As the digital landscape evolves, the traditional "search and click" model of e-commerce is undergoing a radical transformation. For Revelyst—a powerhouse operator of over a dozen premium outdoor recreation and gear brands—2026 has emerged as a pivotal year in understanding how artificial intelligence is reshaping the consumer journey. No longer just a buzzword, AI platforms like ChatGPT, Claude, and Perplexity are becoming the primary research engines for shoppers, forcing retailers to adapt their strategies to keep pace with a more informed, inquisitive, and "ready-to-buy" customer base.

The New Path to Purchase: From Discovery to Conversion

The fundamental shift in consumer behavior is moving away from broad, keyword-based search engine results toward granular, conversational AI queries. According to Tari Huddleston, Global Vice President of Digital and Ecommerce at Revelyst, the brand has observed a distinct change in how customers interact with product information.

In the past, the path to a product detail page (PDP) was often paved by traditional search engine optimization (SEO) and paid advertising. Today, consumers are using generative AI to conduct deep-dive research into technical specifications, innovation history, and feature sets.

"We’re seeing what the questions are that are being asked around our brand," Huddleston noted in a recent interview. "Whether it’s questions about specific features or the technology embedded in our gear, we are monitoring that shift. It’s no longer just a transactional interaction; it’s a consultative one."

Chronology of the AI Shift

The trajectory of AI-driven traffic in 2026 has been marked by a steady, consistent climb across the retail sector, with Revelyst mirroring these broader market trends:

• Q1 2026: AI referral traffic to major retail sites hovered in the 1% to 3% range. At this stage, retailers were still experimenting with how to position their content to be "digestible" by large language models (LLMs).
• Mid-2026: As adoption of generative search tools surged among mainstream consumers, referral traffic from AI platforms grew to between 4% and 7%. While still a minority share of total traffic compared to social media or traditional search, the quality of this traffic became impossible to ignore.
• Late 2026: Retailers, including Revelyst, began formalizing strategies to capture "AI intent." They observed that consumers arriving via AI were significantly more likely to add items to their carts compared to those arriving via traditional search results.

Supporting Data: Why AI Traffic is "High-Intent"

The most compelling data point for retail analysts is the conversion rate of AI-referred traffic. While AI traffic may still represent less than 10% of total site visits for most retailers, its influence on the "top-of-funnel" discovery phase is disproportionately high.

Unlike a casual browser clicking an ad, an AI-informed consumer has often spent several minutes engaging in a back-and-forth dialogue with an LLM. By the time they reach a Revelyst PDP, they have already validated the product against their specific needs.

"They’re ready to convert," Huddleston explained. "We’re seeing that they’ve done that homework. The AI isn’t just sending them to our homepage; it’s sending them directly to the product detail page that answers their specific query. They are effectively ‘pre-sold’ on the brand by the time they arrive."

Official Responses: Decoding the "Why" Behind the Query

Revelyst is using this data as a feedback loop. By analyzing the questions posed to AI platforms, the company is gaining unprecedented insights into what consumers find confusing or compelling about their outdoor gear.

A case study provided by Huddleston involves power sports helmets—a high-investment category ranging from $400 to $900. When consumers research these products, they aren’t looking for a "quick purchase." They are looking for reassurance and technical validation.

"We noticed users asking specific questions about the internal padding of our helmets and how speaker systems integrated with the safety liner," Huddleston shared. "We wouldn’t have known that was a key feature to highlight in our marketing copy or technical specs unless we had been watching those AI conversations. It’s helping us improve our PDPs to better align with the customer’s actual decision-making process."

Implications for the Outdoor Retail Industry

The rise of AI-driven discovery carries several critical implications for how companies like Revelyst must structure their digital ecosystems moving forward:

1. The Death of Generic Content

Retailers can no longer rely on standardized, generic product descriptions. AI models prioritize content that provides clear, specific answers to complex questions. If a product description doesn’t explicitly mention how a jacket’s breathability performs in sub-zero temperatures, an AI might ignore that product in favor of one that provides the data.

2. The Long-Term Investment Mentality

Outdoor recreation gear is often a "high-consideration" purchase. Huddleston points out that unlike a replacement household item, gear purchases involve research, price comparison, and safety evaluation. Retailers who successfully optimize their data for AI will capture the customers who view their purchases as long-term investments rather than impulsive buys.

3. The New SEO: "Answer Engine Optimization"

The industry is shifting from traditional SEO to "Answer Engine Optimization" (AEO). This requires a shift in technical strategy: ensuring that product feeds are structured, metadata is rich with technical specs, and brand content is accessible to LLM crawlers. For Revelyst, this means treating every AI interaction as a potential customer service touchpoint.

4. Directing the Narrative

While AI can provide accurate information, it can also hallucinate or provide outdated specs if the brand’s digital footprint isn’t managed correctly. By proactively feeding accurate, high-quality information into the digital ecosystem, Revelyst is ensuring that when a customer asks an AI for the best helmet for a cross-country motorcycle trip, their brands are at the top of the recommended list.

Looking Ahead: The Future of Retail Engagement

As we look toward the end of 2026 and into 2027, the role of AI in e-commerce is expected to solidify. While AI referral traffic may remain a smaller portion of the total traffic mix for some time, its role as a "conversion catalyst" is firmly established.

For brands like those under the Revelyst umbrella, the strategy is clear: be present where the conversations are happening, listen to the specific technical inquiries of the customer, and refine the digital product experience to reflect the sophistication of today’s AI-empowered shopper.

The "homework" that customers are doing via AI is effectively shortening the sales cycle for high-ticket items. Retailers that embrace this shift—viewing AI not as a threat to their brand sites, but as an advanced concierge service for their customers—will be the ones that thrive in the next era of digital commerce. As Huddleston noted, the discovery phase is not a short window, but by capturing the nuance of the consumer’s questions, brands can ensure they are the ones holding the answer when the customer is finally ready to buy.