The conventional wisdom said AI search would compress the funnel and shift citations toward third-party reviewers and aggregators in the consideration phase. The data tells a different story. Across eight industries on Google AI Overviews and ChatGPT, the middle of the funnel still represents meaningful volume, and brand-owned content takes the dominant share of citations there.

BrightEdge AI Hyper Cube analysis across Google AI Overviews and ChatGPT shows that the consideration stage of the funnel is alive, measurable, and varies dramatically by industry. The size of the middle ranges from 4% to 26% of AI search demand depending on the category and the engine. Inside that middle, brand-owned pages account for 42% to 79% of citations across every industry studied, while review and comparison aggregators account for 1% to 7%. The opportunity for brands is on their own domains.

This is the second installment in our funnel-shape research series. In the prior installment we examined how the four classical query intent categories survived into AI search, each reshaped to fit the medium. That analysis mapped consideration-stage queries to the classical commercial intent bucket. This installment looks at the same data through the lens of the consumer journey: top, middle, and bottom of the funnel. The terminology shifts from "commercial intent" to "consideration stage" because the questions we are answering this time are about consumer-journey shape, not classical SEO intent taxonomy. The underlying data is consistent with the prior piece.

We analyzed the full prompt universe across eight industries on both engines: B2B, Ecommerce, Education, Entertainment, Finance, Healthcare, Insurance, and Travel. Each prompt was classified by the BrightEdge Generative Parser into a funnel stage. Citations from consideration-stage prompts were categorized by source type. The findings are directly relevant to any brand planning content strategy for AI search across multiple industries.

Data Collected

Key Finding

The middle of the AI search funnel is real volume in every industry, and brand-owned content owns it. Volume-weighted, the consideration stage represents between 4% and 26% of AI search demand across the eight industries studied. Travel, Ecommerce, B2B, and Finance show the largest middles. Healthcare and Entertainment show the smallest. Inside that consideration stage, brand-owned commercial pages take between 42% and 79% of all citations across every industry on both engines. Review and comparison aggregators, the source type marketers most often assume dominates the research phase, take between 1% and 7% of citations in most categories. The implication for marketers is that the buying-guide content, category explainers, and comparison pages on a brand's own domain are the highest-leverage AI search assets in the middle of the funnel. Outsourcing the consideration phase to third-party reviewers leaves the dominant citation channel uncovered.

The Size of the Middle, by Industry

Volume-weighted share of AI search demand classified as consideration stage:

Two patterns stand out. First, the size of the middle of the funnel varies by a factor of six between the largest category and the smallest. Marketers who assume the consumer journey looks the same across industries are missing where the opportunity actually concentrates. Second, AI Overviews consistently shows a larger consideration stage than ChatGPT in commercial categories. In Travel, Ecommerce, B2B, Finance, and Insurance, AIO's middle is meaningfully bigger than ChatGPT's. In Education and Entertainment, the two engines look roughly the same. The pattern suggests AIO is woven into the buying journey in commercial categories in a way ChatGPT is not, despite the popular narrative that consumers have moved their research behavior to conversational AI.

What a Consideration Prompt Looks Like

Consideration prompts capture users who are comparing options or evaluating a category without committing to a specific brand or product. Examples drawn from the data, generalized for clarity:

In Travel: "best beach vacations for families," "top all-inclusive resorts in Mexico," "cheapest time to fly to Europe."

In Ecommerce: "best treadmill for home use," "treadmill vs elliptical for cardio," "best mattress for back pain."

In B2B: "small business CRM software," "best project management tools for remote teams," "top cloud storage providers for enterprise."

In Finance: "best high yield savings accounts," "Roth IRA vs traditional IRA," "top robo-advisors."

In Insurance: "term vs whole life insurance," "best homeowners insurance companies," "cheapest car insurance for new drivers."

These queries differ from branded queries (which name a specific product or company) and from transactional queries (which signal readiness to act). The defining characteristic is comparison and evaluation. The user is figuring out what to want, not which one to click.

Brand-Owned Content Dominates Consideration-Stage Citations

Across every industry studied and both engines, brand-owned commercial pages take the largest share of citations in the consideration stage. The range is 42% on the low end (Healthcare, where authority sites take a meaningful slice) to 79% on the high end (Travel ChatGPT, where the engine routes consideration queries heavily to brand domains and bypasses online travel agencies). Most industries land between 50% and 70%.

This finding pushes back on a widely held assumption in the AEO and GEO community. The assumption was that AI engines, when faced with a comparison query, would lean on third-party reviewers and aggregators to make the recommendation. The data shows the opposite. The brand's own buying guide, category explainer, or comparison page is more often the cited source than a review aggregator.

The pattern is consistent across engines, with one nuance. AIO concentrates citations across a small number of brand-owned domains. ChatGPT distributes citations across a wider set of brand-owned domains. The dominance of brand-owned content holds in both cases, but the competitive dynamics are different. On AIO, winning a consideration-stage citation in a given category means displacing a small number of established players. On ChatGPT, winning a citation is more accessible, but the citation share per win is smaller.

Review and Comparison Aggregators Are Not the Dominant Source

The source type that conventional AEO wisdom positioned as the natural winner of the consideration stage, review and comparison aggregators, accounts for between 1% and 7% of citations in most industries on both engines. The two exceptions are B2B on ChatGPT, where software review and comparison sites take a slightly larger share, and Finance on AIO, where financial product comparison sites cluster around the high end of the range. Even in those exceptions, brand-owned pages still take more citations than aggregators.

This does not mean third-party reviews are irrelevant. They influence the brand recommendations AI engines surface and they remain important for trust signals. But the citation slot, the actual source AI engines link to in the consideration stage, more often belongs to a brand's own domain. Marketers who have built their AI search strategy primarily around earning third-party reviewer mentions are competing for a small share of the citation channel.

Citation Concentration: AIO Concentrates, ChatGPT Distributes

Citation concentration in the consideration stage differs substantially between engines. On Google AI Overviews, a small number of domains accounts for the majority of citations in any given industry. On ChatGPT, citations spread across a much larger set of domains for the same industries.

The pattern means winning consideration-stage citations on AIO requires going deeper on a smaller number of pages within a category. The competitive set is narrow. Once a brand earns a citation slot, it tends to hold it across many related prompts. ChatGPT is the opposite. The citation pool is more democratic. Breadth of topical coverage, distinctive perspectives on a category, and content depth across many comparison angles matter more than dominance on a single page.

For content strategy, this means the optimization approach differs by engine. On AIO, the priority is identifying the small number of pages that win the highest-volume consideration queries in a category and concentrating optimization investment there. On ChatGPT, the priority is breadth, coverage across the full comparison landscape, and content depth that signals authority across many sub-topics within a category.

A Note on Google AI Overviews and When They Trigger

Google AI Overviews do not appear on every search. AIO is triggered only when Google decides an AI Overview is the right response format for a given query. Many consideration-stage searches return a traditional organic results page with no AIO at all. The analysis in this study measures the subset of consideration queries where Google has chosen to deploy an AIO.

This caveat actually strengthens the finding rather than weakening it. Even on the consideration queries Google has decided merit an AI Overview, the citation slots are not spreading across third-party reviewers and aggregators. They are concentrating on brand-owned pages. Whatever combination of signals Google uses to decide when to deploy AIO and what to cite inside it, the result is that brand-owned content is the dominant beneficiary in the consideration stage.

ChatGPT does not have an equivalent trigger condition. Every prompt receives a response. The full consideration-stage volume on ChatGPT is measured directly. The fact that brand-owned content dominates on both surfaces, despite the very different mechanics of how AIO and ChatGPT decide what to cite, reinforces the strength of the underlying pattern.

Industry-Specific Patterns

Healthcare. Healthcare authority sites (major medical centers, government health agencies, established medical reference sites) take a larger share of citations than in any other industry, between 26% on AIO and 36% on ChatGPT. Even so, brand-owned commercial pages still take the largest single share. The takeaway for healthcare marketers is that competing for citation slots means competing against highly credentialed authority sources, which makes E-E-A-T signals (expertise, experience, authoritativeness, trust) even more important in this category than elsewhere.

Travel. Travel shows the most divergent engine behavior. On AIO, online travel agencies and booking aggregators take a meaningful slice of consideration citations (around 24%). On ChatGPT, the engine bypasses OTAs and routes consideration citations directly to brand-owned destinations (79% brand-owned). For travel brands, this means a ChatGPT optimization strategy that targets brand-owned travel content can win significant citation share, while an AIO strategy needs to plan for OTA competition in the citation slot.

B2B. B2B shows the cleanest gap between AIO's larger middle of the funnel (22%) and ChatGPT's smaller middle (9%). The implication is that B2B buyers are using AIO for category exploration more than they are using ChatGPT for the same purpose, at least in the consideration stage. Software review aggregators have a slightly more prominent role here than in other categories, but brand-owned product pages and buyer's guides still take the largest share.

Education and Entertainment. These are the only two industries where ChatGPT's middle of the funnel is larger than or equal to AIO's. Both categories also show meaningful citation share for video platforms (10% to 17% on AIO) and UGC sources (10% to 13% on both engines). The pattern suggests that for educational and entertainment decisions, users are pulling in more diverse source types than in commercial categories.

What Marketers Need to Know

The middle of the funnel is real volume in AI search. The size varies by industry and by engine, ranging from 4% to 26% of total demand. In Travel, Ecommerce, B2B, and Finance, the consideration stage represents a meaningful share of AI search demand on both engines and should be a primary focus for content strategy.

Your own content is the opportunity. Brand-owned commercial pages take 42% to 79% of consideration-stage citations across every industry studied. The category guides, comparison pages, and buying guides on your own domain are doing the work. Investing in this content is more leveraged than chasing third-party reviewer placements.

Do not outsource the middle to third parties. Review and comparison aggregators take 1% to 7% of consideration citations in most categories. They remain important for trust signals and indirect influence on what AI engines recommend, but the citation channel itself belongs to brand-owned content.

Optimize for both engines differently. AIO concentrates citations across a small number of pages. ChatGPT distributes citations across a wider set. The same brand-owned content strategy serves both engines, but the tactical priorities differ. On AIO, win the small number of pages that own the highest-volume consideration queries. On ChatGPT, build breadth and topical depth across the full comparison landscape.

Audit your consideration coverage by industry. Some industries have much larger middles than others. If you compete in Travel, Ecommerce, B2B, or Finance, the consideration stage deserves significant share of your AI search investment. If you compete in Healthcare, Education, or Entertainment, the middle is smaller, but the source-type dynamics in those categories require category-specific strategy (authority signals in Healthcare, video and UGC presence in Education and Entertainment).

Expect engine-specific behavior, not engine-specific intent. The underlying user behavior in the consideration stage is the same across engines. The way each engine surfaces and cites sources for that behavior differs. A unified content strategy organized around the consumer journey, with execution tuned for each engine's citation dynamics, is more durable than separate engine-specific playbooks.

Technical Methodology

Key Takeaways

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

BrightEdge AI Catalyst analysis across five AI search engines shows that the brands AI engines recommend converge tightly in some categories and diverge sharply in others. Where consumers transact, the engines agree. Where consumers research, the engines develop distinct preferences for which brands belong in the answer.

BrightEdge AI Catalyst analysis reveals that ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews recommend a strikingly similar set of brands at the aggregate level. Pairwise overlap in top-named brands across the dataset falls in a tight 36% to 55% band. But that aggregate average masks meaningful category-level variance. In retail, travel, and tech, brand agreement across all five engines runs between 88% and 97%. In healthcare and finance, it drops to 60% and 71%. The same engines that converge on the same retailers, hotels, and consumer electronics brands disagree substantially about which hospitals, financial institutions, and authoritative publishers to recommend.

This is the follow-up to our recent BrightEdge AI Catalyst research on cross-engine source and brand convergence. The original study found that AI engines pull from wildly different sources but recommend the same brands. That finding holds at the dataset level. This installment splits the same dataset by category to show where the agreement is real, where the divergence concentrates, and what each engine's editorial signature looks like inside the categories where the engines part ways.

Data Collected

Key Finding

The aggregate brand convergence finding is real, but it is not evenly distributed across categories. When the same dataset is split by industry vertical, the spread of pairwise top-30 brand overlap looks very different from category to category. Retail shows 97% average pairwise brand agreement across all five engines. Travel shows 94%. Tech shows 88%. Finance drops to 71%. Healthcare drops to 60%, with a low-to-high range of 40% to 77% across engine pairs. The categories where consumers transact directly with brands (retail, travel, tech) draw from a smaller universe of well-established brands, and the engines pick from that same shared pool. The categories where consumers conduct deep informational research (healthcare, finance) have a much wider universe of credible brands, and each engine has developed its own preferences for which brands belong in the answer. The implication for brand strategy is that one playbook works across all five engines in transactional categories, while research-driven categories require engine-by-engine measurement and category-specific positioning.

Brand Agreement Sits at Different Points by Category

The pairwise top-30 brand overlap across all five engines varies materially by category. Where the buying decision is the dominant query intent, the engines converge. Where the research process is the dominant query intent, they diverge.

In retail, every engine pulls from a small, stable universe of major retailers and consumer brands. Amazon, Walmart, Target, Best Buy, Home Depot, and a handful of category leaders dominate every engine's top 30, leaving little room for divergence. Travel behaves the same way: a tight cluster of online travel agencies, hotel groups, and airlines (Expedia, Booking, Marriott, Hilton, Delta, United) carries across all five engines. Tech sits one step behind at 88% because the pool of relevant tech brands is slightly larger and the engines start to differentiate on which platforms, tools, and services they elevate. Finance and healthcare are the two categories where the divergence becomes the story.

Same Brand. Same Study. Different Treatment.

Inside the high-divergence categories, the same brand often receives dramatically different treatment from one engine to another. The gap is not subtle. Several flagship brands show share-of-mention differences of 9x to 22x across engines.

Mayo Clinic. Mayo Clinic is the most universally recognized healthcare brand in the dataset. Gemini directs 13.1% of its healthcare brand mentions to Mayo Clinic. Google AI Overviews directs only 1.5%. The same flagship authority commands roughly 9x more share on Gemini than on AI Overviews.

Cleveland Clinic. Cleveland Clinic captures 6.5% of Perplexity's healthcare brand mentions. On ChatGPT, that figure drops to 0.3%, a roughly 22x gap. Two engines querying the same healthcare prompts treat the same major medical institution very differently.

Goldman Sachs. Goldman Sachs ranks in the top 10 most-mentioned finance brands on Google AI Mode. On ChatGPT, Gemini, and Perplexity, Goldman Sachs does not crack the top 100. Institutional banking presence on AI Mode is materially different from institutional banking presence on the other engines.

Nasdaq. Nasdaq holds 4.5% share of finance brand mentions on ChatGPT. On AI Mode, that figure drops to 0.4%, an 11x gap. Engines that draw from exchange and regulatory sources elevate Nasdaq strongly. Engines that draw from institutional banking sources do not.

Bloomberg. Bloomberg captures 2.9% of finance brand mentions on Gemini. On AI Mode, Bloomberg has zero presence in the top 100. Editorial finance brands are heavily represented on Gemini and largely absent from AI Mode.

These are not measurement noise. They are systematic patterns that reflect the editorial signature each engine has developed inside high-divergence categories.

Each Engine Has a Personality Inside High-Divergence Categories

Inside the categories where the engines part ways, each engine favors a recognizable type of brand. The patterns hold up across the dataset and offer a usable mental model for predicting how each engine will treat brands in any high-divergence category.

ChatGPT favors specialty associations and exchanges. ChatGPT's healthcare top brands include the American Academy of Orthopaedic Surgeons (AAOS), the American Urological Association (AUA), the American College of Gastroenterology (GI), the American Academy of Pediatrics (AAP), and the American Academy of Family Physicians (AAFP). Its finance top brands lead with Nasdaq, the SEC, and major brokerages (Fidelity, Schwab, Vanguard). The pattern is consistent: ChatGPT elevates professional associations, regulatory bodies, and exchanges that carry institutional weight within their specific subcategory.

Perplexity leans on consumer health publishers and trading research tools. Perplexity is the only engine where consumer health publishers (Healthline, WebMD, Medical News Today) appear prominently in healthcare mentions. In finance, Perplexity uniquely elevates trading research tools like Stock Analysis, http://Investing.com , and Marketbeat alongside the standard exchanges. The pattern reflects Perplexity's broader posture as a research-oriented engine that surfaces both institutional and consumer-facing reference sources.

Gemini hyper-concentrates on flagship authorities and editorial finance. Gemini directs 13.1% of healthcare brand mentions to Mayo Clinic and 9.5% to NIH, two brands that account for nearly a quarter of all Gemini healthcare mentions. In finance, Gemini elevates Bloomberg, Wall Street Journal, Reuters, Forbes, and Investopedia, the editorial finance media set. The pattern is consistent: Gemini behaves like a concentrated authority recommender that leans heavily on a small set of flagship brands rather than producing broad lists.

Google AI Mode quietly favors institutional banks. AI Mode's finance top brands lead with JPMorgan, Wells Fargo, UBS, Goldman Sachs, Barclays, and Citigroup. The institutional banking presence is unique to AI Mode and is not mirrored on any other engine in the dataset. This pattern is not visible in aggregate share-of-voice reports, but it is clearly visible when finance brand mentions are isolated by engine.

Google AI Overviews spreads thin across providers. AI Overviews has the lowest brand concentration in high-divergence categories. No single brand commands more than 1.5% of healthcare mentions, and no finance brand exceeds 0.8%. The engine's top-15 lists in these categories include a wider variety of brands at lower share-of-voice levels, consistent with AIO's broader UGC-first sourcing posture.

What Marketers Need to Know

Brand agreement is real, but it is category-dependent. The aggregate finding that AI engines mostly agree on brands holds up. But the agreement is not evenly distributed. Retail and travel converge at 94% to 97% pairwise overlap. Healthcare and finance drop to 60% and 71%. Where your category sits on that spectrum determines whether one strategy works across all five engines or whether you need to look engine by engine.

Where consumers buy, engines converge. Where consumers research, engines diverge. Transactional categories pull from a small pool of well-established brands, and the engines pick from the same pool. Research-heavy categories have a much wider universe of credible brands, and each engine has developed its own preferences inside that universe. The strategic implication is straightforward: a single AI search strategy is portable across engines if you operate in retail, travel, or tech. If you operate in healthcare, finance, or other research-driven categories, you need to think harder about which engine is treating your brand how.

Each engine has a recognizable personality inside high-divergence categories. ChatGPT favors specialty associations and exchanges. Perplexity favors consumer health publishers and trading research tools. Gemini favors flagship authorities and editorial finance media. AI Mode favors institutional banks. AI Overviews spreads thin across providers. Knowing the editorial signature of the engines that matter to your buyers is half the work of building visibility in a high-divergence category.

Audit by engine within your category, not just in aggregate. The aggregate share-of-voice number can hide a lot if you are operating in a high-divergence category. A flagship competitor "missing" from one engine but dominant on another is not a measurement error. It is that engine's editorial signature in your space, and it is actionable. Brand teams operating in healthcare, finance, or any research-driven category should be measuring share of voice engine by engine and treating the divergence as a strategic input rather than a reporting inconvenience.

Match your authority strategy to the engines that matter. If your buyers rely heavily on Gemini, your strategy should prioritize being covered by the flagship authorities and editorial media that Gemini elevates. If your buyers use ChatGPT, your priority is presence in the specialty associations, regulatory bodies, and exchanges relevant to your subcategory. If your buyers use AI Mode in finance, institutional banking visibility is the lever that moves the needle. The three-layer source framework from the prior study still applies. The category-level engine personalities tell you where to put the emphasis.

A flagship competitor missing from one engine is signal, not noise. When a major brand in your category dominates one engine and is absent from another, that is meaningful information about how each engine has built its editorial signature. Mayo Clinic at 13.1% on Gemini and 1.5% on AIO is not random. Goldman Sachs in AI Mode's top 10 and absent from ChatGPT, Gemini, and Perplexity is not random. These patterns reflect each engine's source layer weighting and editorial posture. They are also the most actionable input for prioritizing PR, content, and authority-building investment.

Technical Methodology

Key Takeaways

BrightEdge AI Catalyst analysis reveals that ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews operate with fundamentally different editorial personalities when selecting the sources they cite. At the same time, the brands named in AI-generated answers remain far more consistent across engines than the sources those engines use to construct those answers. The gap between how engines source and what engines recommend is the single most important pattern for any brand building an AI search strategy.

The prevailing assumption in AI search is that each engine requires its own playbook because each engine behaves differently. The data confirms the engines do behave differently, in some cases by close to two orders of magnitude. But the consistency on the output side, which brands get named in the final answer, tells a different story. The playbook does not need to be fragmented by engine. It needs to be organized by source layer.

This is the latest installment in our BrightEdge AI Catalyst research series. We analyzed citations and brand mentions across ChatGPT, Perplexity, Gemini, Google AI Mode, and Google AI Overviews, drawn from prompts spanning ten industries including B2B technology, education, entertainment, finance, healthcare, insurance, restaurants, travel, and ecommerce. The patterns that emerged are directly relevant to any brand planning AI visibility at scale.

Data Collected

Key Finding

AI search engines are often discussed as if they behave similarly because they produce a similar kind of output: a synthesized answer with citations. The BrightEdge AI Catalyst data shows that behind the surface, the five engines pull from meaningfully different parts of the web. The share of citations coming from authoritative sources ranges from 10% to 26%, depending on the engine. The share coming from user-generated content ranges from 0.2% to 18%, roughly a 90x spread across engines answering the same categories of questions. Despite that divergence in sourcing, the brands those engines recommend cluster in a much tighter range. Pairwise top-100 overlap in named brands across engines falls between 36% and 55%, a 19-point spread, while pairwise top-100 overlap in cited sources ranges from 16% to 59%, a 43-point spread. Source agreement between any two engines varies widely and inconsistently. Brand agreement is consistently steady. The implication for brand strategy is that the path AI takes to reach its answer matters less than most strategies assume, but being present across the three distinct source layers that feed those paths matters more than strategies typically account for.

Five AI Engines, Five Sourcing Personalities

Gemini functions as a formal institutional recommender. Gemini shows the strongest bias toward authoritative sources of any engine in the dataset. Approximately 26% of Gemini's citations come from government domains, academic institutions, and major industry institutional bodies combined. UGC and social content makes up only 0.2%. The authority-to-UGC ratio is roughly 130 to 1, the highest in the study. Gemini also shows the highest .gov share of any engine at roughly 13%, paired with a .org share of 23%. The engine behaves as a conservative, list-oriented recommender that leans on trusted institutional voices and tends to produce longer, more inclusive brand lists than other engines.

ChatGPT acts as a long-tail editorial engine. ChatGPT cites the flattest source distribution of any engine in the study. Its top 10 most-cited domains account for only 18.5% of total citations, meaningfully lower than Perplexity (26.7%), Gemini (26.3%), or AI Mode (19.4%). ChatGPT also has almost no UGC presence (0.5%) and pulls heavily from government and .org domains (12% and 20% respectively). The engine reads as a formal editorial assistant with a long, diverse tail of corporate, institutional, and government sources.

Perplexity behaves like a research librarian. Perplexity concentrates more of its citations in institutional medical, government, encyclopedic, and medical publisher sources than any other engine. Combined, those four categories account for approximately 30% of Perplexity's citations. Perplexity shows the highest share of .edu citations (3.2%) and the highest share of international country-code domains (4.4%) in the dataset, reflecting a more formal and globally sourced material mix. It also names brands earliest of any engine, with 86% of its brand mentions landing in position 5 or earlier. Perplexity behaves like an engine that commits to a short, authoritative shortlist rather than producing an exhaustive list.

Google AI Mode operates as a broad commercial aggregator. Google AI Mode pulls from a wider catalog of unique domains than most other engines, with a long-tail distribution that spreads citations across far more sources than its siblings. It also distributes its citations more evenly across source types than any other engine in the study, showing the strongest mix of review aggregators, finance data sources, and news media citations in the dataset. UGC exposure is moderate at roughly 7%, well above ChatGPT or Gemini but well below AI Overviews. AI Mode's top 10 citation concentration is among the lowest at 19.4%, reinforcing its identity as a long-tail, balanced commercial surface.

Google AI Overviews is a UGC-first engine. Google AI Overviews stands apart from every other engine in the study. Approximately 17.5% of its citations come from user-generated content platforms, 35x higher than ChatGPT (0.5%) and 87x higher than Gemini (0.2%). A single video platform accounts for roughly 10.6% of all AI Overviews citations on its own, and a single forum platform adds another 2.9%. Authoritative sources, including government, academic, and major institutional bodies, account for only 9.5% of AIO citations combined. AI Overviews is the only engine in the dataset where UGC citations outweigh authoritative citations.

Authority Share Versus UGC Share, by Engine

The Two Google Engines Are Not the Same Engine

Among the five engines studied, the two most similar are Google AI Mode and Google AI Overviews, with a top-100 citation overlap of roughly 59%. But Gemini, also a Google product, behaves very differently from its siblings. Gemini's top-100 citation overlap with AI Mode is only 27%, and with AI Overviews only 34%. Gemini actually has more in common with ChatGPT (39% overlap) than with the Google search-embedded surfaces. In practical terms, "Google AI" is not one thing. The search-embedded surfaces lean heavily on commercial and UGC content, while standalone Gemini behaves like a conservative, authority-heavy reference engine. Any brand strategy that treats all three Google AI surfaces as interchangeable will miss the actual sourcing patterns driving visibility on each.

The Brand Convergence Signal

The most consequential finding in the study is not the divergence in sources. It is the convergence in brand recommendations despite that divergence. Pairwise top-100 overlap in cited sources across engines ranges from 16% to 59%, a 43-point spread. Pairwise top-100 overlap in named brands ranges from 36% to 55%, a 19-point spread. In every pairwise comparison, brand overlap falls in a tighter, more predictable range than source overlap. The engines disagree substantially and inconsistently about where to pull information from. They agree more consistently about which brands belong in the final answer. That pattern is what makes a unified strategy viable across all five engines, rather than five separate playbooks.

Sentiment Is Overwhelmingly Positive Across Every Engine

Brand sentiment in AI-generated answers skews positive on all five engines, but not uniformly. Gemini is the most positive at roughly 96% positive sentiment, with only 0.3% negative. ChatGPT sits at 94% positive with effectively zero negative mentions. Perplexity shows the highest neutral share at 11%, consistent with its more journalistic, reference-oriented posture. The Google search-embedded surfaces (AI Mode at 93% and AI Overviews at 89%) show slightly higher negative sentiment (1.7% and 2.1%), which reflects their deeper pull from UGC and commercial commentary sources where critical framing more commonly appears. Across the dataset, negative brand mentions remain a marginal share of total volume, which reinforces that visibility in AI answers is almost always presented in a positive or neutral frame.

What Marketers Need to Know

AI engines pull from three distinct source layers, and every engine uses all three. Authoritative sources include government, academic, and major industry institutional content. Commercial and editorial sources include review sites, comparison content, trade press, news media, finance data, and retailer listings. UGC includes video content, forum threads, community discussion, and creator coverage. No engine uses only one layer. The engines differ in how they weight each layer, not in whether they use it. A brand visibility strategy built around only one layer, no matter which layer, will underperform on engines weighted toward the other two.

Authority is category-relative. "Authoritative" does not mean .gov or .edu for every brand. Not every company can or should aim to be cited by federal agencies or academic institutions. Every category has its own authoritative layer: trade associations, analyst firms, expert publishers, standards bodies, professional associations, and institutional voices trusted within the vertical. The strategic question is which authoritative sources serve as the backbone of AI citations in your specific category, and whether your brand is covered by those sources.

Commercial and editorial presence is the widest visibility lever. Across all five engines, the brand/corporate and commercial/editorial source layer accounts for the largest share of citations, ranging from roughly 37% on Gemini to 51% on AI Overviews. Review sites, comparison content, trade press, retailer listings, and finance data are the sources AI most frequently reaches for. Investment in PR, trade coverage, review site visibility, and category comparison content translates into visibility across every engine, not just one.

UGC is non-negotiable for AI Overviews and still meaningful elsewhere. The AI Overviews surface draws roughly 18% of its citations from user-generated content, but UGC is not zero on other engines either. Perplexity pulls 1.5% of its citations from UGC, AI Mode pulls 7%, and both represent real retrievable impressions in categories where community and creator content is strong. A UGC strategy does not mean "produce short-form video." It means understanding which videos, forum threads, and community discussions AI is already citing in your category, and being present in that conversation with authority.

Weight investment based on which engines matter most to your buyers. The three-layer framework is universal. The emphasis is not. A B2B SaaS brand whose buyers rely heavily on ChatGPT and Perplexity will prioritize authority and commercial coverage, with UGC as a supplemental layer. A consumer brand whose buyers use AI Overviews heavily will prioritize UGC and commercial presence, with authority as reinforcement. Brand tracking at the engine level, not just in aggregate, is how those priorities get set and validated.

Engine overlap patterns should inform where you measure first. The two Google search-embedded surfaces share roughly 59% of their top-cited sources, so visibility gains on one frequently translate to the other. Gemini behaves more like ChatGPT than like its Google siblings, so brand teams should not assume that a Gemini strategy is a Google strategy. These overlap patterns are not intuitive, and brands that map their measurement plan against actual engine behavior will catch gaps that aggregate tracking hides.

Technical Methodology

Key Takeaways

BrightEdge AI Hyper Cube analysis of auto prompts across Google AI Overviews and ChatGPT reveals that non-branded queries dominate the top of the purchase funnel -- and that AI recommends brands in nearly every response, whether shoppers ask for one or not.

Every day, car shoppers turn to AI with questions about vehicles, financing, reliability, and deals. But a closer look at how those prompts are structured reveals something that challenges a core assumption about AI search behavior in automotive: a substantial share of auto-related AI prompts contain no brand name at all. And yet brands are being recommended in the AI-generated answers almost every single time.

We used BrightEdge AI Hyper Cube to map the full AI prompt universe across the top auto brands in both Google AI Overviews and ChatGPT. Prompts were divided into three stages of the purchase funnel -- informational, consideration, and transactional -- and analyzed for whether they contained a brand name. We then examined whether brands appeared in the AI-generated answer regardless of whether the prompt named one.

Data Collected

Key Finding

Automotive search strategy has long been organized around branded intent. The assumption is that consumers who are ready to act know which brand they want, and that non-branded queries belong to the awareness stage where brand influence is limited. The prompt data challenges both assumptions. Non-branded prompts represent a significant share of auto AI search volume at every funnel stage -- and AI is actively recommending brands in response to those prompts 97% of the time. The implication is direct: auto brands are not just competing for visibility when shoppers search their name. They are competing to be the brand AI recommends when shoppers do not search for anyone.

Four Patterns Across the Auto AI Purchase Funnel

At the informational stage, branded and non-branded prompts are nearly equal in volume. On Google AI Overviews, non-branded prompts account for 48% of informational auto prompt volume. Shoppers at this stage are asking about car maintenance, towing capacity, charging infrastructure, fuel economy, and vehicle comparisons without naming a specific brand. These are not low-intent queries. They are the first moment of AI-assisted discovery, and brands are being named in AI answers to these prompts 97% of the time. The brand that earns placement in informational AI answers is setting the consideration set before the shopper has explicitly formed one.

Brand intent increases measurably as shoppers move toward purchase. By the consideration stage, branded prompt volume climbs from 52% to 64% on Google AI Overviews -- a 12-point shift that reflects shoppers narrowing their options and beginning to research specific makes, models, trim levels, and lease deals. The non-branded share at consideration still represents more than one-third of prompt volume. Prompts like "most reliable car brands," "best used cars to buy," and "luxury SUV brands" contain no brand name but generate AI responses that name multiple brands, rank them, and editorially favor some over others. For brands not appearing in those answers, consideration-stage visibility is effectively zero.

The transactional stage shows the clearest brand concentration on Google AI Overviews, where 67% of prompt volume is branded. Shoppers at this stage are pricing specific models, searching for dealer locations, comparing lease offers, and requesting test drives. They have done their consideration work. But one-third of transactional prompt volume on Google AI Overviews still contains no brand -- prompts like "car dealership near me," "0% finance car deals," and "test drive" -- and brands are still being recommended in the AI-generated responses to those queries.

On ChatGPT, the transactional stage behaves differently and in a way that is strategically significant. Despite transactional prompts being the stage most associated with brand-specific intent, 70% of transactional auto prompt volume on ChatGPT is non-branded. Prompts like "used cars for sale," "work trucks for sale," and "what car has the best rebates right now" are purchase-intent queries that name no brand. ChatGPT is generating brand recommendations in response to all of them. This pattern suggests that ChatGPT users at the transactional stage are more likely to be delegating the brand decision to AI rather than arriving with a brand already selected.

Branded vs. Non-Branded Prompt Volume by Funnel Stage

The 97% Signal

Across all funnel stages and both platforms, 97% of non-branded auto prompts resulted in auto brands being named in the AI-generated answer. This finding reframes where the competitive battle in AI search actually takes place. Whether a shopper types a brand name or not, AI is selecting brands and presenting them with varying degrees of prominence and sentiment. The prompt is not the battleground. The answer is. Brands that are not present in AI-generated responses to non-branded prompts are absent from a substantial portion of the consideration and purchase journey -- even though no shopper explicitly excluded them.

What Marketers Need to Know

Non-branded prompt volume is not awareness-stage noise. Nearly half of all informational auto AI prompt volume contains no brand name, and those prompts are generating brand recommendations at a 97% rate. A visibility strategy built only around branded query performance is measuring the wrong thing.

ChatGPT transactional behavior in auto is fundamentally different from Google AI Overviews. The 70% non-branded transactional volume on ChatGPT suggests a platform where shoppers are more likely to ask AI to help them decide rather than arriving with a brand already chosen. Content and product pages that can be surfaced in response to generic purchase-intent queries need to be AI-accessible on this platform.

AI is building consideration sets before shoppers do. The brands that appear in AI answers to informational non-branded queries are establishing familiarity and preference before a shopper has consciously begun comparing options. Informational content -- reliability data, comparison content, ownership cost breakdowns -- needs to be optimized for AI citation, not just organic ranking.

Prompt share does not equal answer share. A brand can be named in a prompt without being recommended prominently in the answer, and a brand can be absent from prompts entirely while appearing consistently in AI-generated responses. Understanding where your brand appears in AI answers -- across branded and non-branded prompts at every funnel stage -- is a distinct and necessary measurement capability.

Technical Methodology

Key Takeaways

BrightEdge AI Hypercube analysis reveals that healthcare-related ChatGPT prompts are dominated by transactional intent, with patients and members using AI not just to research conditions but to find care, price procedures, self-treat, and act on their benefits.

People turn to ChatGPT with health questions every day. But a closer look at the prompts reveals something that challenges a core assumption about how AI search works in healthcare: the majority of transactional prompts aren't about learning. They're about acting. People aren't just researching conditions. They're finding providers, pricing care, managing symptoms, and trying to unlock their benefits.

This is the second installment in our AI Hypercube transactional intent series. Last week we analyzed finance. This week we turned the same methodology on healthcare, looking at queries tied to top U.S. health brands and filtering for transactional intent. The patterns that emerged are consistent, significant, and directly relevant to any healthcare brand thinking about AI visibility strategy.

Data Collected

Key Finding

Healthcare is widely treated as a research-heavy, high-trust informational vertical in search strategy. The assumption is that people use AI to learn about conditions, treatments, and providers before taking action elsewhere. The prompt data tells a different story. Transactional intent is present throughout ChatGPT healthcare queries, from finding a family doctor to pricing a dental procedure, checking GLP-1 coverage, self-treating symptoms, and figuring out how to spend an OTC benefits card. Unlike finance, where transactional intent clustered around a single action (applying), healthcare transactional intent fragments across the entire patient and member journey. The implication for healthcare brands is direct: an AI visibility strategy built only around condition content and symptom pages is incomplete.

Five Transactional Intent Clusters in ChatGPT Healthcare Prompts

Finding care is the single largest cluster of transactional healthcare prompts in ChatGPT, accounting for roughly 55% of identified transactional volume. A single prompt, "family doctor near me," drives over 7,000 monthly queries on its own. Variations include urgent care searches, specialist lookups, and "accepting new patients" queries across dental, primary care, and specialty medicine. These are not research prompts. They are front-door prompts from people ready to book. The shift is significant: AI is becoming the discovery layer for care access, replacing traditional provider directory searches and health plan "find a doctor" tools.

Cost shopping accounts for approximately 17% of transactional healthcare volume. People are using ChatGPT to price procedures before committing to them. "How much do veneers cost" drives over 1,600 monthly prompts. Queries for the cost of tooth extraction, dental implants, eye exams, and contact lens fittings appear repeatedly. Dental and vision dominate this cluster, which is consistent with how consumers experience healthcare economics: these are the procedures they pay for most directly. Cost-shopping prompts signal someone who has already decided to act and is now comparing where to do it.

Self-treatment and symptom action represents approximately 16% of transactional volume and is the most distinctive healthcare pattern in the dataset. Prompts like "how to cure neck pain fast," "how to relieve period cramps fast," "how to fix gingivitis," and "how to get rid of cold sores fast" reflect people asking ChatGPT for an action to take, often in place of seeing a provider. The AI is functioning as a first-line care substitute. This is a pattern that has no direct analog in finance, and it reshapes what "content strategy" needs to mean for health systems, pharma brands, and retail health players.

Insurance coverage and enrollment prompts account for approximately 9% of transactional volume, with the pattern heavily concentrated around GLP-1s and weight loss medications. Queries like "Is Ozempic covered by Medicare," "Does Aetna cover weight loss medication," "Will Aetna cover Zepbound," and "How to get insurance to cover GLP-1" reflect users trying to unlock access to a specific drug through their benefits. Parallel queries about buying health insurance on the open market, Medicaid enrollment, and plan selection extend the same access-seeking pattern into the coverage itself.

Benefits card utilization accounts for approximately 2% of volume but represents the most maximally transactional behavior in the dataset. Prompts like "Where can I use my Humana spending account card," "Can I buy toilet paper with my OTC card," and "Can I use my OTC card on Amazon" are from members mid-transaction, trying to determine in real time whether a specific purchase is covered by their benefits card. These are not questions about healthcare. They are questions about completing a purchase. The intent state is closer to a checkout decision than a search query.

Emerging Signal: Agentic Scheduling and Plan Shopping

As in finance, a small but directionally significant pattern of agentic prompts appeared in the healthcare data. These are prompts written not as questions but as direct instructions: "Find a primary care physician accepting new patients in [city]," "How do I find a Medicare Advantage plan with wellness programs in Maryland and Virginia," "Schedule a pediatric specialist visit in [city]." These prompts do not have traditional keyword search equivalents. They reflect a user treating ChatGPT as an agent capable of initiating care access, not just describing how to find it. The behavior is early, but the healthcare version of agentic intent points at two natural use cases: appointment booking and plan shopping. Both are high-stakes, multi-variable decisions that consumers have historically handled through call centers or paid agents, and both are exactly the kind of task an AI agent is well-suited to handle.

Intent Cluster Distribution

The Access Signal

The distinctive finding in healthcare is that transactional intent is distributed across the entire journey of gaining access: access to a provider, access to a procedure at a price the consumer can afford, access to a drug through coverage, access to a benefit the member has already paid for. In finance, intent concentrated around a single action. In healthcare, it fragments across five distinct access moments, each with its own content, page type, and operational owner inside a health organization. That has direct implications for AI visibility strategy. Owning educational content on a condition is no longer sufficient. Brands need to ensure AI can reach the parts of their ecosystem where access decisions get executed: provider directories, scheduling systems, cost and procedure pages, formulary and coverage pages, and member resources.

What Marketers Need to Know

Transactional intent in ChatGPT is real and present in your healthcare category right now. The assumption that AI search is a top-of-funnel channel for healthcare does not hold. People are finding providers, pricing procedures, checking coverage, and acting on benefits inside ChatGPT. Content strategy and AI visibility strategy need to account for where people are in the decision process, not just what condition they are researching.

AI agents need access to the parts of your site where action happens. When someone asks ChatGPT to find a doctor, price a procedure, or figure out what their benefits card covers, the AI needs to be able to surface your provider directory, scheduling pages, cost pages, formulary, and member resources. If your AI-accessible content footprint consists primarily of condition pages and symptom explainers, you are optimizing for the wrong moment.

Coverage and access content drives decisions at the moment of truth. The concentration of GLP-1 coverage prompts, OTC card utilization prompts, and in-network provider prompts shows where benefit-gated decisions are being made. These pages have historically been treated as member-portal content, often behind login walls or buried in PDF formularies. That content needs to be citable by AI.

Self-treatment behavior is a signal, not just a threat. People bypassing the care journey to ask ChatGPT for a remedy are showing exactly where your education-to-action content is weakest. Brands that treat self-treatment prompts as a content opportunity, connecting symptom action content to the appropriate next step (telehealth, urgent care, pharmacy, provider booking), can reclaim that moment.

Agentic prompts are an early signal of where this goes. Prompts framed as instructions to book appointments or compare plans do not carry traditional search volume, but they represent the leading edge of AI-facilitated healthcare decision-making. The brands that build AI-accessible scheduling and plan-comparison infrastructure now will own that conversation as it scales.

Technical Methodology

Key Takeaways