Local domains dominate AI search clicks across global markets

Local domains win where the answer feels native

Aleyda Solis’s latest Similarweb analysis lands with a quiet but sharp warning for global brands: in AI search, the click is often staying local. Across nearly 87.6 million visits and 57,696 domain-market entries, the strongest performers were not always the biggest names, but the domains that looked most usable in a specific market.

That matters because the dataset is not measuring vague visibility. It tracks click-producing AI search traffic only, meaning visits that reached a domain after someone clicked a citation or link inside an AI-generated answer. It does not count citations that never earned a click, and it does not try to score sentiment or recommendation strength. In other words, this is the layer that matters most to publishers and marketers alike: where AI search actually sends people.

What the 10-market picture really shows

Solis looked at ten markets: the United States, United Kingdom, Spain, France, Germany, Italy, the Netherlands, Australia, Mexico, and Brazil. The data came from March 2026, and the pattern that emerges is not a uniform global leaderboard. It is a market-by-market map of who gets the click when AI surfaces an answer.

In the Netherlands, Brazil, Germany, and Italy, local brands frequently rose ahead of global competitors. Bol.com, MercadoLivre, Bahn, and Lefrecce appeared prominently because they were not simply recognizable names. They were the most useful answers for users in those places, backed by local inventory, routes, or market-specific data. That is the part multinational teams often miss when they keep assuming a .com-first structure can do all the work.

Why local authority beats generic global reach

Solis argues that the pattern is not explained by translation quality alone. That is an important distinction, because many international content programs still treat localization as a language job instead of an authority job. A page can be perfectly translated and still lose if it does not carry the operational data, local regulations, country-level product detail, or institutional trust signals AI systems can rely on.

The clearest example in the analysis is travel. A query like “Milan to Rome by train” favors Lefrecce because it holds timetables and routes that match the user’s intent. Booking.com, despite its global scale, does not automatically win that click layer because it is not always the most directly usable source for that specific question. In AI search, the model is not just rewarding brand size; it is rewarding the best answer that works in that country, on that route, or for that local buying decision.

Different verticals behave differently, so one playbook will fail

The report also shows that visibility is not evenly distributed by category. Ecommerce is highly concentrated, finance is more spread out, and travel is fragmented enough that many more domains share the visible space. That alone should change how teams think about AI search strategy. A brand that wins in one vertical cannot assume the same architecture, content depth, or authority signals will transfer cleanly to another.

For global organizations, that means the standard central content model starts to look brittle. The same global page architecture may underperform when local entities matter, when regulations differ by market, or when country-specific supply and pricing data shape the answer AI chooses to surface. If the local page does not hold the right inventory, the right policy language, or the right trust markers, AI search may bypass it in favor of a local competitor that does.

What multinational teams need to change now

The strategic lesson is simple, but operationally uncomfortable: localized pages, localized citations, and market-specific authority are no longer nice-to-have extras. They are strategic assets. Teams need to ask not only whether AI mentions the brand, but whether AI sends traffic to the correct country-level page, or whether it is routing users to a local competitor with stronger evidence in that market.

That means content ownership cannot stay fully centralized. Market teams need enough control to publish the data that makes a page useful locally, from pricing and availability to schedules, compliance details, and local terminology. It also means internal teams should audit whether country pages are genuinely differentiated or just translated mirrors of a global template. In AI search, thin localization is easy to ignore.

A practical response is to treat market signals as infrastructure, not cosmetics:

• Build country-specific pages that include local inventory, routes, pricing, or service data.

• Publish citations and references that reflect the market’s own institutions and standards.

• Keep structured data aligned with local offerings, not just headquarters messaging.

• Review which AI tools are sending clicks to the right destination pages in each country.

• Compare performance by vertical, because ecommerce, finance, and travel behave very differently.

Why this matters even more as AI search scales

The broader search market context makes the shift harder to dismiss. Similarweb’s earlier reporting showed ChatGPT leading the AI search race with an 80.1% share in its tracked data. At the same time, Similarweb’s 2026 Generative AI Brand Visibility Report is already tracking sector-level visibility across finance, travel, beauty, electronics, fashion, and news, which shows how quickly this channel is becoming measurable enough to manage.

That measurement shift is the real turning point. Brands are moving from asking whether AI mentions them to asking whether AI drives usable traffic in the right market. Similarweb’s AI Traffic Tracker exists for exactly that reason: to show where GenAI engines send visitors and which landing pages actually perform. Solis’s findings slot neatly into that new reality, where visibility is only useful if it converts into the right click in the right country.

The new rule for global AI search

The old assumption was that global authority would eventually flatten local differences. The new evidence points the other way. AI search is becoming more local than many global SEO strategies expected, and the winner is often the domain that feels closest to the user’s actual need.

That is why local domains are showing up so often in the click layer abroad. They are not just optimized. They are operationally true, market-specific, and easier for AI systems to trust when the question has a local answer. For multinational teams, the job is no longer to publish one authoritative page and hope it travels. The job is to build authority where each market actually clicks.