The importance of brand language in times of AI and agents.
Why language is becoming the ultimate differentiator.
Brands are facing a double disruption. On the one hand, conversational or generative AI is changing the way people search for information and prepare purchasing decisions. On the other hand, AI agents are beginning to act as customers themselves. They research, compare and buy autonomously. Both developments hit brands at a sensitive point: their language. Because in a world in which websites are visited less frequently, search engines are clicked less often and purchasing decisions are increasingly prepared or made by algorithms, the linguistic identity of a brand is changing from a soft brand value to a hard competitive factor.
This article argues that brand language - understood as the systematic interplay of word choice, tonality, style and structure of a brand - deserves a fundamental strategic reassessment in the AI era. Not because it was previously unimportant. But because the conditions under which it works have fundamentally shifted.
What brand language can do and what it didn't have to do before.
Corporate language, the established technical term coined by Armin Reins in 2006, describes the linguistic dimension of brand identity. Just as a corporate design gives a brand an unmistakable visual face, corporate language gives it a characteristic voice. The essential elements are well known: Word choice and vocabulary (the "what"), tonality (the "how") and style including structure (the "with what"). In extended practice, there are also aspects such as comprehensibility, spelling, UX writing principles or guidelines for directly addressing individual target groups.
The objectives pursued with a brand language are just as clear: strengthening brand identity, building trust through consistency, differentiation from the competition, creating emotional loyalty and greater efficiency in communication. Brands such as IKEA, Sixt, Porsche, Hornbach and Fritz-Kola have been demonstrating the value of a consistent brand language in German-speaking countries for years. Studies of recipients of brand communication suggest that they appreciate the value of good brand language - presumably because they prefer clear communication to interchangeable noise.
And yet the proportion of companies with a clearly defined corporate language is estimated to be well below 50 percent. Unfortunately, there is no data on this. The difference between the high attributed importance and the low actual implementation points to untapped potential. This deficit is now being exacerbated by the development of AI, because brand language is suddenly no longer just a topic for brand strategists and copywriters. It is becoming a systemic requirement.
The first revolution: When search becomes conversation.
Stefano Puntoni, Professor of Marketing at the Wharton School, presented an analysis in the Harvard Business Review at the beginning of 2026 that describes two parallel upheavals. The first concerns search behavior: Conversational AI, i.e. systems such as ChatGPT, Gemini or Claude, is increasingly displacing traditional web search as the primary way people find out about products, services and brands.
The mechanics of this shift are both simple and momentous. In the old model, someone searched on Google, got a list of links, clicked through various websites, compared options and finally bought. In the new model, someone asks a chatbot and gets a complete answer without ever visiting a brand's carefully crafted web presence. Google's own AI overviews at the top of search results amplify this effect: many people read the summary and no longer scroll down to the links.
The research on this is becoming clearer. A study by Boston University shows that traffic on Stack Overflow - a platform for software developers - plummeted after the launch of ChatGPT because developers received their answers directly from the chatbot. Based on ComScore data, researchers from the London Business School and UCLA found that online searches fell by around 20 percent after the introduction of ChatGPT. Smaller websites are more affected than large ones because they lack the brand awareness that motivates direct navigation.
This development has a direct consequence for brand language: the contexts in which a brand can be experienced linguistically are changing fundamentally. If people land on brand websites less frequently, the language used there loses its contact surface. At the same time, the question of how a brand is represented in AI-generated responses and whether it appears there at all is gaining in importance.
Puntoni describes the resulting paradigm shift as a transition from SEO to GEO - from search engine optimization to Generative Engine Optimization. The techniques that worked in the search engine era - keyword optimization, link building, metadata - do not simply translate into the world of conversational AI. Instead, structured data, clear content categorization and comprehensive, directly answering content are becoming more important. It's no longer just about what a brand says, but how it structures its content so that AI systems can process, contextualize and recommend it.
Brand language thus becomes a structural asset. A precise, consistently defined and cleanly documented language is not only a quality feature for human readers. It is increasingly becoming a prerequisite for AI systems to correctly capture and reproduce a brand.
The second revolution: when algorithms become customers.
The second upheaval that Puntoni describes goes even further. And it affects brand language in a way that many have not yet anticipated. AI agents are beginning not only to provide information, but also to make independent purchasing decisions. This is shifting a fundamental category of marketing: the question is no longer just "Who is your customer?", but "What is your customer?"
The scenario is already tangible: A busy person tells his AI assistant that he needs a new laptop, budget two thousand euros, battery life and display quality are important. The agent researches options, weighs up alternatives, negotiates prices and makes the purchase. No one has ever visited a product page or read a review. The entire customer journey took place within an algorithm chain.
What does this mean for brand language? First of all, something sobering: A linguistic identity developed solely for human emotional resonance misses a growing target audience. When an AI agent evaluates a product, they are not interested in a charming headline or an inspiring brand claim. It needs well-structured, clear, machine-readable information. Product descriptions that are optimized for emotional impact but lack structured clarity will be a disadvantage in this new world.
At the same time, a new field of research is emerging, which Puntoni calls "bot psychology". The findings are remarkable: researchers have shown that AI systems exhibit an AI-AI bias. In other words, they tend to rate AI-generated content higher than human-generated content. AI agents exhibit position effects, i.e. they prefer products depending on their position on a page, and these effects differ from model to model. GPT prefers the first position, Claude the center, Gemini the right side. For any brand that wants to optimize its digital presence, these are not academic curiosities, but operational variables.
The implication for brand language is clear: in future, it must work for a DUAL audience. Content must resonate with human readers and at the same time be structured in such a way that AI systems can process and quote it. This doesn't mean writing for machines instead of humans. But it does mean recognizing that the same content is absorbed and processed through fundamentally different pathways.
What AI "understands" about brands and what it doesn't.
To understand the practical implications of these shifts, it is worth taking a look at the technical reality. Large Language Models (LLM) are based on probabilistic models. They learn probability distributions over sequences of words or tokens from extremely large text corpora. Text generation contains a stochastic element: even with identical prompts, the model can produce different outputs. Both are relevant for brand language. The stochastic nature tends to lead to generic, common formulations, i.e. the opposite of a distinctive language. And the random effect is at odds with the desired consistency in the use of distinctive brand terms.
No LLM can represent a brand linguistically correctly from a standing start. The initial situation is the same for all companies: the model only knows what is generally available on the web. This is often content from forums, review portals or press reports that do not necessarily represent the correct brand status. Or, in the worst case, may even contradict it.
Several steps are necessary to enable an AI system to generate brand-compliant speech: the integration of brand values and brand personality, the provision of text patterns as a reference, the definition of context rules and, last but not least, continuous feedback. In practice, some of these steps are implemented via system prompts in the company's own AI instances, others via dedicated brand voice modules in content platforms. However, this technical integration requires something that an astonishing number of companies do not yet have: a clearly defined, documented and operationalizable brand language.
There are also systemic limits to AI language processing in the brand context: the context dependency of linguistic meaning, cultural differences in reception, the difficulty of recognizing complex emotions such as irony or melancholy, as well as issues of bias, authorship and data protection. These boundaries will shift as technology develops, but they will not disappear. They do not make brand language superfluous. On the contrary, they make a precise definition all the more necessary because AI needs a clear frame of reference in order to operate meaningfully within these boundaries.
From assistants to agents: The new operating model of the brand.
The development of AI chatbots, virtual assistants and autonomous agents marks a qualitative leap that once again fundamentally changes the requirements for brand language. Back in 2019, Satya Nadella formulated the expectation that every brand would need its own agents in future that could speak directly to customers and communicate across different digital systems. This prediction was ahead of its time. Today, with the rapid development of agent-based AI systems, it is beginning to materialize.
AI agents differ from assistants in their autonomy, goal orientation and adaptability. They set themselves sub-goals, plan actions, make independent decisions and learn from experience. For brands, this means that their linguistic identity is no longer expressed solely in texts and campaigns, but in autonomous systems that interact with people and other systems in real time. A brand agent who processes customer inquiries, makes product recommendations or receives complaints must not only reproduce the brand language, but must also be able to apply it appropriately in new, unforeseen contexts.
The warning about the risks is not theoretical. The cases are well known: Burger King had to withdraw AI-generated advertising texts because they sounded incomprehensible and off-brand. An AI chatbot from DPD UK insulted its own company as the "worst delivery service in the world". Air Canada was ordered to pay damages in court because an AI assistant had made false statements about ticket prices, and the court did not accept that the company delegated responsibility to the system. These examples are now 2-3 years old and date from the early stages of immature implementations. But they illustrate an enduring structural problem: a clear, operationalized brand language is needed as part of the framework for AI applications to operate in a company-compliant manner.
The audio dimension: brand language becomes audible.
One aspect that is still often underestimated: brand language will increasingly be heard rather than read. After the relative failure of the first wave - Amazon's Alexa division was massively downsized in 2023, Apple allowed Siri to wither away for years - the development of voice technologies has received a new boost through integration with modern foundation models. The advances made back then in Automatic Speech Recognition, Natural Language Processing and text-to-speech are now fully benefiting the current models and ensuring a sometimes astonishingly natural conversational ability.
Statistically, speaking is around three times faster than typing, and in the history of UX, applications with the least effort for the user have won out in the end. The combination of old linguistic strength and new AI intelligence gives voice interfaces a new, this time more plausible and more effective start. For brands, this means that their language must not only work in written form, but also as an acoustic experience. Tonality, rhythm, sentence structure and word length gain an additional sensory dimension. A brand language that is convincing on paper but sounds wooden or robotic when read aloud will fail in a voice-first interaction.
What follows from this: Seven principles for practice.
The convergence of these developments - conversational search, algorithmic customers, autonomous agents, voice interfaces - can be condensed into concrete principles of action:
Firstly: Understanding brand language as a management task. Transformation affects technology, content, product and customer experience in equal measure. Organizations that keep these capabilities in silos will be slower than those that integrate them at leadership level. Brand language can no longer be a side issue of the marketing department.
Secondly, audit your own search exposure. What proportion of traffic and conversions comes from traditional search? High reliance on information-seeking traffic means high short-term risk. Brands need to understand how and whether they appear in AI-generated responses.
Thirdly, build up expertise in Generative Engine Optimization (GEO). The field is young and the rules are being written. Companies that learn early to structure content for conversational AI will have a real head start. This doesn't require new marketing, but a new way of thinking about content architecture.
Fourthly, focus on what AI cannot replicate. The insight from the comparison between Stack Overflow and Reddit is strategically valuable: platforms that are built on information delivery suffer from AI substitution. Platforms that focus on community, emotional connection and shared experience are protected. Brands should invest in precisely these dimensions - and align their language accordingly.
Fifth: Be prepared for AI customers. How would an algorithm evaluate your own product? What structured data would it need? How does your own value proposition translate into the factors that AI systems use to weigh things up? Asking these questions does not mean abandoning human communication. It means developing a second, parallel communication architecture.
Sixth: Think content for a dual audience. Texts must be effective for human readers and at the same time be able to be processed and quoted by AI systems. This is not a contradiction, but it does require discipline: clear structure, precise terms, unambiguous statements, clean metadata.
Seventh: Observe the research. Whether bot psychology, AI-AI bias, position effects or the dynamics of GEO: academic research is generating new findings at a rapid pace. The ability to track these findings and derive operational consequences from them is becoming a competitive advantage.
The real question.
Marketing has survived disruptive transitions before: from print to broadcast, from broadcast to digital, from desktop to mobile. Each transition has produced winners and losers. The current transition goes deeper. We are changing not only how marketing reaches human decision makers, but potentially who or what those decision makers are.
In this environment, brand language is transformed from a brand communication tool into an infrastructure decision. A clearly defined, consistently documented and technically operationalizable language is the basis for AI systems to be able to represent a brand correctly, consistently and in a differentiating way. Regardless of whether they generate texts, answer search queries, conduct voice dialogs or act as autonomous agents representing a brand.
The companies that invest in this groundwork now are creating the conditions for an era in which brands no longer only speak for and with people, but also through machines, with machines and to machines. Those who have not defined this linguistic identity will leave the definition to others - and in case of doubt to an algorithm that will not distinguish between distinctive and generic.
Uncertainty is where opportunities arise. But opportunities can only be seized by those who are prepared. And preparation begins, as is so often the case, with a simple but challenging question: Who are we and is this shown appropriately and effectively through our language or auditory appearance?
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Sources:
Reins, Armin: Corporate Language. Published by Hermann Schmidt, 2006.
Puntoni, Stefano: "AI Is Upending Marketing on Two Fronts." Harvard Business Review, February 2026.
Reins, A., Czopf, G. & Classen, V.: Corporate Language: Das Praxisbuch. Published by Hermann Schmidt, 2020.
Christian Daul, "Brand language", whitepaper Radiozentrale, 2024
PR Newswire (2021): Survey: Brand language soars in importance.
McKinsey & Company (2023): The State of AI - Global Survey.
London Business School / UCLA: ComScore study on the decline in online searches after ChatGPT adoption.
Boston University: Study on the decline in traffic on Stack Overflow.
