Recently, I discovered Google’s latest addition to their Google Ads arsenal: the Association metric in Brand Lift Studies. This innovative feature reveals how consumers connect brands with essential attributes, bridging the gap between awareness and consideration.
Google is addressing a critical gap by providing advertisers with a clearer view of how their brand is truly perceived—not just recalled.
What’s new. With this update, Google Ads introduces a fresh “Association” metric within Brand Lift Studies. As advertisers, we can specify a concept, category, or attribute, and Google will survey users to determine which brands they associate with these ideas.
How it works. This revolutionary metric evaluates whether audiences link our brand to a desired positioning—such as “premium” or “sustainable”—offering a sophisticated perspective on brand perception.
Why we care. This new metric allows us to measure brand positioning, not just surface-level awareness or recall. It’s crucial to understand if our campaigns genuinely influence how consumers perceive our brand—vital for those targeting specific attributes or categories.
Between the lines. Previously, Brand Lift focused on awareness, recall, and consideration. Now, Association dives deeper, illuminating whether our messaging shapes how people perceive our brand, beyond mere recognition.
The catch. However, there’s a catch: we can only choose three Brand Lift metrics per study. Adding Association requires us to balance the existing KPIs.
The bottom line. Association provides a strategic perspective on brand building, enabling us to measure whether our intended messages resonate with consumers.
First seen. This update was first spotted by Google Ads expert, Thomas Eccel, who shared the news on LinkedIn.
I’ve discovered that rankings alone no longer guarantee visibility in AI search. In today’s digital landscape, four key signals dictate whether a brand appears in AI-generated responses and how they’re portrayed.
Ranking and visibility have diverged. For years, SEO was all about securing that sweet spot on the SERPs, boosting visibility, clicks, and traffic. This connection is unraveling.
Earlier this year, Ahrefs reported that only 38% of pages featured in Google AI Overviews also ranked in the traditional top 10. Compare this to eight months prior when it was 76%, and you’ll see the shift.
The message is clear: a high rank doesn’t necessarily mean visibility.
Visibility in AI-generated responses hinges on inclusion and the portrayal of your brand upon inclusion, determined by a unique set of signals.
So, how exactly does visibility work within the realm of AI search? There are four critical signals I need to focus on:
Mention order.
Depth of explanation.
Authority signals.
Comparative positioning.
Let me dive deeper into them, starting with mention order.
The order in which AI models list options is crucial. According to a study by Growth Memo and Citation Labs, a whopping 74% of users tend to go with the AI’s top suggestion.
Yet, 26% of users overturn the AI’s order if they recognize a brand they trust. This is quite a change from traditional search behavior. In AI Mode, most users accept the AI’s shortlist without further checks.
However, the mention order is unstable. SE Ranking’s research shows AI Mode only overlaps with itself 9.2% of the time when running the same query thrice, indicating variable sources and order.
Lesson learned: While mention order gives an edge, it’s not a sure thing. Brand recognition can surpass position.
Next, let’s explore the depth of explanation.
Not every mention is equal. Some brands earn only a sentence, while others get full paragraphs detailing their strengths and uniqueness.
This comes down to how much citation-worthy information AI systems have gathered about you.
When Semrush launched its AI Visibility Awards in December 2025, it reviewed over 2,500 prompts using ChatGPT and Google AI Mode. Category leaders like Samsung in consumer electronics didn’t just show up more—they received more in-depth mentions.
Challenger brands, like Logitech in gaming accessories, appeared too, but typically with shorter, focused mentions highlighting a single differentiator.
Pages that are comprehensive, answering “what is it,” “who uses it,” and “how to choose” in one place, rose to the top in AI citations.
Lesson learned: If AI systems only find sparse data on your brand, expect sparse mentions.
Third on the list: authority signals.
AI systems not only cite but also characterize sources by tone, indicating how much confidence they place in a brand’s authority.
HubSpot’s AEO Grader classifies brands as leaders, challengers, or niche players, labels influencing how AI conveys their authority.
Semrush’s data shows that brands identified as leaders exhibit less than 20% monthly volatility in AI share of voice, maintaining consistent authority.
Leaders are described using strong terms like “the industry standard,” while challengers are termed “gaining traction.”
Lesson learned: AI doesn’t just name-drop; it frames your reputation.
Finally, comparative positioning is akin to traditional rankings in AI answers—how you’re positioned among multiple brands.
Amsive’s research demonstrates clear positioning hierarchies within sectors.
In banking, Bank of America leads, followed by SoFi and LightStream.
In healthcare, Mayo Clinic stands out significantly.
Kevin Indig’s research highlights how users self-select based on AI’s framing, regardless of actual capabilities.
Lesson learned: It’s not about being number one; it’s about owning a niche in AI’s mental map.
Traditional rankings’ correlation with AI visibility is minimal. The concept of query fan-out explains why visibility dropped so swiftly.
During an AI Overview, Google processes not just the top pages for a query but various sub-queries to synthesize a complete response.
This means your page might rank first for one query but may be overlooked if AI finds more relevant passages elsewhere.
Research shows Google’s Gemini 3 update altered approximately 42% of cited domains, making traditional rank positions less predictive.
Where does AI traffic land? Interestingly, a substantial portion of ChatGPT traffic eventually ends up on Google. Users seek answers from ChatGPT, then confirm their findings on Google.
Most prompts to ChatGPT are too specific for traditional keywords, intensifying the shift.
So, how can I measure visibility in AI answers?
Track citation frequency to gauge how often your brand appears in AI answers.
Measure brand mention rate for category penetration.
Focus on recommendation rates, especially in B2B and high-consideration sectors.
Analyze sentiment and context of mentions to evaluate impact.
Citation position provides an edge, even if it’s not organic rank.
The 2026 measurement model demands dual tracking—traditional and AI-focused metrics for accurate visibility insights.
New tools have emerged for this purpose, complementing but not replacing traditional SEO tools.
For citation tracking, platforms like Profound and Peec AI keep tabs on cited URLs across AI responses.
For brand analysis, tools like Semrush’s AI Visibility Toolkit check mention frequency, portrayal, and recommendations.
For competitive positioning, Bluefish and HubSpot’s AEO Grader assess your brand’s AI categorization against competitors.
Traditional rank obsession persists, but visibility in AI requires a broader view with a distinct measurement model.
I’ve always found brand positioning to be an intricate dance of claims, proofs, and strategic framing. While AI can validate claims, it won’t decide on the conclusions that best elevate your business. Let me share how framing transforms proof into brand loyalty.
In today’s digital world, every brand has its arsenal of claims and underlying proofs scattered across its digital presence. AI engines like ChatGPT and Google’s AI can verify these, but they hold no narrative power to create an engaging story for your brand.
Often, there’s a disconnect between what your audience desires and what brands or AI understand. The missing link? A powerful frame that converts disjointed data into a compelling brand narrative.
Here’s where I introduce the claim-frame-prove (CFP) approach. Claims and proofs are mechanical, but framing adds that strategic layer necessary to craft your brand’s narrative.
Claims and proofs are mechanical tasks AI can handle, but creating a strategic frame is your brand’s unique prerogative.
Building your brand through CFP means understanding that AI can link known facts but cannot make that creative leap your brand requires. AI connects the dots logically but lacks the ability to reach a commercially beneficial insight.
Consider the alphabet analogy: while C is an apparent commercial reach, J represents a nuanced insight, and Q symbolizes a bold vision your brand can aspire to.
I’ll illustrate with some personal examples. My work in answer engine optimization demonstrates this journey from mere understanding to unique brand positioning.
A + B → C
A: I coined answer engine optimization in 2017. B: I also run a brand engineering firm. AI arrives at the simple, logical conclusion: I’m connected to AEO implementation. While true and functional, it lacks depth.
A + B → J
By pushing further, the narrative evolves. J: I might be the only practitioner with extensive insights from a decade’s worth of operational data.
This move from A and B to J is vital. It’s about identifying which non-obvious insight fosters brand growth and constructing a logical link from accepted realities to this aspirational leap. That logical bridge is essential for AI to consider it factual, rather than mere self-promotion.
Why AI Can’t Decide What’s Best for Your Brand
AI won’t instinctively choose the best narrative for your brand—that responsibility is yours. Even as AI gets more sophisticated, it lacks the commercial insight to select paths that benefit your brand uniquely.
A creative marketer makes two critical moves: discovers imaginative insights and aligns them strategically with brand goals. Not a feat even the most evolved AI can match, as it lacks the personal stake in this narrative crafting.
I use an approach called “empathy for the machine,” which helps brands create content that AI can easily comprehend and relay, rather than leaving connections for AI to interpret independently.
This method enables a three-tiered communication with AI, evolving from mere proof of claims to frames that the AI can transmit seamlessly to your audience.
Level 1: Scattered Proof of Claims
Many brands rest here—proofs exist in separate spaces, disconnected, leaving AI to infer relationships. The reality is that without explicit links, much of this value is lost.
Without these connections, AI struggles to assert your brand’s credibility, potentially leaving valuable insights untapped.
Level 2: Connected Proof of Claims
At this stage, connections via copy, hyperlinks, and schema are established, significantly reducing the AI’s workload and increasing your brand’s credibility.
Proper connections allow AI to confidently present your brand’s claims as facts, significantly enhancing its visibility and competitive positioning.
Level 3: Framed Proof of Claims
This is where strategic framing really takes shape—bridging claims, proofs, and strategic insights to position your brand distinctly in the market.
With well-framed claims, AI doesn’t just confirm but actively advocates for your brand’s superiority, making your voice the narrative AI conveys to the world.
I’ve noticed a common misconception that GEO is merely a technical issue. However, upon scrolling through LinkedIn or X for just a short while, you’ll quickly stumble upon the latest viral GEO hack.
For example, advice like creating an AI info page so that LLMs can effortlessly grasp your brand, or generating markdown versions of your content to boost AI visibility, frequently surfaces.
There’s also the idea of commissioning an automated Claude audit to scrutinize your robots.txt file and produce an llms.txt file for you.
Yet, the truth is, these tactics often have a marginal impact because they fail to address the way LLMs determine which brands to endorse.
The performance of GEO is influenced more by the consistent positioning, categorization, and validation of your brand across the web, rather than by minor technical modifications.
If we’re honest about it, GEO performance is chiefly driven by brand positioning and consensus. Thus, it’s not surprising when many well-publicized strategies don’t deliver the expected results.
When searching for GEO tactics aimed at LLM visibility, the internet serves up the same recycled ideas.
Unfortunately, while the suggestions aren’t necessarily wrong, they are mostly elementary. Many people misunderstand and even exaggerate them. For instance, Google’s recommendation to use FAQs with schema has led to companies overloading their content with irrelevant FAQ sections, thinking it will enhance GEO.
As a result, they end up including pointless questions that don’t benefit the end users. This isn’t just an inefficient tactic, but it can also detract from user experience, as evidenced by misaligned FAQ sections.
Another commonly over-hyped method involves placing ‘key takeaways’ at the start of each article. Although it may aid human readability, there’s no substantial proof that it significantly boosts AI visibility.
Furthermore, some strive to over-format pages for LLM readability by forcing content into constrained Q&A formats or infusing bullet points where they don’t belong.
People often believe that LLMs require extensive formatting assistance to retrieve content, resorting to copywriting tricks like ‘chunking,’ which can over-complicate editorial processes.
Then there are those who chase Reddit for GEO, leading to a proliferation of spamming for citations, despite clear warnings from experts like Eli Schwartz against such practices. This misperception highlights that GEO isn’t merely a technical issue.
Reddit’s strength lies in its authentic user voices, a reason why moderators actively target inefficiencies such as astroturfing or ‘SEO shaping’ where software evaluations occur.
GEO is inherently a problem connected to brand positioning and category alignment rather than just technical SEO.
GEO requires strategic efforts from the executive level for the best results. While technical enhancements are a necessity, the greater gains come from harmonizing brand alignment, messaging, and reputation management.
This means GEO isn’t solely the responsibility of the SEO team but also a collaborative effort involving branding, PR, partnerships, and customer marketing.
As Ross Hudgens recently pointed out, inconsistency between sources can hinder LLMs from creating a unified narrative about a brand.
Category alignment is another critical aspect. Even with high web rankings and URL citations, a recommendation may still elude brands unless their alignment within a category is optimal.
The AI landscape acts as a ‘normalizer,’ diminishing the prowess of past SEO tactics that focused purely on rankings and clicks.
Tellingly, listicles can neither brute force brands into AI recommendations nor substitute genuine industry recognition. Citations alone are not enough if accompanied by no recommendation.
Therefore, reporting on ‘citations’ merely as a success metric is misleading without corresponding brand recommendation. The AI overview is more likely to suggest brands that justly deserve the spotlight.
Indeed, many brands remain unaware of how they’re represented across LLMs. Understanding how LLMs compile data about your brand amenities can ultimately influence your GEO approach.
To amplify understanding, engage with bottom-of-funnel prompts, systematically analyze responses and sources, and corroborate your representation with insightful research.
Recognize that in high-competition categories dominated by third-party recognition, you may be compelled to participate in affiliate programs for visibility.
Technical excellence still underpins successful GEO strategies. However, fundamental elements like XML sitemaps and internal linking merely lay the groundwork, rather than driving GEO itself.
Focus on brand positioning and category alignment rather than isolated technical SEO audits.
Consider whether LLMs genuinely recommend your brand and ensure that your messaging reflects the appropriate category and customer perception you wish to cultivate.
Review third-party influences versus your own content to understand their role in shaping brand visibility. Develop a coherent narrative across various channels to reinforce your market status.
It’s crucial to rethink strategic moves like forcing visibility through listicles and formatting tricks that aren’t yielding recommendation statuses.
Ensure that your content truly assists buyers in comprehending your unique positioning and distinct advantages.
Ultimately, GEO goes beyond the technical realm into broader brand ecosystems that shape perceptions and narrative control.
Stop pursuing quick fixes with GEO hacks. Instead, prioritize building a consistent, clear, and compelling brand story that resonates across platforms.
