The marketing industry has operated on a top-down model for over 30 years. From the early days of digital banners to the sophisticated era of search engine optimization, the playbook remained consistent: start with awareness, cast a wide net to capture as much attention as possible, and then nurture those leads down through the acquisition funnel. This logic was sound during the broadcast era and remained functional throughout the first two decades of search. However, in the age of artificial intelligence, this strategy is not just outdated; it is fundamentally flawed.
AI-driven environments, including assistive engines like Perplexity and agential systems like Siri or ChatGPT, do not process information in the same way humans or traditional search engines do. These systems build their ability to recommend your brand from the bottom up. They require a foundation of understanding before they can assign credibility, and they require credibility before they ever consider recommending you to a user. If you continue to build from the top down, you are essentially pouring budget into awareness campaigns while the underlying machines have no structural foundation to attach that awareness to.
The stakes have become absolute. When an AI agent acts on behalf of a user, it evaluates your brand, your offers, and your reputation in milliseconds. If the machine does not understand who you are or whom you serve, the agent cannot act in your favor. This creates a zero-sum moment: a recommendation happens without you ever knowing the prospect was considering you, and the business goes to a competitor simply because the machine “trusted” them more. To survive this shift, marketers must embrace the funnel flip.
The acquisition funnel runs simultaneously in opposite directions
It is important to distinguish between the user experience and the machine strategy. From a consumer’s perspective, the acquisition funnel has not changed. A person hears about a brand (Awareness), evaluates their options (Consideration), and eventually makes a purchase (Decision). This journey has remained the same since Elias St. Elmo Lewis formalized the AIDA model in 1898. For 128 years, the direction was clear: reach first, relationship second, commitment third.
In 2002, search marketer Philippe Lanceleur offered a perfect metaphor for the early web: building a website and hoping for traffic is like opening a shop in the middle of an empty field. No one passes by accident. To succeed, you had to go where the audience gathered and invite them to visit your shop. In that era, awareness was the prerequisite for everything else.
The first crack in this model appeared in 2012 when Google introduced the Knowledge Graph. This marked the shift toward “entities.” Suddenly, the machine began forming its own opinions about brands independently of what users were searching for. Instead of just matching keywords, the machine started drawing its own map and building roads to the shops it deemed relevant. With the rise of AI, these machine-built roads are now the primary way users find brands. The machine builds the road from the shop outward, meaning brand understanding and reputation have replaced awareness as the primary prerequisite for success.
AI makes this flip even more powerful. Assistive engines and agents actively direct users toward destinations they have assessed as credible. If the machine knows your shop exists and believes it is the best destination for a specific user, it provides the road. This is the first genuine structural break in marketing strategy in over a century. While the user still travels from top to bottom, your visibility at the top of that funnel is now entirely dependent on how well you have built your foundation at the bottom.
How top-down and bottom-up coexist
While the strategy has flipped, the two models must coexist. You can still build top-down in channels you control entirely, such as paid media, direct outreach, and broadcast advertising. In these spaces, you can buy awareness and pull people toward a decision. Even within organic search, the user still perceives a top-down experience.
However, for your organic presence within AI engines, you must build from the bottom of the funnel (BOFU) up. This is because every algorithm and agential system operates on entity and brand signals. They don’t care how loudly you push; they care about what they understand. With AI, the roads to your “shop in the field” are increasingly machine-built, and those machines prioritize brand understanding above all else.
The mechanical reality of AI infrastructure can be broken down into three pillars:
- Understandability: This creates the entity node. Does the machine know who you are?
- Credibility: This gives the node preferential consideration. Does the machine trust you?
- Deliverability: This gives the node visibility. Will the machine proactively recommend you?
Without understandability, you have no foundation. Without credibility, you have no proof. Without deliverability, you have no reach. In this new world, you cannot reach the top of the funnel without starting at the bottom.
How the funnel becomes a guided sequence in AI
In the traditional search era, a user journey on Google was a series of self-navigated steps. Google would compose a search engine results page (SERP), and the user would browse, compare, and click. The user was the pilot, and the SEO’s job was to secure a prominent spot on the page.
Today, the “algorithmic trinity” has changed that dynamic. Large Language Models (LLMs) now reason about a user’s intent. They decide whether to answer a question directly, fact-check it against a knowledge graph, or run “fan-out” (cascading) queries to gather information from multiple angles. This allows the engine to answer more accurately, but it also allows the AI to anticipate what the user will do next.
You can see this in the “follow-up questions” suggested by AI tools. The AI is essentially defining the acquisition journey, shaping the current answer to flow toward a specific next step. The user is less in control than they realize. Consequently, the marketer’s job is no longer just fighting for a slot on a page; it is about training the machine’s expectations. You must supply the ideas and publish the logical bridges so that when the LLM predicts the “next step,” your brand is the natural destination.
How far an AI looks ahead depends on the territory. In well-traveled markets, the paths are well-worn, and the LLM can predict three or four moves ahead. In unusual or niche territory, the prediction horizon is shorter. This provides a massive opportunity for brands to “niche down” and solve specific problems, effectively creating the “synapses” in the machine’s brain that lead directly to their solution.
Establishing Funnel Pathways
To capitalize on this, marketers should track “funnel pathways” rather than just isolated keywords. A top-of-funnel (TOFU) query should lead naturally to a middle-of-funnel (MOFU) consideration and finally to a BOFU decision. By publishing content that connects these dots with evidence and corroboration, you ensure the machine treats your brand as the logical conclusion of the user’s journey.
Get your foot in the door and keep it there
In the AI era, you must get a foot in the door as early as possible. The stronger your presence at the beginning of a conversation, the more you shape the machine’s internal logic. As the conversation evolves, the AI “thins the field” of competitors. If you are the brand that provided the initial context and credibility, you are far more likely to be the one that gets the “perfect click” at the zero-sum moment of the final recommendation.
Think of it as educating the algorithm. Google, in many ways, behaves like a child learning about the world. If you guide it properly, its prediction of the “best brand” will consistently converge on you. If you enter the conversation late, you are fighting against the bridges your competitors have already built. You want to be Top of Algorithmic Mind from the very first query.
Display is where your acquisition funnel lives in the AI engine pipeline
The journey from a brand being discovered by a machine to being “won” by a user involves ten distinct gates. The first five gates are purely infrastructural—can the machine access, store, and classify your content? From Gate 6 (Recruitment) onward, the engine begins comparing you to your competitors.
The critical moment happens at Gate 8: Display. This is where the machine’s evaluation becomes visible to the user. Whether or not you move to Gate 9 (Won) depends on the Understandability, Credibility, and Deliverability (UCD) of your brand.
The three dimensions of brand visibility at display
At the point of display, the AI engine can either make or break your brand. It must be convinced that you are the best solution at the exact moment the user is ready to convert. This is handled through three distinct layers:
1. Understandability (U) – The Decision Layer
Understandability is the BOFU layer. It is the deepest trust layer for both the AI and the human user. When a user asks about your brand specifically, the machine draws on its entity record. If that record is weak, the AI will hedge its response with phrases like “appears to offer” or “claims to be.” If the machine doesn’t know who you are, it will stay silent. This is the foundation upon which everything else is built.
2. Credibility (C) – The Consideration Layer
Credibility occupies the MOFU space. This is where the AI evaluates and compares you against others. When a user asks “Who is the best at X?”, the machine looks for N-E-E-A-T-T signals (Experience, Expertise, Authoritativeness, Trustworthiness, etc.). If the machine has more confidence in your competitor’s credibility, you will be excluded from the shortlist. Credibility is what turns an entity into a recommendation.
3. Deliverability (D) – The Awareness Layer
Deliverability is the TOFU layer. This is where the AI becomes an advocate for your brand. When a user asks about a general problem, the machine decides whether to surface your brand as the answer. Advocacy only happens if the machine already understands you (U) and trusts you (C). Without those two, you will remain invisible to users who aren’t already searching for you by name.
The business case for UCD: The three taxes
To explain this to a non-technical audience, consider your primary AI platforms—Google, ChatGPT, Perplexity, Claude, Copilot, Siri, and Alexa—as seven employees working 24/7. They are either selling your products or they are selling your competitors’. Your goal is to train these “salespeople” to work for you.
When you fail to optimize for these machines, you pay what can be described as three distinct “taxes”:
- The Doubt Tax: You pay this when the machine cannot confirm who you are to a prospect who is ready to buy. Instead of a “Yes, buy from them,” the AI gives a hesitant “They might be able to help.”
- The Ghost Tax: You pay this when the machine cannot argue your case during a competitive comparison. You are simply left off the shortlist, essentially becoming a ghost in your own industry.
- The Invisibility Tax: You pay this when the machine doesn’t mention you at all to prospects researching the market. You never even enter the conversation.
The remediation for these taxes must follow a specific sequence: U before C, and C before D. You cannot fix invisibility if the machine doesn’t understand your entity, and you cannot fix a lack of credibility if you haven’t provided the machine with the proper context.
Strategy: Your brand SERP and AI résumé tell you where to begin
How do you know where to start? Look at your Brand SERP (what Google shows for your brand name) and your AI Résumé (what a conversational AI says about you). These are your diagnostic instruments. They show you exactly how much confidence the machine has in your brand.
If the machine gets facts wrong or hedges: You have an Understandability problem. You need to focus on your “Entity Home”—clean structured data, consistent descriptions, and a single authoritative source of truth that the machine can rely on.
If the results are unconvincing or unflattering: You have a Credibility problem. You need to focus on off-site signals like third-party reviews, earned media, and co-citations from high-trust sources.
If the results don’t reflect your actual marketing goals: You have a Deliverability problem. You need to create content that the machine treats as proof of your category leadership rather than just a marketing claim.
Acquisition is one act in a 15-stage play
While we focus heavily on the acquisition funnel because that’s where the money changes hands, it is actually just one part of a much larger 15-stage process. The funnel sits at the “Display” gate, but there are nine gates before it and five gates after it.
The stages that follow a “win”—Onboarded, Performed, Integrated, Devoted, and Codified—are where the real value lies. Every satisfied client creates a signal that feeds back into Gate 0 (Discovery). This creates a flywheel effect. When your existing clients are happy, they train the machine to recommend you more confidently to the next prospect.
In the AI era, marketing is no longer just about the moment of sale; it is about the total lifecycle of the brand and how every interaction feeds back into the machine’s understanding. By flipping the funnel and building from the bottom up, you aren’t just gaming an algorithm—you are building a resilient, machine-trusted brand that can dominate the zero-sum landscape of AI recommendations.
As we move forward in this series, we will explore why evidence alone isn’t enough to win the machine’s favor and how the “framing gap” explains why AI recommends some brands while staying silent on others. For now, the priority is clear: stop building from the top down and start training the machines from the ground up.