: the shopper defines the goal and an AI agent finds, evaluates and may complete the transaction.
These behaviours are not replacing one another overnight. They are operating simultaneously. Brands therefore need to win human attention while also making products sufficiently structured for machines to retrieve, compare and act upon.
AI shopping adds a new decision-maker
A conventional ecommerce journey may begin with a search query, advertisement or marketplace category. The shopper then compares options and decides what deserves attention.
An AI-assisted journey can work differently. A customer might ask: “Find a lightweight office bag under ₹4,000 that can hold a 15-inch laptop, arrive before Friday and be returned easily if it does not fit.”
The assistant must translate that request into product requirements. It may compare price, dimensions, material, compatibility, stock status, delivery promises and return conditions before presenting a shortlist.
The product is therefore not competing only for a click. It is competing to be understood and selected by a system acting between the brand and the customer.
Microsoft’s August 2026 playbook draws a useful distinction: human shoppers respond to imagery and storytelling, while AI assistants rely on structured data and facts. Microsoft also cites Adobe Analytics research reporting stronger conversion among AI-referred visitors. The precise effect will vary by business and market, but the strategic signal is clear: AI-mediated discovery is becoming commercially meaningful.
What makes a product feed useful to AI?
An AI-ready product feed is not simply a longer feed. It is complete, accurate, consistent and easy for machines to interpret. Six areas deserve attention.
1. Clear product identity
The product title, brand, category, SKU and recognised identifiers such as GTIN or MPN should agree across the feed and product page. A strong title identifies the product naturally and includes the attributes a customer would use to distinguish it.
2. Current commercial information
Price, currency, availability and promotional information must stay synchronized with the website. If an assistant encounters conflicting prices, confidence falls and the customer experience suffers.
3. Complete variants and attributes
Size, colour, material, dimensions, compatibility and other category-specific attributes help an AI system answer detailed customer requirements. A product may be present in the catalogue but functionally invisible for a particular request when the relevant attribute is missing.
4. Fulfilment and return information
Delivery speed, shipping cost, serviceability and return conditions can directly influence a recommendation, especially when the customer includes urgency, location or risk-reduction language in a prompt.
5. Useful descriptions—not promotional filler
Descriptions should explain what the product does, its important features, suitable use cases and meaningful limitations. Generic phrases such as “premium quality” provide little evidence for comparison.
6. Consistency across every surface
The feed, product page, structured data, marketplace listing and inventory system should tell the same story. Google recommends using both Merchant Center feeds and Product structured data because they work together to improve product understanding and eligibility. OpenAI also allows eligible merchants to share product feeds so current product information can be represented more accurately in ChatGPT shopping experiences.
The broader lesson is platform-independent: machine confidence depends on product truth being consistent wherever it is read.
The PCA AI-ready product-feed framework
Performance marketers can begin with a five-part audit.
1. Coverage
Are all eligible products and variants represented, or does the feed contain only the subset required for current advertising campaigns? A partial feed creates a discovery ceiling before optimization begins.
2. Completeness
Are important category attributes populated? Review missing values for identifiers, variants, specifications, shipping and returns.
3. Consistency
Do the feed, landing page and structured data agree on price, stock, product identity and variants?
4. Freshness
How quickly are price and inventory changes reflected? Identify fields that become outdated between scheduled updates. Daily updates are a useful baseline; faster-changing catalogues may need more frequent synchronization.
5. Usefulness
Could the available data answer a real customer’s comparison question, or was it written only to satisfy minimum platform requirements?
This audit should not belong to the media team alone. Feed readiness requires coordination across performance marketing, ecommerce, merchandising, product, analytics and development.
Five actions performance marketers can take now
1. Expand beyond the advertised catalogue
Check whether the feed contains the entire eligible product range or only the SKUs used in paid campaigns. Products absent from a machine-readable source may have fewer opportunities to appear in AI-powered shopping experiences.
2. Audit the products that drive the most revenue
Begin with top-selling and highest-margin products. Validate titles, descriptions, identifiers, product categories, prices and availability before attempting a catalogue-wide rewrite.
3. Establish a feed-refresh standard
Use daily updates as a starting point and increase frequency where stock or price changes rapidly. Monitor whether updates complete successfully instead of assuming the scheduled process is working.
4. Activate issue alerts
Feed-processing failures, disapprovals, price discrepancies and stock mismatches should reach the responsible team quickly. Alerts protect not only compliance but also product visibility and customer trust.
5. Create an AI-discovery measurement baseline
Track which AI systems crawl or cite the site, which pages receive AI referrals and how those visitors behave. Microsoft Clarity is one available route for reviewing AI visibility and referral behaviour alongside existing analytics.
Emerging infrastructure such as conversational checkout and agent-led purchasing is strategically important, but marketers should confirm country, currency, platform and account eligibility before including individual features in activation plans. The underlying feed-quality principles remain useful everywhere.
What changes for performance marketers?
The performance marketer’s responsibility is expanding in three directions.
First, feed optimization must move beyond disapproval management. The goal is not merely to keep products eligible for ads, but to make them understandable across search, shopping and AI-assisted discovery.
Second, feed health should connect with commercial outcomes. Missing attributes, inconsistent pricing and stale availability should be investigated alongside impression share, click-through rate, conversion rate and revenue—not treated as isolated technical warnings.
Third, measurement must account for AI-mediated journeys. AI visibility, citation and referral signals will not replace platform attribution, but they can help marketers understand how discovery is changing before the final click.
Creative still matters—but it enters later
The rise of structured product data does not make creative or brand storytelling less important. AI may build the shortlist, but a customer still needs reasons to prefer one brand, trust its claims and complete the purchase.
- Structured data helps the product become understandable.
- Accurate commercial information helps it enter consideration.
- Brand and creative help it become preferable.
- The website experience helps it convert.
Modern ecommerce therefore needs both machine-readable product truth and human-relevant persuasion.
The strategic takeaway
For a long time, product feeds sat behind the campaign. Customers rarely saw them, and many marketing teams treated them as operational plumbing.
AI shopping is bringing that hidden infrastructure closer to the front of the customer journey.
The next competitive advantage may not come only from creating a better advertisement. It may come from giving AI systems more complete, accurate and useful information than the competition.
Creative earns human attention. Structured product data earns machine confidence. Brands preparing for agentic commerce will need both.
The festive season makes this shift urgent, but the capability is not seasonal. A feed improved for peak demand becomes a reusable foundation for year-round advertising, organic product discovery and emerging AI-commerce experiences.
Frequently asked questions
What is an AI-ready product feed?
An AI-ready product feed is a complete, accurate and regularly updated source of structured product information that helps advertising platforms, search engines and AI assistants understand and compare products.
How is it different from a traditional advertising feed?
A traditional feed may contain only the attributes required for campaign eligibility. An AI-ready feed aims to represent the full catalogue and includes enough detail to answer questions about comparison, suitability, availability, delivery and returns.
Does product structured data replace a Merchant Center feed?
No. Google recommends using both when possible. Structured data helps search engines understand the product page, while a Merchant Center feed provides more control over product coverage and update timing.
Who should own product-feed optimization?
Performance marketing can lead the commercial use case, but ownership should be shared with ecommerce, merchandising, product, analytics and development because accuracy depends on several underlying systems.
Sources and further reading
- Microsoft Advertising: How businesses win when AI does the shopping
- Microsoft Advertising: Prepare for the Age of AI Shopping (PDF)
- Google Search Central: Product structured data
- Google Search Central: Share your product data with Google
- OpenAI: Shopping with ChatGPT Search
- Google: AI-powered holiday-season playbook
About the author
Meenaa Varshney is the Founder of PCA Engine and a performance marketing leader with 12+ years of experience across digital advertising, measurement, marketplaces and marketing operations. Through PCA Engine, she helps marketers and businesses become future-ready at the intersection of Performance, Career and AI.