The Subtle Shift as Agentic AI Reshapes D2C Marketing by 2027

For years, D2C marketing strategies have followed a fairly straightforward playbook: get noticed, bring people to your website, give them a reason to buy, and build a relationship that brings them back.

Agentic AI could change what happens before it.

These AI systems can research products, compare options, and, with the permissions a shopper gives them, act on their behalf. They don’t necessarily replace the shopper or make the final decision. But they can influence which brands make it into the conversation in the first place.

Imagine someone looking for running shoes. Instead of opening several websites, they tell an AI agent about their running habits, foot type, and budget, and ask it for a shortlist. They still choose. But the options they see may already have been filtered.

For D2C brands, that’s the shift worth paying attention to. Getting a consumer to choose you is only part of the job now. You also have to be considered before that choice is made.

Where We Actually Are: Assisted, Not Yet Autonomous

It’s worth separating what’s happening today from what might happen next. McKinsey’s automation curve runs from AI helping with research to agents buying independently. Our simplified version has four stages. In the first, a shopper asks AI for recommendations and buys on a website. In the second, an agent builds a shortlist from the shopper’s brief. In the third, it picks an option for the shopper to approve. In the fourth, it completes purchases within the permissions the shopper has set.

The first stage is already picking up. Adobe Analytics found that traffic to US retail sites from generative AI sources grew 693.4% year on year over the 2025 holiday season, and AI referrals converted 31% better than other traffic. That’s a turnaround: earlier in 2025, Adobe found AI-referred visitors converting 9% worse. These figures measure referrals, though, not autonomous purchases.

Bain & Company found that 30–45% of US consumers use generative AI for product research or comparison, while around half are cautious about letting it handle a purchase from start to finish. McKinsey projects up to $1 trillion in US B2C retail revenue through agentic commerce by 2030, and $3–5 trillion globally. Those are forecasts, not results, but they explain the attention.

So AI is already influencing discovery. Handing it the transaction is a different step, and shoppers are still deciding whether to take it.

Four Shifts D2C Marketers Need to Understand

1. Search Becomes Specification

Traditional search often starts with a keyword. AI-led discovery can start with a much more specific brief: running shoes for flat feet, three 5K runs a week, under ₹10,000, no preferred brand.

That changes what a product needs to communicate. The product information has to say what the product does, who it’s for, and how it compares with alternatives. The more clearly a brand answers the shopper’s actual requirements, the easier it is for an AI system to see why it’s relevant.

2. Persuasion Becomes Legibility

A persuasive product page still matters, but it may not be the first thing an AI system reads. Structured product data helps these systems interpret details accurately. OpenAI’s product-feed documentation, for instance, explains how catalogue information such as pricing, availability and seller details supports product discovery.

The wider footprint counts too. Reviews, expert recommendations, FAQs and editorial coverage all shape how a brand is understood. That doesn’t mean writing for machines instead of people. It means being specific and consistent enough that both can see what makes the product relevant.

3. The Customer Relationship Gets Another Layer

D2C was built on bringing brands closer to consumers, often by cutting out the intermediary. Agentic commerce could add a new one: an AI system acting for the consumer. That doesn’t automatically mean brands lose the relationship. Shopify says merchants can remain the merchant of record and keep control of their customer relationships and data. But the discovery conversation may happen somewhere the brand doesn’t own, so brands will want to understand how AI-assisted journeys feed into attribution and acquisition.

4. Trust Becomes Permission

People may be happy to ask AI to compare moisturisers long before they’re happy to let it spend ₹5,000 on one. McKinsey notes that even with simple rules-based automation, consumers delegate when it is reliable and reversible. How far people go beyond that will likely depend on the category, the price, and how risky a purchase feels. Low-risk repeat purchases are the likeliest to be delegated first, though that’s our inference, not a finding.

Trust still has to be earned through clear product information, credible claims, reliable fulfilment and easy returns. The difference is that those signals now have to reassure the shopper and the system helping them decide.

Why Brands Still Have a Say

None of this is settled. OpenAI introduced Instant Checkout in ChatGPT in September 2025 through the Agentic Commerce Protocol, built with Stripe. In March 2026, it deprioritised it as a standalone feature, with merchants using their own checkout while product discovery stayed the focus. Shopify confirmed that products remain discoverable in ChatGPT while purchases complete in the merchant’s own store.

Access is contested too. The American Enterprise Institute notes that Amazon has repeatedly updated its robots.txt to block AI agents, and eBay has added explicit agent restrictions. Others are building shared infrastructure. Shopify says the Universal Commerce Protocol, which it co-developed with Google, is an open standard for how agents transact with merchants, backed by Walmart, Target, Etsy, Stripe and Visa.

For D2C brands, the practical response is to avoid betting on a single platform: keep product data accurate and choose deliberately which systems get access to your catalogue and transactions.

The Same Shift, Seen from India

UPI processes more than 20 billion transactions a month, so digital payments are already routine. In October 2025, NPCI, Razorpay and OpenAI piloted UPI payments inside ChatGPT, with BigBasket among the first merchants. A similar pilot came to Claude in February 2026, with Zomato, Swiggy and Zepto involved.

None of this proves Indian shoppers will adopt autonomous buying faster than anyone else. But mobile-first habits, UPI and quick commerce make repeat purchases of everyday items worth watching.

D2C Marketing Strategies to Build Before 2027

At Ittisa, we see five areas brands can work on now:

• Machine-readable: Keep attributes, variants, pricing, availability, and policies in a structured, up-to-date format.
• Machine-understandable: Keep claims the same across owned channels. Support them with trustworthy content and third-party validation.
• Recommendation-ready: Explain who a product is for, what problem it solves, and where it’s better or worse than alternatives.
• Transaction-ready: Keep feeds, inventory, checkout and returns information correct, and choose which agents and platforms can access them.
• Human-led: Keep investing in community, loyalty, service and brand identity. These give people a reason to choose you beyond a list of features.

Take a shampoo. “Sulphate-free” is useful, but it says little. A better description would say it’s made for dry, curly hair, is silicone-free, and suits colour-treated hair. It would also give the pack size, price, availability and delivery details.

[Ittisa example or insight to be added here]

Most of this isn’t new. Good product data, positioning and a reliable experience have always mattered. Agentic discovery simply makes it more costly to leave them incomplete.

The Takeaway: From Being Discovered to Being Trusted

D2C was built on cutting out the middleman. Agentic AI may bring a middleman back, but this one works for the shopper, not the retailer. The shopper still makes the choice, but more and more it comes from a shortlist that someone, or something, has already made.

That moves the first step of the journey upstream, into conversations where AI helps people compare, narrow down, and shortlist. Being seen there is only the start. How much shoppers delegate depends on trust: clear information, credible claims, reliable delivery, and easy returns.

The brands best placed for 2027 will earn both. They’ll be easy for AI to find and understand, and worth the shopper’s confidence when the final choice is theirs.

Frequently Asked Questions

Will AI agents replace traditional search for product discovery? Not necessarily. AI-led discovery is emerging as another way to research and compare products. Adobe’s referral data shows traffic from generative AI, but not that traditional search is being replaced.

Does agentic commerce mean D2C brands will lose their customer relationships? Not automatically. Some models let merchants keep control of checkout, customer data, and the merchant relationship. But discovery may happen on third-party AI interfaces, which makes measurement and attribution harder.

What product information should D2C brands optimise for AI discovery? Start with accurate attributes, prices, availability, delivery, and returns information. Make it clear who a product suits, and back claims with credible reviews.

Should D2C brands start adapting their marketing strategies for agentic AI now? Yes, particularly at the discovery stage. Audit product data, monitor AI-referred traffic where possible, and test improvements on a focused range. The aim is readiness, without assuming autonomous shopping is already mainstream.

Does brand building still matter if AI recommends products? Yes. Product data helps a system judge relevance, but trust, recognition, and customer experience still count. The challenge is to make a brand’s value clear to people while keeping its products easy for AI to understand.