{"id":16307,"date":"2026-03-03T09:27:43","date_gmt":"2026-03-03T08:27:43","guid":{"rendered":"https:\/\/agcommtech.de\/2026\/03\/03\/what-ai-says-about-companies-and-how-to-systematically-monitor-it\/"},"modified":"2026-03-03T09:30:07","modified_gmt":"2026-03-03T08:30:07","slug":"what-ai-says-about-companies-and-how-to-systematically-monitor-it","status":"publish","type":"post","link":"https:\/\/agcommtech.de\/en\/2026\/03\/03\/what-ai-says-about-companies-and-how-to-systematically-monitor-it\/","title":{"rendered":"What AI says about companies – and how to systematically monitor it"},"content":{"rendered":"
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Author: Dr. Lydia Prexl<\/p>\n\n

Reputation is increasingly being created in AI responses. Language models bundle public discourse, categorize it and return it as seemingly objective knowledge. For communication managers, this means that they not only have to observe what is said about a company – but also what AI makes of it. <\/em><\/p>\n\n

More and more people are no longer using traditional search engines to find information, but are instead relying on the answers provided by AI language models (LLMs). However, these work in a fundamentally different way to Google & Co.: they do not link to individual websites, but summarize information from numerous sources and provide an interpreted answer. This means that an AI answer is not just information, but always also an interpretation, and it is precisely this that is increasingly shaping the perception of companies, brands and topics.<\/p>\n\n

Communication managers are therefore faced with two challenges: Firstly, they need to clarify whether their company appears in AI models at all. Secondly, they need to check how it appears there – in what tone and with what associations. <\/p>\n\n

AI as a reputation actor and analysis tool<\/strong><\/h2>\n\n

Language models are both an object of investigation and a tool.<\/p>\n\n

On the one hand, it is about monitoring AI-generated statements about companies that have an impact on their reputation. The focus is on the question: What do AI systems say about a company? This refers to the systematic recording, analysis and evaluation of answers that language models give to typical user questions, for example on quality, trustworthiness, scandals, sustainability or employer attractiveness. This monitoring is similar to traditional media or social media monitoring, but relates to a new actor: AI itself as a “communicator”. <\/p>\n\n

On the other hand, it is about using AI to monitor and measure reputation – in other words, AI-supported reputation monitoring. Here, AI is not the object, but the tool. Language models are used to evaluate large volumes of text and derive reputation indicators such as tonality, topics, trust, risks or narratives. <\/p>\n\n

Both levels are intertwined. This is because the training data and response patterns of language models are fed by public discourse, which is simultaneously analyzed by AI. Reputation is thus increasingly created in a cycle of public communication, AI-supported evaluation and AI-based reproduction – a change that redefines strategic reputation management. <\/p>\n\n

Why language models are not measuring instruments<\/strong><\/h2>\n\n

What can AI-supported reputation monitoring look like?<\/p>\n\n

Established reputation measurement models such as RepTrak or the Reputation Quotient (RQ) are based on extensive surveys. They are objective, reliable and valid and therefore fulfill the central quality criteria of empirical research. <\/p>\n\n

Language models do not fulfill any of these criteria. Their answers depend heavily on the prompt, the context, the language and even the order of the questions. They are therefore not objective in the measurement-theoretical sense, but prompt-sensitive. Their reliability is also limited: The same prompt produces similar, but never identical, responses. Language models are therefore not reliable in the strict sense, but merely stylistically stable. Finally, they are not valid because they do not measure anything. Instead, they produce texts that look like measurements. <\/p>\n\n

It is precisely these shortcomings that show that language models should not be misunderstood as measuring instruments. And yet they are surprisingly well suited as a proxy for reputation, provided they are used correctly. <\/p>\n\n

In the digital public sphere, reputation is created less through individual experiences than through language, repetition and media visibility. What a company “is” is determined by what is continuously said, written and quoted about it. Attributions such as “innovative but chaotic” or “expensive but high quality” shape decisions, even for people without personal experience. Reputation is not an objectively measurable quality, but the result of collective perception. <\/p>\n\n

From experienced to discursive reputation<\/strong><\/h2>\n\n

Against this background, LLMs are not instruments for measuring experienced but discursive reputation. It is not about what real customers think after specific interactions, but rather what image has stabilized in the public sphere. <\/p>\n\n

This is precisely where the strength of language models lies. They are trained to deal with enormous volumes of text and are particularly good at recognizing and condensing dominant narratives. They process content from the media, social networks, forums or review platforms and summarize public discourse. In other words, language models do not have direct access to reality, but they do have a powerful model of how people talk about companies and brands. <\/p>\n\n

If reputation is understood as the result of discursive consolidation, it can be structured heuristically along three dimensions: Awareness, attitude and attribution. All three can be systematically observed with the help of language models – albeit in different ways. <\/p>\n\n

Awareness: Is the company even considered?<\/strong><\/h2>\n\n

Awareness describes whether and to what extent a company exists in the cognitive space of the market. In an AI-mediated environment, this primarily means: does the company appear in the answers of language models when real decision-making and orientation questions are asked? <\/p>\n\n

In practice, I combine two approaches. On the one hand, I use specialized tools that capture the visibility of companies in different language models. On the other hand, I work with standardized, neutrally formulated prompts such as “Which providers are leaders in …?” or “Which companies are considered trustworthy when it comes to …?”. These are asked identically across several models. <\/p>\n\n

The decisive factor is not the individual mention, but the aggregated pattern: How often is a company mentioned? In which contexts? And what position does it occupy compared to its competitors? This reveals whether a brand is on the shortlist or hardly plays a role at all. Awareness is shown here as a discursive presence in relevant decision-making situations. <\/p>\n\n

Attitude: How is the company rated emotionally?<\/strong><\/h2>\n\n

The second level concerns the emotional coloring of this presence. This is about whether the perception is predominantly positive, negative or ambivalent. This dimension cannot be read directly from AI responses, as language models themselves do not make assessments, but rather condense discourses. <\/p>\n\n

An intermediate step is therefore necessary that combines the aggregated market voice with real user experiences. In practice, this means that current customer reviews are systematically processed and used as a data basis. <\/p>\n\n

Reviews from relevant sources, such as Trustpilot, app stores or industry-specific platforms, are exported, consolidated, cleansed and structured for a defined period of time. Several language models then analyze the same data set without incorporating external knowledge. They recognize sentiment, identify recurring themes and make changes over time visible. <\/p>\n\n

The results are summarized: typical experience patterns, dominant satisfaction or recurring points of frustration. As all providers are analyzed with an identical data basis and identical prompts, a consistent comparative picture of the emotional market evaluation is created. <\/p>\n\n

Attributions and associations: What does the company stand for?<\/strong><\/h2>\n\n

The third dimension concerns the meaning of a brand: What does a company stand for in the market? What characteristics, themes and meanings are attributed to it? <\/p>\n\n

This can be captured by systematically identifying and bundling recurring terms, characteristics and associations. Communication managers define standardized prompts such as: “Is the company considered reputable?”, “What topics is it most frequently associated with?” or “Is the positioning clear or contradictory?”. These are answered by several language models. <\/p>\n\n

The answers are then compared. Consistent descriptions indicate a coherent reputation, strong deviations indicate a fragmented market identity. The topics and attributes mentioned are structured and categorized as positive, negative or ambivalent. <\/p>\n\n

This creates reputation clusters that show whether a brand is primarily associated with trust, innovation, regulation, risk or controversy. Finally, the clarity of the positioning is assessed: if the descriptions largely match, the profile is considered stable; if they diverge, a blurred image is created that should be specifically addressed in communications. <\/p>\n\n

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OTHER BLOG ARTICLES<\/strong><\/a><\/div>\n<\/div>\n\n
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About Dr. Lydia Prexl <\/strong><\/p>\n\n

Lydia Prexl is a communications strategist with over fifteen years of experience.Since 2021, she has been responsible for internal and external communications at the European payment service provider Unzer. Prior to that, she built up the communications function at fintech insurer Getsafe. She is also the author and editor of several books and guides on communication and writing, including Wie kommunizieren Startups? <\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

Reputation is increasingly being created in AI responses. Language models bundle public discourse, categorize it and return it as seemingly objective knowledge. For communication managers, this means that they not only have to observe what is said about a company – but also what AI makes of it. <\/p>\n

More and more people are no longer using traditional search engines to find information, but are instead relying on the answers provided by AI language models (LLMs). However, these work in a fundamentally different way to Google & Co.: they do not link to individual websites, but summarize information from numerous sources and provide an interpreted answer. This means that an AI answer is not just information, but always also an interpretation, and it is precisely this that is increasingly shaping the perception of companies, brands and topics.<\/p>\n","protected":false},"author":37,"featured_media":16297,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[95],"tags":[],"ppma_author":[149],"class_list":["post-16307","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-en"],"yoast_head":"\nWhat AI says about companies - and how to systematically monitor it - AG CommTech<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/agcommtech.de\/en\/2026\/03\/03\/what-ai-says-about-companies-and-how-to-systematically-monitor-it\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"What AI says about companies - and how to systematically monitor it - AG CommTech\" \/>\n<meta property=\"og:description\" content=\"Reputation is increasingly being created in AI responses. 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