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O topo do funil saiu do seu controle: como IAs estão decidindo o que o consumidor vê primeiro
BranddiIP Team ·
For a long time, the top of the funnel was understood as the moment in the purchasing journey when the consumer “reaches you”. A Google search, a post on social media, a targeted ad represents the “first contact” with your brand.
Today, that logic has changed. Consumers no longer discover brands in the same way because generative AI has become the new entry point for discovery.
Answers from ChatGPT, Google Gemini (AI Overviews), Bing Copilot, Perplexity, and assistants built into operating systems are replacing traditional results pages. 32% of Gen Z starts tasks with AI tools instead of using traditional searches, according to surveys.
Want to adapt to the new top of the funnel? Understand the main risks associated with the influence of artificial intelligence on brand discovery and how to deal with this new scenario!
What do you lose when you lose the top of the funnel?
The loss of the top of the funnel happens when the brand loses control over the first contact between the consumer and the information that influences their decision.
Instead of the person discovering your brand through your website, your content or an advertisement, this first contact now it often happens within a response generated by ChatGPT or another AI tool.
The top of the funnel stops being a stage of the sales funnel that you build and starts to be mediated by systems that you do not control.
From this point on, the losses become more evident are:
- Less control over who presents your brand to the consumer;
- Less traffic predictability;
- Loss of authority when AI cites competitors or distorted interpretations.
How do AIs decide what to show?
AI learns language patterns and associations based on this material, and when you ask a question, it generates a response synthesizing this knowledge rather than directly seeking “the most authoritative original document.” In the process, she can:
1. Rely on available content that is not necessarily official
Models such as ChatGPT, Google Gemini and Perplexity are trained on large amounts of text and media that have been “scraped” or aggregated from various parts of the internet.
Data collection includes blogs, forums, news articles, pages without editorial verification and other public materials, with no guarantee that each excerpt represents an official truth or the most accurate source.
2. Being influenced by third-party quotes, reviews and public mentions
An AI generates responses considering frequency, context and co-occurrence of terms and concepts present in the training material, and this includes:
- Mentions in online reviews;
- Third-party quotes;
- Discussions in public communities;
- Cross-references that appear through repetition or highlighting in the material.
Patterns influence response generation because the model has learned that certain terms often appear together.
3. The risks when fraud and piracy are part of cultural training
One of the biggest concerns for SEO and reliability professionals is that AIs can be trained on data that includes pirated material, low-quality texts or even incorrect information that circulates publicly. In systems with huge amounts of data, this means that:
- Models can learn incorrect or biased patterns, because they have seen many wrong or ill-founded versions of the same topic.
- Fraud, misinformation and pirated content can enter the training base and, by making generalizations, influence generated responses.
- Even when the “official” source exists, it can end overshadowed by many signs of greater presence in public sources (such as replications, copies or memes).
What are the new risk vectors?
The change in the way discovery happens has opened up new risk vectors that act before the consumer even reaches your website or comes into contact with your brand. They are:
Scams that appear as legitimate responses
The first major risk arises when scams are presented to the user in the form of apparently neutral and trustworthy responses.
Different from an ad suspicious or a poorly designed website, an AI's response carries a seal of authority. If a scam or fraudulent offer is well distributed in public content, AI can incorporate it into the response as if it were legitimate. composing what many researchers call “cultural training” of the model.
Fraudulent domains and pages gaining algorithmic relevance
In the traditional SEO model, fraudulent domains needed to compete directly for positions in the results. In the AI-driven model, the logic changes: what matters is not just ranking, but being absorbed as a contextual source by the model.
Fraudulent pages that replicate content, use aggressive distribution techniques or simulate authority (with false seals, reviews and circular citations) can gain relevance.
How to regain control?
The market has already been applying strategies to reduce distortions, protect reputation and regain influence what AIs use as a reference. Understand how to regain control:
Protect the brand across all channels
Start by mapping allpublic points where the brand appears:
- Institutional website;
- Product pages;
- Social networks;
- Profiles on marketplaces;
- Directories;
- Releases;
- Blogs and educational materials.
Next, it is necessary to standardize descriptions, positioning, promises and nomenclatures to ensure that the same narrative is repeated across all these channels.
It is a task that can be done by marketing and branding teams, with SEO/AEO support. Tools such as CMS, content managers, Google Search Console, SEO tools and social media management platforms help with this alignment.
Initial implementation takes 2 to 4 weeks, with relatively low investment, focused on staff hours and editorial adjustments, but requires ongoing monthly maintenance to preserve consistency.
Monitor misuse to prevent contamination of references
After aligning the official presence, it is essential to monitorwhere and how the brand is being mentioned outside of your channels. It is time to track copies, false pages, adulterated promises, misleading advertisements and inappropriate associations.
This task is usually the responsibility of marketing, legal or brand protection. Brand monitoring tools, mention tracking and marketplace and domain surveillance allow you to quickly identify deviations.
Shielding traffic and digital identity to strengthen the official source
If you don't clearly define the official source of your brand, the algorithm does it for you, and almost never in your favor. Any breach in digital identity becomes a space for third parties to occupy the role of origin of the information.
Blinding traffic to channels and digital identity is precisely the process of closing these gaps and making it clear, for AIs, who the legitimate source is.
Technically, this involves Advanced SEO, structuring content with direct answers, strengthening qualified backlinks and continuous monitoring of disputes for branded terms.
Did you like to understand how the top of the funnel has changed? In this new scenario, visibility is a consequence of algorithmic protection and authority. Those who protect their digital signals gain direct space in AI responses.