Agentic commerce has arrived.

Per Writtenly Hub , ChatGPT processes 50 million shopping-intent queries every day. The platform has reached 900 million weekly active users, a number that doubled in twelve months. Traffic from AI sources to U.S. retail sites grew 393% year over year in Q1 2026, according to Adobe Analytics , and that traffic converts 42% better than non-AI traffic.

This is no longer a test channel. This is infrastructure.

For the first time in twenty years, a new platform has displaced search as the primary shopping surface. Yet most brands remain almost entirely unprepared.

Agentic commerce represents a generational reset of who and what selects and buys products. The gap between demand for AI-powered commerce and brand readiness to serve it has created a window where first movers will lock in permanent competitive advantages.

The discovery layer is where this battle gets won first.

Agentic Commerce Fails Without Conversation Context

When a consumer asks ChatGPT, Google Gemini, or Amazon Alexa a question like 'I'm going to a friend's wedding this weekend and it might get a little hot, what should I wear?' they are not searching by product name.

They are describing a need.

They are having a conversation. Most product content is written for search engines, not conversations. Ingredient lists, technical specifications, and product codes do not map to how consumers talk with AI agents.

If enriched product data does not match how a question is asked, the brand does not get found. Brands that submit existing product data directly to Google's Merchant Center Generative AI capabilities feed or OpenAI's affiliate commerce program feed are discovering that generic product listings do not survive large language model retrieval logic.

Descriptions of what something is do not capture how it's actually used, what customers say about it, or the conversational context that determines whether an LLM surfaces it in response to a real consumer query.

And since 40% of AI will run on Agentic AI , it is important to get this one right.

The missing element is not better feeds but context.

How DaVinci Commerce Solves Agentic Commerce Discovery

Accenture has invested in a new player , DaVinci Commerce. This week, DaVinci announced general availability of Agentic BrandStore Enterprise, and the architecture reveals how the category will develop. This came after the CEO and founder evoked “surge” mode for the full team to move quickly on customer feedback!

According to DaVinci Commerce founder Diaz Nesamony, discoverability emerged as the weak point in agentic commerce. 'Agentic commerce is not just another marketing channel,' he explained. 'It is a foundational change in the way consumers are discovering and buying products, comparable only to the early days of Google search. Brands that invest in and reinvent the commerce experience for consumers on LLMs stand to gain significant advantage over those that sit on the sidelines.'

This matters because it reveals where the actual gap sits. When someone searches for a dress, that product data typically does not include information about wedding usage or weather suitability. For luxury brands, consumer questions often center on fashion shows, trends, and what's in style. But standard product descriptions ignore this context entirely.

DaVinci's Content Enrichment Engine transforms raw product data into conversation-ready metadata by drawing from verified customer reviews, social conversations, consumer intent signals, lifestyle content, and real-time data on what consumers are actually asking LLMs. The enriched content then flows through two distinct outputs: enriched discovery feeds submitted to LLM platforms via OpenAI's affiliate commerce program and Google's merchant capabilities, and a branded Agentic Storefront with access to the full enriched dataset.

Through discovery feeds, an LLM receives enough context to surface a brand's products. Through the Storefront's Answer Agent, DaVinci can answer nearly any consumer question accurately, matching it to products because the full enriched dataset remains available in real time, without the character limits that constrain feed submissions.

This architecture matters because it shows that agentic commerce is not a single layer. It is layered, and the brands that dominate will be those that manage both discovery and experience simultaneously. Brands now face two audiences in commerce: the person and the agent acting on that person's behalf. Brands that optimize their offerings for people and AI agents will grow. Those that optimize only for search alone risk becoming invisible to agents.

Nestlé and Nordstrom Lock In Agentic Commerce Advantage

Brands like Nestlé, Diageo, Giant Eagle, and Nordstrom are already deploying on these platforms. Their advantage is not speed of deployment alone. It is a category position. The first major brand a consumer encounters when asking an LLM about a product category shapes the mental model for all subsequent interactions. The brand that gets found first, answers questions most accurately, and delivers the smoothest purchase experience will carry outsized share.

This advantage compounds when multiple LLM platforms reach critical mass.

For example, ChatGPT is already the primary interface. Google Gemini is accelerating adoption, Amazon Alexa for Shopping is experimenting with users, while Walmart Sparky, and Target's AI Shopping Assistant are gaining momentum.

A brand that is production-ready on ChatGPT and Gemini today will have built the operational muscle to scale to retail agents and emerging platforms months before competitors.

Agentic Commerce Infrastructure: Who Controls the Layer

The real prize is not brand-by-brand deployment. It is which platforms become the infrastructure layer for agentic commerce the way Shopify became the infrastructure for direct-to-consumer e-commerce.

DaVinci Commerce is pursuing this with support for ChatGPT, Gemini, Alexa, Sparky, Target, and brand-owned surfaces through a single integration. As these chat surfaces implement interoperable standards such as UCP and ACP, organizations can publish machine-readable, LLM-optimized product content, unlocking new revenue channels within agentic commerce.

The company is also building compliance guardrails, multi-agent content orchestration, and ratings and reviews capabilities that enterprises demand.

What Do You Do About Agentic Commerce

The recommendation from Mark Smith, Partner, Chief AI and Software Analyst for ISG, is for enterprises to prioritize four strategic initiatives:

  • Build structured, AI-consumable product intelligence as the foundation
  • Modernize API and orchestration architecture for agent interoperability
  • Establish governance and trust frameworks for AI-mediated transactions
  • Prepare for AI-driven customer engagement and autonomous commerce lifecycles

What matters going forward is which platform vendors get the most brands to production, ship compliance and analytics features fastest, and ultimately control the data layer underneath the shopping experience. That is where the margin sits.

The brands that master agentic commerce now will define this category for the next decade. DaVinci and Accenture have signaled where the infrastructure layer is heading. First movers in agentic commerce will lock in permanent advantage.