AI Search is rewriting the rules of ecommerce, but what’s actually changing? 

Over the last 15 years, the holiday ecommerce path to purchase was simple and predictable: the consumer typed a simple keyword into Google, scrolled through sponsored links and keyword matches, clicked on a landing page, browsed, and bought. As we head into the 2026 holiday season, this is no longer the case.

The way people search has changed and so has the way consumers browse products. This is the era of conversational commerce, and shoppers no longer have the patience to scan multiple suggested links and product pages, instead they head straight to LLMs and AI search engines and speak to them as if they were a sales assistant on the high street. From ChatGPT and Gemini to Google search and site-native Chatbots, agentic platforms are acting as personal shoppers who know your likes, dislikes and conditions. For brands, this isn’t just a minor shift; it’s an entirely new way to capture consumer intent, and to nurture and convert it. 

The YoY rise of the AI-first consumer

Traditional search volumes are depleting and the common phrase “I’ll Google it” has quickly become “I’ll ask Chat.” The shift is massive, over 41.6% of consumers now start product searches with an AI assistant like ChatGPT, Gemini or Claude, and what was once niche behaviour for early tech adopters has quickly crossed into the mainstream.  

This growth wasn’t incremental, the use of Agentic AI search in the first stage of online shopping has risen by around 200% year on year. Shoppers have realised they don’t need to spend 45 minutes filtering through irrelevant search results, and instead of typing a rigid query like ‘unisex leather weekend bag,’ they ask an AI assistant contextual and situational questions:

“I’m going on a 3-day business trip to Edinburgh in late November so I need a durable, water-resistant bag that looks professional but will also fit trainers, a 16-inch laptop and two suits. My budget is around £300.”

The assistant factors in intent and context, compares options and cross-references customer reviews, and provides a curated shortlist, ready for purchase. 

Why is AI traffic is so great for conversion?

When a shopper referred by an AI assistant lands on their chosen product, they have already completed their research, compared it to competitors, and established trust in the recommendation. This means that by the time they land on your product, they are already primed to buy: a conversion dream.

Leading retailers using specialised, on-site conversational agents are reporting conversion rates of 50% and higher compared to traditional non-personalised shopping experiences. Not only are we seeing increased conversion, but across the board there’s an average 10% - 15% revenue increase as conversational agents curate exact, personalised matches.

This isn’t just a minor improvement; it’s a complete commercial validation of the conversion funnel. Shoppers arriving through AI search are extremely high-intent and are checking out at rates that far surpass those of more traditional search. 

The AI assistant is the new holiday gift guide

As we head into the 2026 holiday season, it is important to recognise the impact that AI shopping will have on the industry.  

Data shows that this is in inescapable reality: In a recent study from McKinsey, research showed 46% of US consumers plan to actively use AI tools for holiday budgeting. Among these users, 49% use it to compare prices, 48% to search for holiday discounts and 47% to generate gift ideas.

Ai search engines have effectively become the modern-day holiday gift guide. Gone are the days of pouring over articles for the perfect inspiration, instead shoppers can just ask an agent, “what should I buy my hard-to-please, outdoorsy brother-in-law for Christmas, for under £50?”

For retailers, the importance of being recommended by these engines is massive. The bounce rates alone are game- changing, and with 69% of consumers less likely to return an item if bought by the help of an AI assistant, the impact of AI search is undeniable. By matching the buyer intent with the right product, AI isn’t just driving conversions, it’s quietly solving the industry’s multi-billion-dollar returns crisis. 

How to be the answer, not just a result

If the AI models don’t know you exist, there’s no chance of getting recommended, there are three core pillars to ensure your products are cited and ranked on LLMs:

1. Optimise for conversational, intent based queries: traditional search targets keywords like, ‘hydration tablets,’ but AI search engines target natural language sentences, ‘best electrolytes for marathon runners with sensitive stomachs.’ Rewrite product copy, FAQs and blog resources to directly answer these conversational questions.
2. Focus on first-party structured data: AI search engines rely on machine-readable data to pull real-time inventory, pricing and shipping information. Ensure product data is up to date and are optimised in merchant centres and AI indexing platforms.
3. Build a robust off-site digital footprint: LLMs don’t pull recommendations from nowhere, web sentiment is an important factor in rankings. A large portion of AI search citations trace back to real human discussions on forums like Reddit. If real consumers aren’t talking about your brand or products on social platforms, forums and unbiased publications, then you won’t be recommended.

In 2026, much of your product discovery is happening off site, but this doesn’t mean you have no control over your rankings. The brands that treat AI search engines as a partner rather than a competitor are the ones who will reap the benefits. As long as you keep your product data structures, feed the models clean information and focus on keeping your customer experience positive, there’s no reason why the changing ecommerce landscape shouldn’t benefit your business too.