AI search engine landscape: where buyers now search
Buyers now research across more than one AI search engine. A map of where those journeys happen, what each surface rewards, and how to plan visibility.
By Roozbeh Nazari · CEO
A buyer researching a supplier in 2026 rarely uses one search box. They might start with a question in ChatGPT, check a shortlist in Google, read an AI Overview, ask Perplexity for a comparison and finish on a vendor's site. Each AI search engine they touch summarises the web differently and links out differently. This guide maps that landscape from the buyer's side: which surfaces sit where in a purchase journey, what each one appears to draw on, and how to plan visibility without chasing every new tool.
What counts as an AI search engine now
The label covers three different kinds of product, and it helps to keep them apart:
- AI features inside a traditional search engine. Google's AI Overviews and AI Mode sit on top of Google's index and ranking systems. Google states that there are no additional requirements to appear in them beyond being indexed and eligible to show a snippet.
- Assistants with web search built in. ChatGPT search, Gemini and Microsoft Copilot answer in conversation and fetch web pages when a question needs current information. OpenAI describes ChatGPT search as giving timely answers with links to relevant web sources, shown in a sources panel.
- Answer engines built around retrieval. Perplexity and similar products run a search for nearly every question and present a cited summary first, with links second.
For a brand, the practical difference is where the content comes from. AI features in Google draw on Google's index. Assistants and answer engines use their own crawlers, partner indexes or third-party search providers, so the set of pages they can see is not identical.
Where buyers use each surface
In B2B and considered purchases, the surfaces tend to line up with stages of the journey rather than replace each other:
- Problem framing. Open-ended questions such as "how do companies usually handle X" often go to a chat assistant first, because the buyer does not yet know the vocabulary.
- Shortlisting. "Best tools for X" or "agencies that do Y in Z" queries move between assistants and classic search, where AI Overviews, maps and review sites compete for attention.
- Validation. Before contact, buyers check the vendor's own site, reviews, pricing pages and third-party coverage. Classic organic results still carry much of this step.
- Comparison. Side-by-side questions are a natural fit for answer engines that cite several sources in one reply.
That pattern explains why the question "which is the best search engine to optimise for" has no single answer. The right mix depends on which stage your buyers struggle with and where they are already looking.
What each surface seems to reward
None of these products publishes a ranking formula, so be wary of anyone who claims a precise recipe. What is documented, and what is consistent across tests that teams can run themselves, points to a few common requirements:
- Crawlability and indexing. If a page is blocked, slow to render or not indexed, it cannot be cited. Google says AI features rely on the same technical requirements as Search.
- Clear, extractable passages. Pages that answer a specific question in a self-contained paragraph are easier to quote than long narrative copy. Our guide to content format for AI search covers this in detail.
- Corroboration across the web. Answer engines tend to cite brands that also appear in independent sources: reviews, directories, industry media and comparison pages.
- Freshness for time-sensitive topics. Assistants fetch current pages for questions about prices, releases and news, so outdated pages lose out on those queries.
How to plan visibility across AI search
A workable plan treats AI search as an extension of search fundamentals, not a separate channel with its own tricks:
- Fix the technical base first: indexing, rendering, internal links and structured data that matches visible content.
- Write for questions buyers actually ask at each stage, and answer them directly near the top of the page.
- Build presence on the third-party sources that AI answers cite in your category, which you can find by running your own prompts and recording the links.
- Measure with a fixed prompt set over time, alongside Search Console and analytics, rather than relying on one-off screenshots. Google notes that traffic from its AI features is included in Search Console's Performance report under the Web search type.
This is the scope of our AI search visibility work. We do not promise citations or positions in any AI answer; we make sure your content is accessible, quotable and consistently represented across the sources these engines read.
What to watch next
The landscape is moving quickly. Assistants are adding shopping, booking and agent features that complete tasks rather than send clicks, and search engines are expanding conversational modes. The brands that adapt fastest are the ones that already know which questions their buyers ask, which sources answer them, and how often their own pages appear in those answers.