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

People turn to ChatGPT with financial questions every day. But a closer look at the prompts reveals something that challenges a core assumption about how AI search works: a significant portion of finance-related ChatGPT prompts aren't informational at all. They reflect transactional intent. People aren't just learning about financial products. They're trying to act on them.

We used BrightEdge AI Hypercube to analyze finance-related prompts in ChatGPT, focusing on queries tied to the top U.S. banks and financial institutions. The patterns that emerged are consistent, significant, and directly relevant to any financial services brand thinking about AI visibility strategy.

Data Collected

Key Finding

Finance is widely treated as an informational vertical in search strategy. The assumption is that people use AI to research, compare, and learn before taking action elsewhere. But the prompt data tells a different story. Transactional intent is present throughout ChatGPT finance queries, from credit card applications and pre-approval requests to account opening, credit access management, and full agentic requests to initiate financial processes. The implication for financial services brands is direct: an AI visibility strategy built only around educational and informational content is incomplete.

Five Transactional Intent Clusters in ChatGPT Finance Prompts

Credit card applications and shopping represent the single largest cluster of transactional finance prompts in ChatGPT, accounting for roughly 72% of identified transactional volume. Nearly 90,000 monthly prompts reflect people actively comparing or applying for credit cards. The majority are branded, tied to specific co-branded retail cards, airline loyalty products, and hotel programs. A single co-branded retail card drives approximately 66,000 monthly prompts on its own. These are not research prompts. They are decision prompts from people who have already identified what they want and are using ChatGPT to act on it.

Credit card pre-approval is a distinct sub-pattern worth separating out. We tracked 4,465 monthly prompts for pre-approval queries. Pre-approval is the first step in an application process. Someone asking ChatGPT about pre-approval is not exploring options. They are starting the process of applying.

Banking operations prompts reveal a different kind of transactional behavior. Prompts for opening accounts, setting up direct deposit, initiating bill pay, and processing transfers collectively represent approximately 18% of transactional finance volume. These are people using ChatGPT as a step-by-step guide for completing a banking action. The AI is functioning as a procedural concierge, not an information source.

Credit access management prompts, including queries about checking credit scores, unfreezing credit, and resolving credit report issues, account for approximately 4% of transactional volume. The significance here is what these prompts signal about intent state. Unfreezing a credit file is a prerequisite to submitting an application. A person asking ChatGPT how to unfreeze their credit is not casually curious. They are preparing to apply for something.

Agentic prompts represent an emerging pattern that sits apart from the volume-driven clusters. These are prompts written not as questions but as direct instructions: "Help me start a mortgage refinance application," "Find lenders offering competitive rates for a $250,000 loan," "Get me a personalized refinance payment estimate." These prompts do not have traditional keyword search equivalents. They reflect a user treating ChatGPT as an agent capable of initiating or facilitating a financial process, not just describing one. The behavior is early but directionally significant.

Intent Cluster Distribution

The Branded Signal

Branded prompts, those naming a specific financial institution, retail partner, or co-branded product, drive higher average monthly volume than non-branded equivalents. This is counterintuitive. Broad generic queries might be expected to dominate volume. Instead, the data shows people arriving in ChatGPT with a specific brand already in mind, using the AI to help them take action on a decision already made. Brand presence in AI-generated responses matters most at the exact moment of highest intent.

What Marketers Need to Know

Transactional intent in ChatGPT is real and present in your category right now. The assumption that AI search is a top-of-funnel channel does not hold in finance. People are comparing, pre-qualifying, and initiating applications inside ChatGPT. Content strategy and AI visibility strategy need to account for where people are in the decision process, not just what they want to learn.

AI agents need access to the parts of your site where action happens. When someone asks ChatGPT to find a credit card or start a loan application, the AI needs to be able to surface product pages, application entry points, and offer pages. If your AI-accessible content footprint consists primarily of blog posts and educational resources, you are optimizing for the wrong moment.

Branded intent is concentrated at the decision stage. People arriving in ChatGPT with a specific brand in mind are not at the beginning of their journey. They have already done their consideration work. If your brand is not visible at that moment, you are losing customers who were already sold.

Agentic prompts are an early signal of where this goes. The queries without traditional keyword volume are not low priority. They represent the leading edge of how consumers will interact with AI in high-consideration financial decisions. The brands that understand and optimize for this behavior now will own that conversation as it scales.

Technical Methodology

Key Takeaways