Zainab Hussain is a distinguished e-commerce strategist with a deep background in operational management and digital engagement. As the landscape of retail shifts from traditional search engines to conversational artificial intelligence, her expertise has become essential for brands trying to navigate the complexities of modern consumer behavior. In this discussion, we explore the rise of “agentic commerce,” investigating how brands can maintain visibility when algorithms—not people—are the primary gatekeepers of product discovery. We dive into the surprising growth of AI-driven traffic, the technical hurdles of no-click searches, and the strategic pivot from passive reporting to active, automated optimization.
With roughly 75 percent of new product searches now occurring inside large language models, how is the traditional concept of “discoverability” being rewritten for modern retail brands?
The shift we are seeing is more than just a change in platform; it is a total transformation of the consumer’s journey from discovery to checkout. For years, brands focused on SEO keywords and visual placement, but when 75 percent of searches happen inside a conversational AI, the criteria for success change overnight. You are no longer competing for a spot on a list of links; you are competing to be the singular answer provided by a specialized agent. If the AI doesn’t “understand” your product’s value proposition or technical specs, your brand simply ceases to exist in that consumer’s world. It creates a high-stakes environment where being second best is the same as being invisible, forcing teams to move beyond static metadata into dynamic, agent-ready product data.
The data shows that traffic from AI-driven sources has surged by 4,700 percent year over year, yet 60 percent of these interactions end without a click. How can brands reconcile this “no-click” reality with the need for conversion?
This is perhaps the most frustrating paradox for modern marketers: seeing a 4,700 percent explosion in traffic while watching the majority of those users stay within the AI’s interface. When an AI assistant answers a query directly, it often satisfies the user’s need without them ever touching the brand’s actual website, which explains that high 60 percent drop-off in traditional click-through rates. However, the traffic that does make it through is incredibly high-intent, converting at a rate 5 to 8 times higher than organic Google searches. The key is to stop viewing the “click” as the only metric of success and start ensuring that the “answer” the AI provides is so comprehensive and persuasive that the subsequent transaction becomes inevitable. Brands must focus on the quality of the information fed into these models so that when a user finally does click, they aren’t just browsing—they are ready to buy.
Andrew Lissimore mentioned that the commerce tools of the last decade were built for reporting rather than fixing. In an “agentic” world, what does it actually look like for software to take action on behalf of a brand?
In the past, an ecommerce manager would spend their morning looking at a dashboard, identifying a drop in visibility, and then manually updating product descriptions or backend tags—a process that is far too slow for today’s market. Andrew Lissimore’s vision with Lantern is to close that loop by deploying specialized agents that don’t just point at a problem but actually execute the solution. These agents monitor how products are interpreted by different AI models in real-time and proactively apply changes to product pages and catalogs to improve visibility. This means the human team moves into an “approval” role, overseeing the strategy while the software handles the heavy lifting of adjusting data structures. It’s about moving at the speed of demand rather than the speed of a manual update cycle, allowing a brand to remain relevant even as the underlying AI protocols shift.
With the infrastructure of AI-driven commerce still being built, including new standards like the Universal Commerce Protocol, how should businesses prepare for a market that has no fixed workflows?
The most dangerous thing a brand can do right now is commit to a rigid, fixed workflow because the ground is shifting beneath our feet every single week. Since there is no settled standard yet, we are seeing the emergence of protocols like the Universal Commerce Protocol and the Agentic Commerce Protocol to help agents complete transactions autonomously. Brands need to align themselves with platforms that are actively contributing to these standards rather than just reacting to them. By adopting a system that stays flexible and anticipates how retrieval methods change, companies can avoid the constant, exhausting cycle of reworking their product data. It is about building a foundation that is “agent-agnostic,” ensuring your products can be discovered and purchased regardless of which specific AI model becomes the dominant player.
What is your forecast for the future of agentic commerce performance?
I believe we are rapidly approaching a “post-website” era where the majority of retail transactions are negotiated and finalized between two AI agents—one representing the consumer and one representing the brand. In this forecast, the brands that win will be those that have moved past simple “reporting” and have fully integrated agentic systems that can optimize their presence in real-time. We will see a world where your refrigerator’s AI negotiates with a grocery store’s agent to find the best price and delivery window without a human ever looking at a screen. To thrive in this environment, businesses must prioritize the health of their data and the agility of their automated systems today, or they will find themselves locked out of the conversations that drive the future of global trade.
