The AI Shift: How Revelyst is Decoding Consumer Discovery in the Age of Generative Search

As the digital landscape evolves, the way consumers discover and research products is undergoing a seismic shift. For Revelyst, a parent company overseeing more than a dozen prominent outdoor recreation and gear brands, this transformation is defined by the rise of generative artificial intelligence. By 2026, the retail giant has begun to treat AI platforms—such as ChatGPT, Claude, and Perplexity—not just as third-party tools, but as critical windows into the consumer psyche.

According to Tari Huddleston, global vice president of digital and e-commerce at Revelyst, the company is actively monitoring how potential customers interact with AI to understand the intricate specifications and innovative technologies embedded within their product lines. This transition represents a departure from traditional, purely transactional web traffic, signaling a new era of "intelligent discovery."

Main Facts: The AI-Driven Path to Purchase

The core revelation for Revelyst is that the modern consumer journey no longer starts exclusively on a brand’s landing page. Instead, it begins in the conversational interfaces of large language models (LLMs). Consumers are turning to these AI platforms to conduct deep-dive research, asking nuanced questions about product features, technical capabilities, and value propositions before ever clicking through to a retail site.

Huddleston notes that this phenomenon is effectively "top-of-funnel" discovery. While the volume of traffic originating from these sources remains a relatively small percentage of overall site visits, the quality of that traffic is significantly higher. Consumers arriving via AI referrals are often more informed, having already vetted the product through a digital assistant. Consequently, these users are more likely to add items to their carts and convert, as they have effectively completed their "homework" before reaching the brand’s product detail pages (PDPs).

Chronology of an Evolving Digital Strategy

The integration of AI into the retail funnel did not happen overnight. The progression can be categorized into three distinct phases witnessed throughout 2026:

Early 2026 (The Emergence): Retailers began noticing a trickle of traffic stemming from AI referrals. At this stage, traffic from these sources hovered between 1% and 3%. Brands were largely passive, observing the trend without fully understanding the intent behind the queries.
Mid-2026 (The Expansion): As LLMs became more integrated into browsers and search engines, referral traffic doubled and, in some cases, tripled. By the second half of the year, retailers like those under the Revelyst umbrella saw AI-driven traffic rise to the 4% to 7% range.
Late 2026 (Strategic Optimization): Retailers moved from observation to action. Revelyst, in particular, began using AI-generated data to identify gaps in their own product documentation. By tracking the questions being asked on external AI platforms, the company started refining its internal content strategy to better align with the language and information needs of its customers.

Supporting Data and Market Benchmarks

Data provided by industry analysts supports the trends Huddleston has observed. While AI-driven referral traffic remains under the 10% threshold for most major retailers, its trajectory is undeniably upward.

The importance of this traffic lies in its conversion potential. For high-ticket items—such as the power sports helmets mentioned by Huddleston, which can range from $400 to $900—the decision-making process is lengthy. Unlike a commodity purchase, these investments require technical verification. Revelyst’s data suggests that consumers who utilize AI to research these high-cost items arrive at the e-commerce store with higher intent.

Because AI responses often link directly to specific PDPs rather than generic homepages, the consumer is placed exactly where they need to be to complete a purchase, reducing the friction typically found in a multi-step navigation process.

Official Responses: Insights from Revelyst Leadership

Tari Huddleston’s perspective underscores the necessity of a "listening" culture in modern e-commerce. "We’re seeing what the questions are that are being asked around our brand," Huddleston stated. "It’s not all transactional-based anymore."

Huddleston highlighted that the company is specifically watching for questions about technology and innovation. By monitoring these queries, Revelyst can identify which features resonate with consumers and which features remain misunderstood.

A prime example cited by Huddleston involves the internal components of a helmet. A consumer might ask an AI, "How do the internal pads of this specific helmet accommodate speaker systems?" If this information isn’t clearly displayed or searchable on the product page, the brand loses an opportunity. Huddleston noted, "We would not have known that that was a feature that we need to call out and be able to make sure was searchable… unless we had been watching and seeing what type of conversations were happening inside the space."

This proactive approach turns AI into a market research tool, effectively crowdsourcing the information consumers value most. By integrating these insights into their product descriptions and metadata, Revelyst ensures that their site content evolves in tandem with consumer curiosity.

Implications for the Future of Retail

The implications of this shift are profound for the broader e-commerce industry. As consumers rely more heavily on AI to filter choices, the traditional SEO (Search Engine Optimization) playbook is becoming secondary to "AIO" (Artificial Intelligence Optimization).

1. The Death of the Generic Search

As consumers move away from keyword-based search engines toward conversational AI, the ability to answer complex, multifaceted questions will become the primary competitive advantage. Brands that fail to provide clear, technical, and accurate information in a format that AI can ingest will find themselves excluded from the "recommendations" provided by these tools.

2. High-Ticket Research Dynamics

The "long-term investment" purchase behavior noted by Huddleston suggests that AI is particularly potent for categories requiring deep research. As products become more technologically advanced, the gap between a standard product description and the information a consumer actually needs will widen. Companies that leverage AI to bridge this gap will likely see higher conversion rates for premium products.

3. The Need for Structured Data

To remain visible in an AI-first world, retailers must invest in structured data. If a brand wants its product features to be correctly identified and recommended by an AI, that data must be machine-readable. Revelyst’s focus on ensuring features are "searchable across the product" is a clear acknowledgement that visibility in the AI age requires rigorous digital housekeeping.

4. A Shift in Marketing Spend

While Huddleston notes that "many other channels" still drive the majority of traffic, the efficiency of AI-driven traffic is forcing a re-evaluation of marketing budgets. If a consumer arrives at a site via an AI recommendation, they have essentially been "pre-sold" by the machine. Brands may soon find that investing in the quality of their digital product content is more cost-effective than traditional paid search or social media advertising.

Conclusion: Adapting to the Conversational Commerce Era

The transition to AI-driven product discovery is not merely a technical update; it is a fundamental shift in the relationship between brands and consumers. By embracing the role of AI as a mediator in the research process, Revelyst is positioning itself to lead in an environment where information is the ultimate currency.

For the rest of the retail industry, the message is clear: the consumer is already using AI to decide what to buy. The question for retailers is no longer "How do we get them to our homepage?" but rather "How do we ensure that when the AI is asked, our product is the one it recommends?" Through the strategic use of consumer query data and a commitment to refining product information, companies like Revelyst are proving that in the age of generative search, the best way to lead is to listen to the questions being asked.