AI and Branding: The Era of Algorithm Positioning

Artificial intelligence has become the primary source of information. That is, the first impression of your brand is now generated by an algorithm. In a world where millions of people interact daily with models like ChatGPT, Gemini, or Claude to get information, solve problems, or make decisions, brand identity is increasingly shaped by systems that do not consult the companies themselves, but instead synthesize what they understand about them. They assign attributes, rank relevance, and summarize brands in a single phrase. No warnings. No nuance.

This new landscape forces us to rethink, from the ground up, what it means to be well positioned today. For years, branding was the realm of creativity. Then, with the rise of search engines, it also became a matter of technical optimization. Now, the playing field has shifted again: brands are no longer just competing for attention, they are competing for representation. And that brings new rules, new risks, and above all, a new way of understanding reputation.

Visibility no longer depends on how much content a brand produces or how often it states its purpose. It depends on how present that information is in the sources models consider trustworthy. It is not enough to say who you are, you must ensure that this definition is recorded, understood, and faithfully reproduced by an AI operating under logics different from our own.

What is concerning is that, often, companies do not  even know what those logics are. These models learn from millions of texts available online, but not necessarily from official sources. They analyze what others say, infer connections, and prioritize certain messages over others. A narrative is built in that synthesis. One that may be outdated, incomplete, or biased, but will be read as truth by those consulting it.

We are talking about a strategic challenge. Brand management can no longer be limited to curating what we publish on our own channels or what the media says. It must understand how this new algorithmic layer of reality is trained and responds. Because reputation is also shaped there, and often with more impact than a front page or viral post.

According to a study we conducted at LLYC, only 47 of the Top 100 Mexican brands are correctly identified by GPT-4, and the brand image projected by AIs has less than 60% similarity with the image communicated by the companies. Beyond not identifying them correctly, the models often misrepresent the brands’ relative positioning within their sectors.

There are leading brands that, despite having strong media presence, clear strategies, and powerful storytelling, are being misrepresented — or ignored — by these models. This does not mean they are doing things wrong, but rather that they are not yet aware that if they previously created and positioned content with consumers or other stakeholders in mind, they must now increasingly do so with AI as the reader in mind. This means companies must now ask a new question in their reputation or brand management strategy: How does AI read me? What does it say about me when no one is watching? Does it identify my products or services? What version does it deliver when someone asks about my company, my industry, or my competitors?

Answering these questions requires collaboration between communication, marketing, technology, and data teams. It demands a critical review of digital assets and of the environments where knowledge is now built: open databases, authoritative media, academic sources, and trusted repositories. Because that is where the models look for signals to build their responses. Not always in our social media. Far less often on our websites.

The challenge now is to create content that withstands algorithmic reading. Content that is coherent, verifiable, and placed in relevant contexts. That forms part of a broader narrative, strategically distributed. Because AI does not make decisions based on a single post. It does so based on patterns, correlations, and repetition.

The paradox is that the more sophisticated the technologies become, the more valuable consistency becomes. The brand that manages to say the same thing across different spaces, formats, and contexts, is the one that will be best understood. The one generating noise without direction, on the other hand, risks becoming white noise to the machine.

We are entering an era where brands must be properly told and translated, accurately represented by an invisible layer that now intermediates much of the information experience. And that translation requires precision, strategy, and humility because we cannot control what algorithms say, but we can influence what they learn.

Understanding this new terrain does not mean abandoning the human factor. Quite the opposite. It means reclaiming a more complete view of reputation and brand positioning, one that does not separate the tangible from the digital, or emotion from interpretation. A reputation that lives and is shaped by data.

The brands that first understand this logic, not just technical but cultural, will be the ones leading the conversation. The others will keep talking loudly, but will not be heard. Because the decision is no longer just in the hands of the audience. It is increasingly made by a language model that decides what is worth saying, and what is not.