If an AI assistant recommends my company’s product, it has made a promise on our behalf. Someone asked for help with a decision, and the assistant concluded that we were a good answer. Now the product has to justify that confidence. As a branding director, I find that far more interesting than finding yet another way to get our name into an automated recommendation.
Shopify’s October 13 announcement of Shop MCP makes that question more immediate. It gives compatible agents a single connection through which they can search Shopify’s catalog, build carts, and move toward checkout. A signed-in shopper can use saved Shop Pay details, and the purchase is completed only after the shopper confirms the total and payment method. The distance between asking for a recommendation and acting on it is getting shorter.
Shoppers were already traveling that distance before this release. Adobe reported that traffic from AI sources to U.S. retail sites grew 393% year over year in the first quarter of 2026, and that by March those visitors were converting 42% better than other traffic. For brands, this means another participant has joined the decision. A customer might meet a product through an assistant’s explanation before spending much time with the brand’s own presentation. Accurate product information becomes essential. It also raises a harder question: how much of a customer’s reason for choosing a brand can the assistant actually see?
A visible brand and a preferred brand can look different
Working in gaming PCs, I have repeatedly run into a gap between the brands AI assistants recommend and the competitive picture I see in retail sales reports. The recommendations I received in the past often favored familiar multinational computer brands. The reports I review at CyberPowerPC show how consequential specialist companies like ours are to actual purchasing behavior. Those are two windows into the same market, and they can leave very different impressions.
An assistant cannot use market evidence it cannot access. It may have abundant material about one company and much less about another, and a brand with extensive media coverage is simply easier to describe with confidence. Being easy to describe, however, is not the same as being the brand customers most often choose, or the right choice for the person asking. A recommendation system that leans too heavily on available publicity risks mistaking the distribution of information for the distribution of demand.
What interests me is what happens when the system starts learning from purchases. Suppose an assistant ranks ten brands, and its first recommendation sells far less than its last. That should prompt an investigation. What are buyers responding to that the ranking missed? Price and availability might explain it. When a more expensive product keeps winning, though, the interesting work begins, because something else is contributing to the value people perceive.
Sales are evidence that someone committed money to a choice. They are where I would start, and then I would work backward to identify the conditions that consistently predict demand. Tracking a recommendation through to a purchase could help, although any single platform would still see only part of the market. It would also need to account for its own influence, since recommending something more often gives it more opportunities to sell. Otherwise the assistant may end up measuring the effect of its own advice and calling it consumer preference.
The specifications do not contain the whole purchase
In gaming, the components inside the machine can carry meaning beyond their specifications. An enthusiast may recognize a motherboard manufacturer, care deeply about the choice of graphics card, or simply appreciate knowing which parts went into the system. To that buyer, recognizable components signal knowledge and membership in a community. They help explain why someone might feel very differently about two computers whose product summaries look nearly identical.
None of this makes every specialist system better than every computer from a large manufacturer. Performance and quality have to be judged configuration by configuration. The point is that a buyer’s reasoning can include confidence in particular components, the ability to understand the build, and the social meaning of owning it. A recommendation that treats all of that as interchangeable can miss what the customer is actually buying.
I see the same issue with community. At CyberPowerPC, our work around gaming, esports, anime, and fashion gives people ways to encounter the company beyond a product listing. Some of that relationship develops at events, and some of it develops in conversations between friends. A person may trust a brand because someone they trust has lived with it for years. The assistant might find a specification sheet and a review while missing the conversation that made the recommendation persuasive in the first place.
Online discussions can supply clues. Comments and community threads may reveal why a brand feels credible, what disappoints people, and which details matter to them. Still, those conversations are a partial view. The useful ambition is to connect what people say with what they choose and what they experience afterward, while staying curious about what the available information fails to explain.
One argument I make in Bigger, Smarter, Stronger Brands is that a brand needs several ways to sense what is happening around it. Customer behavior, employee observations, market research, and cultural signals each reveal something the others might miss. That matters here because an AI assistant can have an enormous amount of information and still have an incomplete picture of demand. If its recommendations favor one brand while purchases consistently favor another, that disagreement deserves investigation. The value of the feedback comes from using it to question the recommendation and understand what customers see that the system has missed.
For a brand, that makes evidence worth organizing. Explain the components, document performance fairly, show how support works, and tell specific stories about community participation. Those materials give both customers and assistants a better basis for evaluating the promise. Repeating that a brand is authentic tells them very little about where the authenticity comes from.
Better recommendations would change where I invest
If AI became reliably better at recognizing real demand and explaining its causes, I would put more effort into the product itself. I would want to understand which experiences make a recommendation hold up and which failures undermine it. If the system consistently surfaced a problem with reliability, setup, or support, that information would become a brief for improvement.
The assistant and the brand end up depending on each other. The assistant earns trust by helping someone choose well, and the brand earns trust by delivering what was recommended. A disappointing purchase can damage either relationship, depending on whether the advice was wrong, the product fell short, or the customer misunderstood the tradeoff. We have a direct business interest in making the assistant’s confidence look sensible after the box is opened.
Living up to that promise does not require winning every specification comparison. People knowingly accept a higher price or a compromise because they value the design, the community, the experience, or what the brand represents. Those benefits are part of the product’s value to that person, and they deserve to be understood accurately, with the tradeoff in plain view. A brand’s meaning can make a choice worthwhile without making the product technically superior on every measure.
Shopify’s announcement gives agents tools to move a transaction forward. It does not establish that those agents understand all the reasons behind a preference. For businesses preparing for this kind of shopping, the immediate work is twofold: make the offer easy to evaluate, and make the experience strong enough to deserve the recommendation. Easier access to checkout raises the stakes on both.
Creativity still gets to change the answer
There is another possibility I keep coming back to. If more businesses use similar AI tools for research, pricing, and strategy, their conclusions may start to converge. Companies could become very good at producing the answer that existing data says people want. That would make creative judgment more consequential, because someone still has to propose something the market has not already explained.
Music recommendations offer a useful analogy. I listen across genres, and a good recommendation can connect interests I would never have connected myself. But a listening history is not the boundary of what a person could enjoy. Something unexpected can change their taste. Product markets work the same way. A new design, a collaboration, or a cultural association can make people care about something they previously ignored.
This is why I resist the idea that shared access to AI will erase competitive advantage. People still make different connections, read weak signals differently, and choose different risks. Cultural participation can help a brand notice a possibility before the sales data makes it obvious. Sometimes that judgment will be wrong. The job is to make a considered bet, learn from the response, and recognize when something new is happening.
The most interesting brands may sometimes succeed by doing something a recommendation model would not have predicted. Once customers respond, that behavior becomes evidence the system can learn from. In that sense, creativity keeps feeding the market new information, which is a pleasant job description for anyone in marketing.
Shopify is helping bring the shopping assistant closer to the purchase. Brands should prepare by making their value clearer and their delivery more dependable. The customer remains the person whose expectations matter. If an assistant helps them find us, our responsibility begins with proving that the recommendation was worth trusting.