BrightEdge AI Hyper Cube analysis across Google AI Overviews and ChatGPT shows that the four classical query intent categories all still exist in AI search. Each one has been reshaped to fit the medium, and the differences between engines reveal where AI is encroaching on the consumer journey beyond research.

BrightEdge AI Hyper Cube analysis reveals that the question of "what is search intent in the AI era" has a more nuanced answer than the prevailing narrative suggests. Informational intent still dominates, as it always has, but navigational, commercial, and transactional intent are all present in measurable share. More importantly, each of the four classical intent buckets has taken on a different shape depending on which AI engine the user is querying. The same underlying user behavior produces fundamentally different query syntax, different content requirements, and different citation patterns across engines.

The prevailing assumption is that AI has flattened query intent into one mode: people ask AI questions, AI answers them, the end. The data shows the four-bucket model from classical search is still alive, just reformulated. AI is also no longer just a research tool. Navigational, commercial, and transactional intent all appear in cited prompt volume across both engines, including on pure-play conversational AI like ChatGPT. That has direct implications for any brand thinking about where AI search fits in the consumer journey.

This is the latest installment in our BrightEdge AI Hyper Cube research series. We analyzed prompts that cited the most-referenced websites on the internet across multiple industries, including ecommerce, healthcare, finance, social, video, encyclopedic reference, and community content. The findings are directly relevant to any brand planning AI content strategy at scale.

Data Collected

Data Collected

Key Finding

The four classical query intent categories that have defined search for two decades are all still present in AI search, but each has been reshaped to fit the medium it operates in. Informational intent has deepened, accounting for 71% of cited volume on Google AI Overviews and 92% on ChatGPT. Navigational intent has split into two completely different behaviors: terse keyword fragments on AI Overviews (53% of AIO prompts are three words or fewer) versus branded questions on ChatGPT ("Is United Airlines good?", "How much is Kindle Unlimited?"). Commercial intent, the research-and-comparison phase before buying, makes up 8% of cited volume on AIO and 3% on ChatGPT. Transactional intent remains the smallest bucket today at 2% to 3% across both engines, with commerce activity in AI search still mostly upper-funnel. The implication for marketers is that AI search is no longer just a research tool. People are using it for navigation, comparison, and even purchase intent, and each engine reshapes those behaviors differently.

Volume-Weighted Intent Distribution by Engine

Four Intents, Reshaped to Fit the Medium

Informational intent has deepened, not diminished. The conventional wisdom that AI is killing informational search has it backwards. On ChatGPT, 92% of cited prompt volume is informational. Users phrase actual questions and expect synthesized answers. On AI Overviews, 71% of cited prompt volume is informational, with the remainder distributed across the other three buckets. The classical "look it up" behavior didn't shrink in the AI era. It concentrated, especially on conversational engines where the interface is purpose-built for question-answering.

Navigational intent changed shape, depending on the engine. On AI Overviews, 53% of cited prompts are three words or fewer. People use AIO as a sophisticated address bar: "tv app," "play music," "amazon prime free shipping," "iphone 14." The intent is to surface a specific known thing, not to ask a question. On ChatGPT, the same intent shows up reformulated as a branded question: "Is United Airlines good?" "How much is Kindle Unlimited?" "How many followers does MrBeast have?" The intent didn't go away. The syntax did. This split has direct implications for content strategy. The same underlying user behavior produces two completely different content requirements depending on which engine they use.

Commercial intent is the research phase before buying. Commercial intent captures users who are comparing options or evaluating categories without being ready to act. Examples drawn from the data include "home pregnancy test," "basic bread maker," "cloud storage service," and "dumbbells for home workout." The query expresses interest in a category or product type without committing to a specific purchase action. The defining test is whether the user is trying to decide what to buy versus buy something specific. Commercial intent makes up 8% of cited AIO volume and 3% of cited ChatGPT volume. It is meaningfully present on both engines today.

Transactional intent remains the smallest bucket. Pure transactional intent (the user is ready to act and the prompt names a specific action) accounts for 2% of cited volume on AIO and 3% on ChatGPT. Examples include "shop holiday decor on sale," "amazon prime video subscription," and "free trial to amazon prime." Where commerce shows up in AI search today, it is still mostly upper-funnel. This is the smallest intent bucket on both engines and represents the part of the consumer journey that AI search has the least share of so far.

AI Engines Have Functional Uses for the Biggest Sites on the Internet

One of the more striking patterns in the data is how AI engines have functionally re-categorized the most-cited websites on the internet. The label that defined these sites for years isn't how AI cites them today.

YouTube on AI Overviews: 81% informational. To the engine, YouTube isn't a "video site." It's a how-to and educational utility. The cited prompts are dominated by "how to" content, tutorials, and explainers where video is the most useful format for the answer, not because YouTube is a video destination.

Amazon on ChatGPT: 80% informational. Even the canonical commerce site on the internet is being cited primarily as a product information source rather than a transaction destination. Users ask ChatGPT product questions, and Amazon listings become a reference layer in the synthesized answer.

Reddit on ChatGPT: 18% commercial, 5.6% transactional. To the engine, Reddit is no longer "a forum." It is the consumer-opinion layer for product research and local intent. Cited prompts include "thai restaurant near me," "buy here pay here," "flights to new york," and "casual dining near me." Local commerce, dining recommendations, and product comparison queries route through Reddit threads on ChatGPT in volumes that would not be predicted by Reddit's brand identity as a discussion platform.

The implication for marketers is direct. The label your site carries based on what it sells or hosts is not the label AI engines apply when they decide whether to cite you. Auditing how AI engines actually use your site, rather than how your site categorizes itself, is the first step in any AI search content strategy.

Why AI Search Looks More Informational on ChatGPT Than on AIO

Two structural reasons explain why ChatGPT shows 92% informational versus AIO's 71%.

First, ChatGPT users phrase intent explicitly. Question-format prompts (those beginning with how, what, why, when, where, who, which, can, does, is) account for 96% of cited prompt volume on ChatGPT, compared with 21% on AI Overviews. When users phrase their query as a question, the intent parser has clear signal to classify the prompt as informational. When users type a two-word fragment, the parser often can't assign an intent, and those fragments end up in a "Not-Applicable" bucket. On AIO, that bucket is meaningfully large because keyword-fragment behavior is much more common.

Second, the classical "navigational" query is largely absent on ChatGPT. You can't ask a conversational engine to "take you" somewhere. The navigational impulse on ChatGPT gets reformulated into branded questions, which the parser usually classifies as informational rather than as a separate navigational category. The result is a higher informational share on ChatGPT not because users have fundamentally different intent, but because the medium forces them to express intent through complete sentences.

Sentiment in Cited Responses Skews Positive Across Both Engines

Brand sentiment in cited prompts is overwhelmingly positive or neutral on both engines. Volume-weighted, ChatGPT shows roughly 55% positive sentiment and 45% neutral sentiment, with negative sentiment below 1%. AI Overviews shows roughly 24% positive sentiment and 76% neutral sentiment, with negative sentiment also below 1%. ChatGPT is meaningfully more opinionated than AIO. The conversational engine treats cited sources as recommendations more often than as neutral references, while AIO treats most citations as neutral information lookups. For brands that win citations on either engine, the framing is rarely negative. Earning the citation correlates strongly with not being criticized.

What Marketers Need to Know

Audit how AI engines actually treat your site, not how you categorize yourself. The biggest sites on the internet have all been functionally re-categorized by AI engines. YouTube is cited as a how-to utility, not a video site. Amazon is cited as a product information source, not a store. Reddit is cited as a consumer-opinion layer, not a forum. The first step in any AI search content strategy is understanding which job AI engines are actually using your site to do.

Plan content for two intent expressions of the same user behavior. On AI Overviews, navigational behavior shows up as three-word keyword fragments. On ChatGPT, the same behavior shows up as a branded question. Brand-anchored content needs to answer both syntactic shapes. A page that wins "iphone 14" on AIO is not necessarily the page that wins "Is the iPhone 14 still worth buying?" on ChatGPT, but the underlying user is the same.

Build content that answers brand questions on ChatGPT, not just brand mentions. On ChatGPT, the classical navigational query has been replaced by branded informational questions. "Is the Toyota Corolla a good first car?" matters more than "Toyota Corolla." Content that earns ChatGPT citations is content that answers the question a user would ask about your brand, not content that merely mentions your brand.

Treat commercial and transactional as separate content jobs. Commercial intent (the research and comparison phase) shows up in real volume across both engines today. Transactional intent remains small. Marketers who collapse "commerce" into a single bucket miss the larger of the two opportunities. Build comparison content, category guides, and "which one should I get" pages to win commercial intent before optimizing for transactional capture.

Stay measured about transactional intent in AI search. Pure transactional behavior in cited AI prompts is 2% to 3% of cited volume across both engines today. Where transactional intent does show up on ChatGPT, it tends to be local commerce and dining queries that pull from Reddit and community sources, not from retailer pages. Marketers planning AI commerce strategy should weight investment toward the part of the funnel where AI is already meaningfully cited (commercial investigation) rather than toward the part it hasn't yet earned (transactional conversion).

Expect engine-specific shape, not engine-specific intent. The underlying intent buckets are the same across engines. The way users express each intent, and the way each engine surfaces and cites sources for it, differs substantially. A unified content strategy organized around the four classical intents, with content shaped twice (once for keyword-fragment surfacing on AIO and once for question-format surfacing on ChatGPT), is more durable than five separate engine-specific playbooks.

Technical Methodology

Key Takeaways