As the digital landscape shifts from traditional search engines to conversational artificial intelligence, the way consumers discover, research, and purchase outdoor gear is undergoing a profound transformation. Revelyst, a parent company managing over a dozen specialized outdoor recreation brands, has found itself at the center of this evolution. Throughout 2026, the company has observed a marked increase in AI-driven product discovery, prompting a strategic pivot in how they analyze consumer intent and optimize their digital storefronts.
Tari Huddleston, Global Vice President of Digital and Ecommerce at Revelyst, notes that the modern buyer journey is no longer confined to brand-owned websites. Instead, potential customers are turning to Large Language Models (LLMs)—including ChatGPT, Claude, and Perplexity—to conduct deep-dive research into product specifications, technical innovations, and feature sets long before they reach a checkout page.
The Evolution of the Digital Funnel
For years, the standard ecommerce model relied on search engine optimization (SEO) and paid social media to drive traffic to product detail pages (PDPs). However, 2026 has marked a distinct departure from this paradigm. As AI platforms become more integrated into the daily workflow of consumers, they are acting as "digital concierges," synthesizing complex product data and providing answers that were previously buried in long-form manuals or dense website descriptions.
Huddleston emphasizes that the nature of these inquiries is changing. "We’re seeing what the questions are that are being asked around our brand," she says. "It’s not all transactional based anymore." Consumers are increasingly using AI to solve specific technical problems or to validate their research on high-ticket items, moving beyond simple price comparisons to granular queries about material durability, technological integration, and compatibility.
A Chronology of the AI Integration
- Early 2026: Retailers across the sector began reporting a baseline of 1% to 3% of total website traffic originating from AI-driven referral sources.
- Mid-2026: As LLMs gained widespread adoption, the influence of these platforms became undeniable. Retailers saw a rise in referral traffic, with many brands experiencing a jump to 4% to 7% by the second half of the year.
- Current State: Revelyst has identified a specific trend: while AI traffic remains a smaller percentage of overall volume compared to traditional channels, the quality of that traffic is significantly higher. Consumers arriving via AI are bypassing the homepage and landing directly on product-specific content, indicating a higher propensity to convert.
Supporting Data: Quality Over Quantity
While AI-driven traffic has not yet eclipsed traditional organic search or social media marketing, the data points to a shift in consumer behavior that cannot be ignored. For Revelyst, the most telling metric is the conversion rate of AI-referred users.
Data collected across the industry shows that while AI referrals generally account for less than 10% of total site traffic, the individuals arriving from these platforms are "primed" for purchase. Because the AI has already facilitated the "homework" phase of the buyer’s journey—answering questions about product features, comparing technical specs, and verifying compatibility—the consumer arrives at the brand’s PDP with high intent.
"They’re ready to convert," Huddleston notes. "We’re seeing that they’ve done that homework and they’re ready to make that purchase." This suggests that AI is successfully handling the "top-of-funnel" discovery, effectively shortening the time it takes for a consumer to move from initial interest to a completed transaction.
The High-Investment Research Model
The reason AI has become so instrumental for Revelyst lies in the nature of their product catalog. Outdoor recreation gear, particularly in the powersports and performance equipment sectors, often represents a "sizable investment."
Huddleston highlights the example of a powersports helmet, which can retail anywhere from $400 to $900. For an item at this price point, a consumer will rarely make a spontaneous decision. They require validation, technical verification, and, often, proof that the product will integrate with their existing gear.
"I think the reason why it’s still small is because the discovery phase for the consumer, for our products, is not a short window," Huddleston explains. "It’s not like someone’s deciding: ‘Hey, I need a replacement of something’—which is a quick purchase. It’s a long-term investment."
In this context, AI serves as an objective research tool. Consumers use it to ask complex questions, such as how internal padding affects ventilation or how integrated communication speakers factor into the helmet’s overall profile. These are specific, nuanced questions that traditional static web pages might not clearly address unless the company has proactively optimized for such intent.
Official Response: Leveraging AI for PDP Optimization
The insights gained from monitoring AI conversations are being fed directly back into Revelyst’s product development and marketing teams. By listening to the questions users are asking LLMs, the company is learning exactly where its existing web presence falls short.
For instance, if AI logs reveal that a significant number of users are asking about the compatibility of speakers within a specific helmet model, Revelyst can immediately update its PDPs to explicitly feature that information. This creates a virtuous cycle: the company uses AI data to improve content, which in turn makes the brand more "discoverable" and "answerable" for the next wave of AI-driven queries.
"We would not have known that that was a feature that we need to call out and be able to make sure was searchable across the product unless we had been watching and seeing what type of conversations were happening inside the space," Huddleston says.
Strategic Implications for the Future of Retail
The implications for the broader retail industry are clear: the era of "passive" product descriptions is ending. Brands that fail to provide rich, technical, and easily parsed data will find themselves invisible in an AI-driven discovery ecosystem.
Key Takeaways for Retailers:
- Direct-to-PDP Traffic: As AI continues to refine its ability to provide answers, consumers will increasingly bypass homepages. Brands must ensure that their product detail pages are comprehensive, data-rich, and optimized for both human readability and machine-learning ingestion.
- The New "Search" Intent: Keywords are being replaced by "conversational intent." Retailers need to map out the common questions their customers have—beyond just product names—and ensure that their content infrastructure supports these conversational queries.
- Data as a Strategic Asset: The information that AI provides about what consumers want to know is as valuable as the transaction itself. Companies should treat AI referral logs as a primary source for market research and product feature prioritization.
- Managing the Long-Tail Funnel: For high-ticket items, AI will become the primary vetting tool. Retailers must provide high-quality documentation, FAQs, and technical specs that allow AI to build confidence in the product on the brand’s behalf.
As Revelyst continues to monitor this space, the company’s approach serves as a blueprint for other retailers navigating the same transition. The goal is no longer just to be the first result in a list of blue links; the goal is to be the primary, authoritative source of information that AI models cite when a potential customer asks, "Is this the right product for me?"
In the race to capture the AI-informed consumer, the winner will be the company that best understands that every query is a signal—and every signal is an opportunity to refine the brand’s digital presence for the future. As we look toward the end of 2026 and into 2027, one thing is certain: the conversation between the consumer and the machine is just beginning, and the retailers who lean into this dialogue will be the ones who lead the market.
