Buyers are no longer working through a neat sequence of search, click, compare, and decide. They type a question into Google, AI Mode, ChatGPT, Gemini, Perplexity, or another assistant and get what feels like a finished answer. That answer quietly compresses awareness, consideration, and even early demand into a single screen.
If your brand is missing from that response, you can lose the deal before a click ever hits your analytics. To adapt, marketing leaders need a new funnel model that treats LLM and AI Overview visibility as a first class channel, with its own stages, metrics, and content requirements.
Key takeaways
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AI summaries change behavior. When an AI summary appears in results, users click traditional links far less often, which shifts discovery into the answer layer.
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LLM visibility is multi stage. Awareness, consideration, and demand all now occur inside answers, not just on your site.
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Google AI features use query fan out, splitting one prompt into sub queries, so brands win by owning the sub answers the system needs.
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The visibility win is not only ranking. It is being mentioned, cited, framed correctly, and recommended for the right use cases.
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A practical program maps prompts to funnel stages, defines decision assets for each stage, and tracks mention, citation, and recommendation presence over time.
Why a new funnel model is necessary
Classic SEO reporting treats the funnel as impressions, clicks, sessions, and conversions. That model assumes most influence happens on your pages after a user chooses you from a list. In AI driven experiences, the influence moves earlier and off site.
Studies of AI summaries in search results show that users are significantly less likely to click any result when an AI summary appears, and clicks on links inside the summary are relatively rare. The summary itself becomes the primary surface where users learn definitions, encounter brands, and form a shortlist.
At the same time, Google positions AI Overviews as a way to give users the gist quickly and then let them click for depth. That first impression, even if the user eventually visits, is already filtered through whatever sources the model chose.
How AI answers assemble a funnel in one step
When a buyer asks something like “best ERP for multi entity manufacturing,” AI systems do not run only one search. Google’s AI features and research on query expansion show that models can use a form of query fan out, dividing the original prompt into multiple sub questions and querying across different subtopics and sources.
For example, the system might ask itself:
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What are the leading ERP options for manufacturing firms
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Which ERPs support multi entity and multi currency structures
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What evaluation criteria matter for manufacturers with complex plants
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What integration and deployment patterns are common
The final answer is a synthesis of those sub queries. Your visibility depends on whether you own the sub answers that feed into that synthesis and whether your brand is framed correctly once you appear.
That is where the LLM visibility funnel comes in.
Awareness: are you present when the category is defined
At the awareness stage, prompts look like:
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“What is revenue attribution for B2B SaaS”
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“How does patient engagement software work for multi location clinics”
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“What is identity management in a hybrid cloud environment”
Here the user is learning language, understanding the problem, and sketching the solution space.
Win conditions at this stage:
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Your brand or product is mentioned as an example in category explanations.
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Your point of view on the problem, constraints, or approach is reflected in how the answer is framed.
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Definitions and explainers from your site or high quality third party content are cited as sources.
Content and asset types that support awareness visibility:
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Category explainers that use clear, non fluffy definitions.
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Glossary and concept pages built in an answer first format.
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Micro guides around specific pain points, with constraints like role, industry, or environment baked into the title.
If AI systems repeatedly use other brands or publishers as the reference point for “what this is,” you start every journey behind.
Consideration: are you on the shortlist with the right “best for”
In consideration, prompts shift toward comparison and fit:
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“Best attribution tool for product led SaaS with HubSpot”
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“Best patient engagement platform for multi location clinics”
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“Best identity management tool for hybrid environments”
Here, LLMs pull heavily from comparison content, review platforms, integration docs, and vendor pages that describe tradeoffs.
Win conditions at this stage:
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You appear in the shortlist, not just in a long list of possible options.
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The answer associates you with the right “best for” segment.
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The system references your comparison, integration, or decision assets as sources.
Decision assets that drive consideration visibility:
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Best for hubs that segment clearly by use case, size, and constraints.
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Vendor vs competitor comparison pages that state tradeoffs directly.
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Alternatives pages that acknowledge when a different option is a better fit.
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Integration requirement pages that spell out prerequisites and limitations.
Think of this as the “shortlist shaping” layer. Even if a user never clicks, the way you are positioned here influences how they view every conversation with your sales team later.
Demand: does the answer remove or create friction
Demand stage prompts look much more like late stage evaluation and procurement questions:
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“Pricing model for [brand]”
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“Implementation timeline for [category]”
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“Does [product] support SOC 2, SSO, SCIM, and data residency in the EU”
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“What are typical contract terms for [category] software”
At this point, the buyer is checking feasibility, risk, and effort. Misleading or incomplete answers can stall deals or send them to a competitor.
Win conditions at this stage:
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AI answers describe your pricing model, not someone else’s guess.
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Implementation expectations, security posture, and compliance boundaries are accurate and up to date.
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Answers route users toward your “ground truth” pages instead of random community threads.
Demand stage assets:
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Pricing model explanation pages that state what drives cost, what is included, and what is not.
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Implementation playbooks with phases, timelines, and responsibilities.
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Security and compliance hubs that detail certifications, audit reports, and data handling.
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Procurement and legal FAQs that mirror real approval and risk questions.
Here, LLM visibility is not only about being present. It is about reducing friction before your team ever speaks to the buyer.
Turning the LLM visibility funnel into a roadmap
To make this operational, treat LLM visibility like any other channel with a structured plan:
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Build a prompt universe by stage: 10 to 20 prompts for awareness, consideration, and demand that reflect your real buying motion.
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For each prompt, define what a win looks like: mention, citation, recommendation context, and accuracy.
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Map prompts to existing assets and expose gaps where no page cleanly answers the question.
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Prioritize decision assets that influence multiple prompts, such as best for hubs, comparison pages, integration requirements, security, and pricing model explanations.
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Run a fixed prompt set in AI Overviews and at least one or two major assistants monthly, then score outputs on presence, positioning accuracy, and competitor share.
Over a few cycles, you move from guessing about “AI visibility” to treating it as a measurable funnel that complements rankings and traffic.
If you want outside help, Potenture’s LLM Visibility Funnel Baseline builds your funnel prompt universe, measures where you show up across AI Overviews and leading chatbots, then delivers a 60 day asset and authority plan so your brand is present and correctly framed at every stage of the AI driven journey.
