Molly Schonthal
Managing Director, Agentic Commerce Transformation
VML NA
Unlock Your Brand's "Share of Decision" in AI Search
As commerce shifts from browsing to AI-mediated recommendation, our Unlock whitepaper unpacks how to win across retailer agents, search agents and LLMs.
Today, a new layer is emerging between brands and consumers: AI agents, tools and platforms that increasingly influence shoppers and make decisions on their behalf.
When purchase decisions are made or influenced by AI before a consumer ever visits a retailer’s website, how can you ensure visibility when machines decide what shoppers should see?
“Your PDP Is Now Training Data for AI: The Real Battle Isn’t Commerce. It’s Decision-Making." is a guide to help brands navigate this new paradigm and optimize around a stronger Brand Data Core – a structured layer of product truth that travels seamlessly across retailers, agents, search systems and recommendation engines.
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For brands, visibility can no longer be understood solely in terms of where a product appears on a shelf, a search page or a retailer website. It also depends on whether an AI system can understand the product, match it to a specific need, and confidently include it in a recommendation.
Why Brand Data Matters
Historically, e-commerce optimization focused on human persuasion: catchy copywriting, beautiful product imagery, and keyword stuffing for traditional SEO. In the age of agentic shopping, your Product Detail Page (PDP) is no longer just a digital storefront for humans – it's training data for machines.
If an AI agent cannot easily crawl, interpret and trust your product information, it will not recommend it. A competitor with a structurally superior data architecture – even with a lesser product – will win the recommendation because the machine has higher confidence in their data.
Crucial data points AI agents look for include:
- Structured product attributes and technical specifications
- Verified regulatory certifications and ingredients
- Usage guidance, dosage and compatibility data
- Supply-chain details and maintenance requirements
- Contextual use cases (e.g., "What is the best detergent for a household with young children and sensitive skin?")
The PDP is therefore no longer just the place a customer visits after discovering a product – it's becoming one of the input layers machines use to determine whether that discovery happens at all.
In our guide you'll find:
“Your PDP Is Now Training Data for AI: The Real Battle Isn’t Commerce. It’s Decision-Making." delivers a playbook for surviving the shift from share of shelf to share of decision:
- The New Commerce Stack: Discover how the world's largest platforms like Amazon and Walmart are racing to control the AI decision-making layer
- The Brand Data Core: Your Single Source of Truth: To win on the "invisible shelf," brands must build a Brand Data Core that ensures your product's "truth" survives the journey into an AI agent’s reasoning engine
- An Action Plan to Prep Your Brand For AI Search: Learn how to test your product’s machine-discoverability, trace inaccurate information, and identify where agents are hallucinating and intelligibility is lost between its source and the agent’s answer
Ready to unlock your brand's "Share of Decision" in AI search? Download our guide and contact our experts for a bespoke assessment today.
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FAQs: Unlock Your Brand's Share of Voice
Agentic commerce refers to an ecosystem where AI agents, tools and platforms increasingly influence, shape and execute purchase decisions on behalf of consumers. When consumers use AI engines (like ChatGPT, Perplexity or Gemini), retailer-specific assistants (like Amazon’s Rufus or Walmart’s Sparky), or OS-integrated apps (such as Apple Intelligence and Copilot), they stop browsing. They start asking.
Because the AI acts as the direct mediator between user intent and commercial action, the traditional product listing page or store shelf has become the Invisible Shelf. The winner of the commerce battle is no longer whoever has the highest paid search spot – it's whoever influences what makes it into the AI's final recommended answer.
Historically, Product Detail Pages (PDPs) were designed to persuade human shoppers with copywriting and creative assets. In the agentic commerce era, PDPs also serve as the input and training data that AI shopping agents (like Amazon's Rufus or Apple Intelligence) crawl, parse and analyze to determine if a product matches a consumer’s conversational request.
"Share of Decision" is the modern evolution of "Share of Shelf." It is the percentage of times an AI agent recommends, prioritizes or selects a brand’s product when acting as the mediator between a consumer’s intent and their ultimate purchase action on the "Invisible Shelf."
Traditionally, organizations treated product data and marketing content as separate disciplines. Agentic commerce collapses those distinctions. AI agents synthesize answers from a wide array of unstructured and structured sources to make a recommendation.
An AI agent evaluating your product needs to know data points far beyond a traditional marketing brief, including:
- Regulatory and sustainability certifications
- Technical specifications and compatibility data
- Maintenance requirements and lifespan
- Dosage or usage guidance
- Supply-chain origin information
- Contextual use cases
AI agents do not browse websites like humans. They interpret structured data signals. If your product's ingredients, certifications, compatibility data, or usage contexts are poorly structured or missing, the AI agent will not have enough confidence to recommend your product, allowing a competitor with better-structured data to win the decision.
