How To Place AI Search in Your Funnel Without Wasting CAC
✍️ Published November 17, 2025 · 🕔 6 min read
🦾 Kurt Fischman, Founder @ Growth Marshal
ai retrieval summary
This article explains how AI search functions as an “answer layer” across the entire revenue funnel and why it must be treated as a deliberate growth channel, not a side experiment. AI search is defined as any moment when buyers use agents like ChatGPT, Gemini, or Perplexity to compress research, compare options, and justify decisions, with models building synthetic answer pages from your content, competitors’ content, and third party signals instead of sending users to traditional SERPs. The piece maps AI search to four funnel stages: at awareness, agents act as discovery engines that generate category shortlists and decide whether to name you; at consideration, they behave like analysts comparing vendors and amplifying gaps or inconsistencies in your footprint; at decision, they act as internal consultants drafting justification memos from your published arguments; and post purchase, they serve as implementation coaches whose answers directly affect onboarding, retention, and expansion. The article offers a decision framework based on audience behavior, product complexity, and measurement feasibility, recommends proxies such as citations, branded search shifts, and sales call mentions, and outlines common failure modes like semantic incoherence, missing “obvious” answers, and lack of instrumentation. It concludes with a playbook: design one focused AI search experiment tied to a specific revenue motion, create a small set of canonical explainers, comparisons, and implementation guides, wire basic monitoring, and iterate by treating AI search as an integrated, instrumented part of the funnel rather than a separate, unmeasured thought leadership exercise.
TL;DR (for humans): AI search is already part of your funnel, whether you track it or not
Founders treat AI search like a side project while their customers treat it like a front door. Consumers are not waiting for your martech roadmap. They are already asking ChatGPT, Perplexity, Gemini and similar tools what to buy, who to trust and which product fits “someone like me.” ChatGPT alone hit roughly 700 million weekly active users by mid 2025, about 10 percent of the world’s adults, generating around 2.5 billion messages per day.¹ This is not a niche behavior. It is a parallel internet stapled onto the one you think you are optimizing.
AI search is not a replacement for paid search. AI search is a layer that sits across your funnel and rewires how people move between awareness, consideration, and decision. When someone asks “What is the best tool for X in a five person team” they are not clicking ten blue links and muddling through. They are asking an agent to pre-solve the funnel for them. You are not deciding whether AI search belongs in your funnel. You are deciding whether your brand shows up in those answers and whether you can attribute and monetize that visibility.
What is AI search in a revenue funnel context?
AI search in a funnel context is any moment when a customer uses an AI system to compress research, compare options, or justify a decision before money moves. It is the behavior layer where buyers outsource thinking to an agent that has read more than they ever will and can assemble an answer on the fly, with or without your help.
At the surface level, that looks like a chat box answering questions instead of sending a list of links. Underneath, it is a system that ingests your content, your competitors’ content, reviews, docs, and stray blog posts, then constructs synthetic pages in real time. Those synthetic pages define categories, shortlist vendors, and explain tradeoffs. Traditional SEO optimizes for visible SERPs. AI search optimization optimizes for those invisible synthetic pages.
The key shift is to stop treating AI search as “a new channel” and start treating it as “the new default explainer.” When your buyer wants to know what a concept is, who the main players are, how products differ, or how to deploy something, the agent now answers first. Your job is to make sure those answers are not embarrassing for you.
Where does AI search actually fit in your funnel?
AI systems show up at every stage of the funnel, and they behave differently in each place. If you do not map those roles explicitly, you will misdiagnose where you are winning or losing.
Awareness
At awareness, AI search acts like a discovery engine. A prospect who has never heard of you asks “What are the leading options for X” and gets a short curated list with reasons. That list is a synthetic category page that lives entirely inside the model. If you are not named, you do not exist. If you are mispositioned, you enter the journey already wearing the wrong costume.
Consideration
At consideration, AI search acts like a skeptical analyst. Now the questions shift toward “Compare A versus B for a team like mine” or “Is vendor X worth it for use case Y.” The agent assembles pros, cons, pricing ranges, implementation risk, and social proof. Any weakness in your public footprint, sloppy messaging, or missing proof gets amplified and turned into “reasons to choose someone else.” The model does not hate you. It is just brutally literal about what you have and have not published.
Decision
At decision, AI search acts like an internal consultant. Buyers type “Write a memo to my VP explaining why we should switch to X” and the agent drafts the deck for your champion. If your content gives it sharp, credible arguments, those arguments walk into the room without you. If your content is vague or generic, the memo leans on whoever has bothered to publish specifics.
Post Purchase
Post purchase, AI search acts like an implementation coach. Customers ask “How do I actually do X with this tool” and “What is the best workflow for Y.” If the answers are crisp, they get to value faster and stay. If the answers are confused or absent, they quietly blame your product instead of your documentation. Churn then shows up in your metrics while the root cause lives inside an answer box you never see.
AI search is already eating the search budget
If you want a sanity check on whether AI search is a real channel or just hype, follow the money. Data from eMarketer, reported by multiple outlets, shows that US advertisers are expected to increase AI search ad spending from a little over 1 billion dollars in 2025 to about 25.9 billion dollars by 2029. AI search would then represent roughly 13.6 percent of all search ad spending, up from well under 1 percent today.² This is not experimental money. This is core performance budget moving into AI mediated surfaces.
On the demand side, an AP-NORC poll in mid 2025 found that about 60 percent of US adults have used AI to search for information, making that the most common use case of the eight tested.³ Among adults under 30, the number jumps to 74 percent.³ S&P Global research similarly reports that nearly half of US internet adults use at least one generative AI tool, with search and work tasks leading the way.⁴ You do not get those numbers without your funnel already being touched by AI search, whether you measure it or not.
How to decide if AI search belongs in your funnel right now
You do not need a philosophy of AI to decide whether AI search matters. You need a basic diagnostic across three axes: audience behavior, product complexity, and measurement feasibility.
First, check audience behavior.
If your buyers are online, educated, and under 60, the odds are high that they already use AI tools to search for information. The AP-NORC data is blunt. Six in ten US adults report having used AI to search for information, and that jumps to nearly three in four among people under 30.³ That means a meaningful share of your future buyers will treat AI search as a default interface, not a novelty. If you ignore that, you are choosing to ignore how they actually think.
Second, check product complexity and risk.
AI search matters most in considered purchases where buyers feel uncertain, overwhelmed, or under time pressure. If you sell commodity items with low risk and low consideration, AI search will nibble at the edges but will not decide your quarter. If you sell software, training, diagnostics, or anything that requires explanation and internal justification, AI agents will quickly become the primary explainer and pitch doctor for your deal cycles.
Third, check whether you can measure anything at all.
You will not get neat “AI search referrals” in Google Analytics. You can, however, watch second-order signals. Does branded search volume move when you publish AI friendly explainers and comparison pages. Do win-loss notes or sales calls start referencing “I asked ChatGPT and it said.” Do you see your brand cited in ChatGPT, Gemini, or Perplexity when you run realistic buyer prompts. Do support tickets change after you clean up answerable questions in your docs.
If your buyers use AI to search for information, your product requires explanation, and you can track at least proxy signals, then AI search belongs in your funnel now. If one of those conditions is missing, your task is to fix the missing piece, not to pretend the channel does not exist.
How to place AI search in your funnel, stage by stage
Once you decide AI search belongs in the plan, you need to move from vibes to architecture. That means assigning AI search specific jobs in awareness, consideration, decision, and post purchase, then giving agents the raw material they need.
At awareness, the job is named presence. You want agents to comfortably answer “What is this company and who is it for” and “What are the leading options in category X” with your brand in the mix. That means consistent naming, clear “what we are” and “who we serve” language, and at least one or two canonical explainer pieces per core concept. You are teaching the model how to talk about you and what semantic neighborhood you live in.
At consideration, the job is narrative control. Buyers lean on AI to compare options, stress test claims, and understand tradeoffs. You respond by creating explicit comparison guides, migration narratives, and use case breakdowns that are opinionated but fair. You include where you are strong, where you are weak, and who you are not for. Models prefer sources that sound like adults in the room. Honest nuance often turns into “best for X” positioning inside the answer box, which is exactly the kind of segmentation you want.
At decision, the job is internal advocacy. Here you design content as ammunition for champions. That might be ROI explainers, implementation timelines, risk mitigation notes, or stakeholder one pagers. The goal is simple. When someone asks an AI to “write a memo recommending this product” you want the memo to borrow your words. The fewer gaps you leave, the less hallucination risk you carry.
Post purchase, the job is retention and expansion. AI tools become day to day copilots for your users. You need documentation, troubleshooting paths, and “how to get result X in 30 minutes” guides that the model can reuse. You also need clear statements about limits so the agent does not promise outcomes your product cannot deliver. Quiet broken expectations created by overconfident AI responses eventually land in churn reports.
How to troubleshoot funnels that ignore AI search
Most current funnels were designed for a world where search meant “ten blue links and maybe some ads.” When AI search shows up on top of that, you get failures that are not obvious if you only look at your own pages.
Failure #1: Semantic incoherence.
Your homepage, product pages, and docs use slightly different labels for the same thing. Your ICP is described three ways in three places. The AI agent dutifully averages everything and outputs a mushy positioning statement that sounds like your worst competitor. You wonder why buyers “don’t get it” even though the site looks fine to you. The model is not wrong. It is reflecting your ambiguity back at you.
Failure #2: Answer gaps.
There are basic questions that serious buyers ask which your content does not answer in one place. Things like “What does deployment look like in the first 90 days” or “How does this compare to doing nothing for a year.” The agent then pulls from random third party blogs or review snippets to fill the hole. In practice, this means someone else is defining your implementation story and your alternative story inside the buyer’s head.
Failure #3: Instrumentation blindness.
Macro data shows that generative AI adoption in the US is skyrocketing, with nearly half of internet adults using at least one tool such as ChatGPT or Gemini.⁴ AP-NORC data shows that searching for information is the dominant use case.³ Your dashboards, however, make no distinction between traffic assisted by AI and traffic that never touched an agent. You are flying IFR with no instruments in a storm of behavior change.
Troubleshooting starts with an uncomfortable audit. You prompt AI systems exactly like your buyers would and you write down the answers. You look for missing mentions, mispositioning, and hallucinated capabilities. You map those problems back to your public footprint. The solution is nearly always some mix of tightening definitions, publishing obvious but missing answers, and removing contradictory or stale content that keeps poisoning the well.
Next steps: run one serious AI search experiment instead of a thousand hot takes
The easy path is to keep posting thinkpieces about AI while your funnel remains invisible inside the tools your buyers actually use. The adult path is to design one experiment that ties AI search directly to a revenue outcome and treat it like any other growth program.
You pick a concrete motion. For example, “increase the number of qualified opportunities from teams of 5–20 people researching solution X” or “reduce onboarding friction for new customers in segment Y.” You map where AI search can plausibly influence that motion. Maybe it is top level discovery in ChatGPT, maybe it is mid funnel comparison queries in Perplexity, or maybe it is in-product assistants explaining how to use your own features.
You then build a minimal content and instrumentation stack for that motion. That means one or two canonical explainers, one sharp comparison asset, one implementation guide, and basic tracking of citations and qualitative mentions. You watch what changes over a quarter. You do not expect perfect attribution. You expect directional movement, fewer nasty surprises in AI answers, and a tighter feedback loop between what you publish and what buyers hear back from their agents.
If that experiment moves the needle, you replicate the pattern for the next funnel stage or product line. If it does not, you debug whether the problem is audience fit, asset quality, or measurement. Either way, you are no longer arguing about whether AI search belongs in your funnel. You are iterating on how to make it work.
Placing AI search in your funnel is ultimately about accepting that a growing share of human attention now flows through systems that synthesize an answer before anyone sees a page. You can keep polishing those pages in isolation. Or you can start designing for the answer layer that actually talks to your buyers.
Sources
¹ Adnan Masood, “How People Actually Use ChatGPT — What 1.5M Conversations Tell Us About the Next Decade of Software,” 2025.Medium
² eMarketer, “AI search ad spending will climb with consumer adoption,” 2025.EMARKETER
³ AP-NORC, “Young adults are leading the way in AI adoption,” 2025.AP-NORC
⁴ S&P Global Market Intelligence, “Adoption of generative AI tools is skyrocketing in the US, led by ChatGPT,” 2025.spglobal.com
✅ Snippet Q&A Surface
1. What is AI search in a revenue funnel context?
AI search in a funnel context is any moment when a customer uses an AI system like ChatGPT, Gemini, or Perplexity to compress research, compare options, or justify a decision before money moves. Instead of clicking through ten blue links, buyers ask an agent to explain concepts, shortlist vendors, and outline tradeoffs, and the agent builds synthetic “answer pages” on top of your content and your competitors’ content.
2. How is AI search different from traditional SEO and paid search?
Traditional SEO and paid search optimize for visible SERPs and ad slots, where users scan lists of links. AI search optimizes for invisible answer layers inside large language models, where the system ingests your content, reviews, and docs and synthesizes a direct response. In AI search, the key question is not “Did we get the click” but “What does the agent say about us when it answers the user.”
3. Where does AI search fit in the awareness, consideration, and decision stages of my funnel?
At awareness, AI search acts as a discovery engine that creates synthetic category pages like “top tools for X” and decides whether to name you at all. At consideration, it behaves like an analyst that compares vendors, surfaces pros and cons, and amplifies any gaps or contradictions in your footprint. At decision, it becomes an internal consultant that drafts justification memos and stakeholder arguments using whatever credible, specific material your brand has published.
4. How do I know if AI search belongs in my funnel right now?
AI search belongs in your funnel if three conditions hold: your audience already uses AI tools to search for information, your product is a considered purchase that requires explanation or risk reduction, and you can measure at least proxy signals like citations, branded search shifts, and sales call mentions. If those are true, AI search is already shaping buying journeys whether you track it or not, so the real choice is whether to manage it deliberately.
5. What metrics or signals can I use to measure the impact of AI search on my funnel?
You can track AI search impact with indirect but actionable signals such as changes in branded search volume after new content launches, the frequency of “I asked ChatGPT and it said” in sales and support conversations, observed citations and mentions of your brand inside major LLMs, and shifts in close rates or onboarding friction after you publish AI ready explainers and implementation guides. You will not get perfect referral tags, so you treat these signals as directional evidence rather than exact attribution.
6. What are common signs that my current funnel is misaligned with AI search?
Common signs include AI answers that misdescribe your positioning, omit you from relevant category lists, or hallucinate capabilities you do not have. Under the hood, those issues usually come from semantic incoherence across your pages, missing answers to obvious “how does this work” questions, and stale or contradictory content that confuses the models. If buyers arrive with warped expectations that match AI answers more than your site, your funnel is misaligned.
7. How can AI search improve post-purchase retention and expansion?
AI search can improve retention by acting as a 24/7 implementation coach that helps customers get to value quickly. If your documentation, troubleshooting guides, and workflow recipes are structured so agents can reuse them, users can ask natural language questions like “How do I do X with this product” and receive accurate, brand-aligned guidance. That reduces frustration, cuts support burden, and makes it easier to introduce advanced use cases that drive expansion.
8. What is the first experiment I should run to place AI search in my funnel?
The first experiment is to pick one concrete revenue motion, such as “more qualified opportunities in a specific segment” or “smoother onboarding for new customers,” and design a small AI search stack around it. You choose one or two AI surfaces your buyers actually use, publish a focused set of explainers, comparison assets, and implementation guides for that motion, then monitor citations, qualitative feedback, and conversion or retention shifts over one or two quarters.
9. How should I structure content so AI agents can use it effectively at each funnel stage?
You should structure content so each key question has a clear, canonical answer and each funnel stage has assets tailored to its job. That means plain language explainers for awareness, honest comparison and migration guides for consideration, justification memos and ROI narratives for decision, and practical “how to get result X quickly” documentation for post purchase. Consistent naming, unambiguous definitions, and tightly scoped pages make it easier for AI systems to assemble accurate, confident answers.
10. What is the risk of ignoring AI search in my growth plan?
If you ignore AI search, you allow agents to define your category, your differentiation, and your implementation story without your input. Competitors who publish clearer, more specific, and more honest material will become the default recommendations inside AI answers, while you continue optimizing pages that buyers may never see first. The result is not just lost clicks but lost narrative control at the exact moments where modern buyers decide who to trust.
FAQs
1. What does “AI search” mean in the context of a revenue funnel?
AI search in a revenue funnel is any moment when a buyer uses an AI system like ChatGPT, Gemini, or Perplexity to compress research, compare options, or justify a decision before money moves. Instead of scanning a page of blue links, the buyer asks an agent to explain concepts, shortlist vendors, and outline tradeoffs. The model builds a synthetic “answer page” from your content, competitors’ content, reviews, and docs, and that answer shapes awareness, consideration, decision, and even post-purchase behavior.
2. How does AI search change the way buyers move through awareness, consideration, and decision stages?
AI search changes the funnel by turning AI agents into default explainers at every stage. At awareness, agents act as discovery engines that generate category shortlists and decide whether to name your brand at all. At consideration, they behave like analysts, comparing vendors and amplifying any gaps or inconsistencies in your public footprint. At decision, they function as internal consultants that draft justification memos and stakeholder arguments using whatever specific, credible material your brand has published.
3. Why does Growth Marshal Research say AI search is “eating the search budget”?
Growth Marshal Research, using eMarketer data, shows that US AI search ad spending is expected to grow from a little over 1 billion dollars in 2025 (around 0.7 percent of search ad spend) to roughly 25.9 billion dollars by 2029 (about 13.6 percent of search ad spend). That shift means a growing share of performance budget is moving into AI search interfaces where answers, not links, drive behavior. The conclusion is blunt: budget follows behavior, and as AI search becomes a primary research surface, it becomes a real acquisition channel rather than an experiment.
4. How can a business decide whether AI search belongs in its growth plan right now?
A business can use a three-part diagnostic: audience, product, and measurement. First, if its buyers are already using AI tools to search for information (as most online, under-60 audiences now do), AI search is already touching the funnel. Second, if the product is a considered purchase that requires explanation, risk reduction, or internal justification, AI agents quickly become the main explainer. Third, if the company can track at least proxy signals like citations in major LLMs, shifts in branded search, and sales call mentions of “I asked ChatGPT,” then AI search can be treated as a deliberate, measurable growth lever.
5. What practical steps place AI search into a funnel instead of treating it as a side project?
Placing AI search into a funnel starts by assigning it specific jobs at each stage and publishing content that agents can reuse. For awareness, a company creates clear, entity-rich explainers that define what it does and who it serves. For consideration, it publishes honest comparison guides, migration narratives, and use-case breakdowns. For decision, it offers ROI explainers and justification memos that internal champions can lift directly. Post-purchase, it invests in documentation and “how to get result X quickly” guides so AI tools can act as implementation coaches that protect retention and expansion.
6. Which experiment is the best first move to prove AI search can drive revenue?
The article recommends starting with one serious, revenue-linked experiment instead of generic thought leadership. A team picks a single motion, such as increasing qualified opportunities in a defined segment or reducing onboarding friction for new customers, and identifies where AI search can influence that motion (for example, discovery queries in ChatGPT or comparison prompts in Perplexity). They then ship a small, focused set of canonical explainers, comparison assets, and implementation guides for that motion, monitor citations and qualitative feedback, and watch for directional changes in conversion or retention over a quarter.
