TL;DR
- Buyers do not use all AI engines interchangeably — intent shapes which tool they open.
- Perplexity attracts early-stage, citation-hungry researchers; ChatGPT handles deep comparison and troubleshooting; Google AI Overviews intercepts high-volume commercial queries mid-funnel; Gemini captures users already inside Google Workspace flows.
- Claude skews toward long-document analysis and vendor evaluation by procurement-minded buyers.
- Spreading GEO effort evenly across every engine is a resource mistake — pick the engine where your intent stage lives.
- The practical output of this analysis is a single prioritization decision, not a six-channel plan.
"Which AI search do buyers use?" is the wrong question by itself. The useful question is: which AI engine do buyers reach for at a specific moment in their decision process? Each major engine has developed a distinct user habit around a distinct type of need — and those habits have consequences for where your content must earn citations.
Why intent mapping matters more than market share
Raw usage share figures for AI engines shift quarter to quarter and are frequently cited without reliable methodology behind them. What changes more slowly is behavioral habit: the mental model a user builds for what a tool is good at. Once someone learns that Perplexity surfaces sourced answers fast, or that ChatGPT is a good thinking partner for complex comparisons, that habit persists even as the underlying models evolve.
Sparktoro's audience research methodology, applied to AI tools, consistently shows that tool choice correlates with task type rather than demographics alone. A B2B procurement manager may use ChatGPT for vendor shortlisting and Google for quick fact-checks in the same afternoon — same person, different engines, different intents.
This is the framing GEO strategy needs to operate from: not "be everywhere" but "be cited where the intent that converts for you lives."
Research intent: Perplexity is the default
When a buyer is in discovery mode — building a mental model of a problem space, learning terminology, assembling a vendor landscape — they increasingly turn to Perplexity. The engine's core design choice, surfacing cited sources alongside every answer, matches the mindset of a researcher who needs to justify their reading list to themselves or a manager.
Perplexity users at this stage are not ready to buy. They are qualifying categories. If your brand is absent from Perplexity citations during this phase, you do not appear in the buyer's consideration set before they form opinions.
The implication: content designed to earn Perplexity citations needs to be definitional, structured, and source-rich. Glossary pages, "what is X" explainers, and category overviews — written with the evidence-density that Perplexity's retrieval layer rewards — are the right asset type here.
Comparison intent: ChatGPT owns the middle
Once a buyer has a shortlist, they shift from "what is this space" to "which option fits my situation." This is where ChatGPT behavior diverges sharply from Perplexity. ChatGPT users at this stage are running multi-turn conversations: they provide context about their stack, their constraints, their team size, and expect the model to reason through tradeoffs with them.
This conversational comparison mode means the buyer is asking questions like "compare X and Y for a 50-person SaaS company that uses Salesforce." The model's answer draws on its training data and, when Browse is active, on live retrieval — both of which depend on whether your brand has been discussed substantively in crawlable text.
Earning mentions in ChatGPT for comparison queries requires your brand to appear in third-party review sources, analyst summaries, and in-depth editorial content that explicitly positions you relative to alternatives. Your own site's comparison pages help, but third-party corroboration is what gives the model confidence to name you.
Purchase and transactional intent: Google AI Overviews intercept volume
When intent sharpens to "I want to buy this specific thing" or "best [category] tool for [use case]," a large share of those queries still originate in Google Search — and Google AI Overviews now intercept the top of those results pages. The user may not think of this as "using an AI engine," but the citation dynamic is identical: Google's model decides which brands and pages to surface in the generated answer.
AI Overviews tend to appear on queries with clear commercial or informational intent that Google's systems judge as answerable by synthesis. If your category has AI Overview coverage, being cited there is a mid-to-late-funnel visibility play with direct traffic implications. The pages Google pulls into Overviews skew toward structured, authoritative content that matches query language closely — which is also what traditional SEO rewards, making this the most overlap-friendly surface for teams already invested in search.
Troubleshooting and post-purchase intent: Claude and deep-session ChatGPT
Post-purchase, the intent shifts again: buyers become users, and users troubleshoot. They also evaluate whether to expand, renew, or switch. This is where Claude and extended ChatGPT sessions appear most prominently in practitioner observation.
Claude's longer context window and more conservative, document-grounded response style have made it a preferred tool for buyers reading long-form vendor documentation, contracts, or technical specs. A procurement lead pasting a vendor's security whitepaper into Claude to extract relevant clauses is a real pattern. If your documentation is clear, structured, and crawlable, it can inform these sessions even without explicit retrieval.
Gemini occupies a related but distinct niche: buyers already working inside Google Workspace — Docs, Drive, Gmail — encounter Gemini as an ambient assistant rather than a deliberate search tool. This makes it most relevant for buyers at organizations with Google as their productivity layer, particularly during the internal selling and evaluation stage where they're drafting proposals and summaries.
How to use this framework without overextending
The point of intent-to-engine mapping is to create a prioritization forcing function, not to produce a matrix you optimize across simultaneously. For most brands, one or two intent stages matter most for pipeline impact.
A B2B SaaS brand with a 60-day sales cycle and a defined ICP should ask: where in that cycle does influence matter most? If deals stall at shortlisting, ChatGPT comparison visibility is the priority. If awareness is the gap — buyers are not arriving with the category need formed — Perplexity research-phase presence is the leverage point.
Optimize for the engine your intent lives on.
FAQ
Does the same piece of content work across all AI engines?
Rarely at its best. Content optimized for Perplexity's citation retrieval — short definitional answers, structured headings, inline sourcing — is different from the multi-perspective, context-rich content that performs in ChatGPT's comparison conversations. You can design content to serve multiple surfaces, but you will make tradeoffs.
How do I know which intent stage matters most for my funnel?
Start with your CRM data: where do deals enter, stall, and close? Map those stages to the query types they imply. A deal that enters qualified already suggests the awareness problem is solved and the comparison stage is where AI visibility matters most.
Can a brand realistically rank on all major AI engines simultaneously?
Technically yes, but practically it requires significant content and authority investment. Most teams are better served by identifying one or two high-leverage surfaces and building depth there before spreading effort. Distributing thinly across five engines tends to produce mediocre citation presence on all of them.
