AI is now responsible for 12% of consumer product search in Europe — up from 8% in Q2 2025 — and is projected to reach 22% by end of 2026, according to NielsenIQ market data. That is not a future trend. It is a present-tense shift in how consumers discover, compare, and shortlist tech products before they ever reach a retailer's shelf or a brand's product page. For consumer electronics brands, a significant and fast-growing segment of buyers are forming preferences about your product through a channel most marketing teams have not yet optimised for.
What follows is what the data shows, what we're seeing in the market, and what it means for your product strategy right now.
From 8% to 22% — Why This Growth Rate Should Change Your 2026 Priorities
In less than two years, AI has become a top-5 product search touchpoint for consumers in Europe, according to NielsenIQ market data. The trajectory is steep: 8% in Q2 2025, 12% by end of 2025, projected 22% by end of 2026. To put that in context, 8.2 billion unique monthly visits to LLM platforms were recorded in January 2026 alone, according to Mirakl platform data.
The nature of the AI search interaction matters as much as the volume. Consumers are not using AI as a search engine with keywords — they are using it as a comparator, compiler, and closer. NielsenIQ data shows the primary use cases are comparing brands, finding offers, understanding technical specifications, and summarising reviews. In other words, AI is performing the most cognitively demanding parts of the purchase journey: the parts where brand perception is formed and purchasing intent crystallises.
For consumer electronics brands, this is the consequential insight: AI is not sending consumers to your website to browse. It is making a recommendation on your behalf — or not making one at all.
Why Marketplaces Are Winning the AI Visibility Race
Here is a finding that should concentrate attention in any brand's marketing team. According to Mirakl platform research, 55% of leading European retailer clients already appear in LLM search results when consumers ask product-related questions. And of those that appear, 39% appear first.
The reason marketplaces are winning this race is not algorithmic preference — it is assortment breadth. LLMs are trained to be helpful, and helpfulness in product discovery means offering comprehensive coverage. Platforms with wide assortment signal completeness to LLMs in the same way they signal authority to consumers. The result is that a brand's product distributed through a major marketplace has a structural advantage in AI discovery over the same product sold exclusively D2C or through a narrow retail footprint.
"If you are not seen, you are not sold" — this principle, which has governed shelf placement strategy for decades, now applies in full to AI search. The new question is not just which shelf, but which training corpus.
The implication for brands: marketplace distribution is no longer just a revenue channel. It is a visibility infrastructure decision with direct consequences for AI discoverability. Brands that treat marketplace presence as a secondary or commoditised channel are effectively ceding ground in the search touchpoints that are growing fastest.
The In-Store AI Layer — From Brand Ambassador to Always-On Concierge
Two examples from retail operators illustrate how AI is reshaping the discovery experience at the point of sale — not just in search.
Qualcomm's "Jenny" AI brand ambassador pilot at John Lewis in the UK offers a direct case study in what scaled AI presence looks like in physical retail. The core commercial logic is simple: it is impossible to put trained brand ambassadors in every store in Europe, but you can deploy Jenny at a fraction of the cost. The pilot was extended after positive results, and the data shows the model is ROI-positive even at a lower individual conversion rate than a human ambassador — because scale compensates for the per-interaction gap. The human ambassador is better in any single interaction; the AI ambassador wins on breadth of presence. According to Qualcomm data, the economics compound as the network of deployments grows.
Coolblue, the Dutch and Belgian electronics retailer, is building AI agents directly on product pages. This replaces what was effectively a static information wall with what they describe as "a seamless conversation instead of talking to a website." The shift is from monologue to dialogue at the moment of highest purchase intent.
What these two examples share: AI is becoming the interface layer between product information and consumer intent, both in-store and online. For brands, this raises a question that most have not yet asked: what is the AI layer saying about your product when no one from your team is in the room?
Your Product Data Is Now a Marketing Asset, Not a Backend Problem
This is where we have a clear perspective based on what we see across the brands we work with. It is the issue I spend the most time on in conversations with our Data Science clients.
Most brands treat product data as a logistics and operations concern — something managed by the category or e-commerce team to ensure the right SKU information reaches the right retailer portal. That is the wrong frame. Product data is now the primary front-end marketing asset in an AI-driven discovery environment.
The difference between a product that surfaces in LLM results and one that doesn't is almost never the product itself. It is whether the product data is structured in a way that LLMs can interpret, cite, and prefer. According to Mirakl platform research, this requires moving from spec-based data to intent-based data — rich attributes, image extraction, intentional context that reflects how consumers think about the product, not just what it technically is. LLMs do not retrieve spec sheets. They retrieve meaning.
The brands we help structure their product data today are building a lead that compounds as AI search grows from 12% to 22% and beyond. The brands that delay are not standing still — they are falling behind at the pace of AI adoption. When AI accounts for 35% or 40% of product discovery interactions, the gap between structured and unstructured product data will be a measurable revenue gap.
We also think the longer-term trajectory is more structurally disruptive than the current numbers suggest. The emerging dynamic in consumer behaviour is a split between two distinct roles: the "consumer" (the human who experiences and emotionally connects with the product) and the "shopper" (the agent that evaluates options on rational, data-driven criteria). As AI agents increasingly take on the shopper role — comparing options, evaluating specifications, shortlisting products before a human ever engages — the battlefield for brand preference splits in two. You need to win with the AI before you win with the human.
Four Things to Act On Before the End of 2026
▸Audit your product data for LLM readability before your next retail negotiation — structured attributes, rich descriptions, and image metadata are now as important as spec sheets. If your data isn't structured for intent-based retrieval, it will not surface in AI-driven discovery.
▸Evaluate your marketplace distribution as a discoverability decision, not just a revenue channel. The 55%/39% data from Mirakl platform research is not a coincidence — it is structural. Wide assortment wins AI visibility. Brands with narrow retail footprints need to account for this explicitly in their channel strategy.
▸Ask what the in-store AI layer is saying about your products. If retailers in your distribution network are deploying AI agents on product pages or in-store, your brand's representation in those conversations is being determined by your product data quality — right now, without a brand ambassador in the room.
▸Treat the gap between 12% and 22% as an action window, not a grace period. The brands building AI-readable product data infrastructure today will hold a compounding discovery advantage by the time AI search reaches its projected share. The investment required now is a fraction of what catch-up will cost later.
AI-driven discovery is one of five forces reshaping EMEA retail in 2026. For the complete market picture, see EMEA Retail Trends 2026: The Five Forces Reshaping Consumer Electronics.
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