Key Takeaways:
- Brands with incomplete product data are eliminated from AI search recommendations entirely. Not downranked. Eliminated.
- Sixty-eight percent of consumers used an AI shopping tool in the past three months, mostly to compare brands before buying.
- Clean data is not enough. It also has to be connected across your product information management (PIM), customer relationship management (CRM), and commerce systems.
What Surf and Outdoor Brands and Retailers Need to Know to Win at AI-Powered Shopping
If data is the oil of the 21st century, the rise of AI-powered shopping is about to expose a serious energy crisis. Surf, outdoor, and action sports brands are not immune.
The rules of product discovery are changing fast. It’s no longer enough to have solid basic product data sitting on a retailer’s site, findable by keyword. Agentic AI search, the kind where a shopper asks a question in plain language and gets a direct recommendation instead of a page of results, demands something more. Your data has to be complete, clean, and structured in a way that a machine can read, parse, and act on in seconds.
The stakes are higher than most brands realize. When a consumer asks an AI agent to find a wetsuit for cold Pacific water, or a pack that works for both trail running and weekend travel, the AI either surfaces your product or it doesn’t. There is no close second.
“In traditional search, having bad data hurt the rankings of individual products,” said Stuart Klein, Partner in the Digital and Analytics practice of Kearney in an interview with SESO sister brand Retail TouchPoints. “An agentic search doesn’t return a page of rankings; it gives a recommendation based on what it knows about you, what your interactions have been. That’s why people like it. It’s very prescriptive with its recommendations, and it’s returning them in a completely new format that allows (people) to visualize the product and interact with the recommendations.”
The margin for error has essentially disappeared. “In this case, having bad data completely eliminates you from the consideration set,” Klein said.
And while agentic AI is not yet driving a large share of purchase volume, it is already capturing significant research volume, which means it is shaping which brands consumers even consider before they buy. An April 2026 McKinsey survey found that 68% of consumers have used an AI-powered tool in the past three months, with 62% using these tools to compare brands, models, prices, and reviews. That is the discovery phase. That is where your brand either enters the conversation or gets left out of it entirely.
The Data Problem Goes Deeper Than Most Brands Think
Clean product data is the starting point, but it is not the whole picture. The harder question is whether a brand’s data is connected internally and whether it tells one coherent story across systems.
“Retailers need to ask themselves key questions,” said Nick Kramer, Principal, AI and Applied Solutions at SSA & Co. “Is all of your data connected? Does all of your data tell one story? You might have 30 to 40 product attributes in a PIM solution and 100 attributes in a CRM solution — are they connected? And does the data know where the product can actually be purchased — a store, online, or on Amazon? And is this data connected to the product and customer segmentation attributes? From what we see, the answer too often is no.”
Kramer noted this is not a new problem, but it has become far more urgent. He pointed to a master data project SSA completed for a major clothing retailer more than a decade ago. The issue then was not conflicting data. It was too little data, siloed in pockets and not communicating across systems. That problem, he said, is still widespread in retail today.
The tools to solve it have improved significantly. AI has lowered the barrier to collecting, organizing, and analyzing data over the past three to five years. But the tools alone are not enough. “You will have to dramatically scale your data engineering capabilities,” Kramer warned. “I can walk into any client and they’ll say the demands on their data are much higher, as is the frequency of change and the frequency of iteration. Every data engineering team I see is swamped, and because retail has traditionally underinvested in this type of infrastructure, this problem will really separate the winners from the losers.”
External Data Is Part of the Equation Too
Beyond internal data hygiene, AI-powered search also draws from a much wider pool of inputs: social content, influencer activity, media coverage, and user reviews all factor into what gets recommended and how.
Klein breaks it down into three layers brands need to think about: “There’s structured product data, the facts. There’s behavioral data, such as what’s happening on Reddit or TikTok, reflecting what people are doing in relation to a brand or product. Then there are trust and contextual signals. Those come from how others, for instance influencers and the news, interact with and comment on the brand.”
That last layer is largely outside any brand’s direct control, and that is part of a larger shift in power these analysts see playing out. “Agentic commerce is undermining the brand’s ability to control the message,” said Kramer. Where email marketing once gave brands a direct, controlled channel to consumers, AI has flipped that dynamic. “Now, AI has empowered the consumer to search on their terms. They’re using their language to find stuff. Now they’re saying, ‘I want this, this is what I’m looking for.’ So now it’s not enough just to describe a product; you need to fit the product with the customer’s experience.”
For action sports brands, where product use cases are contextual and lifestyle-driven by nature, that last point carries real weight. A surfer is not just searching for a wetsuit. They may be searching for a wetsuit for particular water conditions. The brands whose data anticipates that kind of query and connects the product to that experience are the ones that will show up.
“You need to create the opportunity for AI agents to make connections that you haven’t explicitly made,” Kramer said. “And that’s a much harder problem.”
The window to get ahead of it is open. But it will not stay that way.
What Brands and Retailers Should Do Now to Show Up in LLMs
Based on the experts interviewed for this story, here are the data priorities brands should be addressing:
- Audit whether your product data is complete and consistent across every system that touches it, including PIM, CRM, and clienteling platforms. If those systems are not talking to each other, your data is not telling one story.
- Confirm your data reflects exactly where each product can be purchased: in-store, online, on Amazon, or through other channels. Gaps here can remove you from AI recommendations entirely.
- Connect product attributes to customer segmentation data so AI agents can match your products to the right shopper context, not just the right keyword.
- Invest in data engineering resources. Experts warn that the frequency and volume of data demands are increasing sharply, and brands that have underinvested in this infrastructure will feel it.
- Monitor your external data signals: reviews, social mentions, influencer content, and press coverage. These inputs feed AI recommendations and are increasingly outside your direct control, making active reputation and content management more important than ever.
- Think beyond product descriptions. AI search rewards brands that connect products to experiences and use cases. Build your data with the customer’s context in mind, not just product specs.
