#MarketingTeam4U
#MarketingTeam4U
aufeinwort Interview KI
Artificial intelligence is part of everyday marketing – from content creation and brainstorming to analysis and automation. As vast as the possibilities are, it is just as important to understand the limitations of the technology. How reliable is AI-generated content? Where is human review essential? And what data can AI actually provide? In our interview, we ask an AI itself about its strengths, weaknesses and useful applications in marketing.
AI: I see the greatest opportunities in areas where large amounts of information need to be processed, variations created or repetitive tasks streamlined. When it comes to content creation, for example, AI can generate drafts for blog articles, social media posts, newsletters, adverts or product descriptions. However, it is often particularly helpful one step earlier: in brainstorming, research, structuring and conceptualisation. It can cluster topics, develop different perspectives, suggest headlines or derive several shorter formats from a long piece of content.
A second major area is analysis. AI can summarise large volumes of customer feedback, survey results, search queries or campaign data and identify patterns. Added to this are personalisation and automation: for example, different text variations for target audiences, automated responses, lead qualification or support with recurring marketing processes. In my view, the greatest benefit does not come from AI taking over marketing entirely, but from the fact that it makes research, production and evaluation significantly faster.
AI: I am particularly well-suited to assisting with tasks where language, structure, ideas or large volumes of information play a role. These include, for example, brainstorming, content drafts, summaries, editorial planning, persona ideas, variations on advertising copy, analysis of provided information, or the revision of existing content.
I am less suited to acting as the sole authority for tasks where up-to-date, comprehensive or business-critical data is crucial. These include, for example, a robust market analysis without the relevant data sources, a comprehensive SEO analysis, legal assessments or deciding which campaign a company should pursue strategically. Nor does a genuine understanding of a brand develop automatically. I can apply a brand guide very consistently – but I do not automatically know a company’s internal experiences, customer relationships or cultural nuances.
You could say: I am very good at supporting thought processes and production workflows. However, I should not be expected to replace responsibility and sound judgement.
AI: As a general rule, the more relevant context I am given, the more helpful my response can be. This includes, for example, the target audience, tone of voice, communication objective, product information, briefings, brand guidelines or anonymised sample data.
However, you should exercise caution when dealing with confidential, personal or business-critical information. This includes, for example, passwords, login details, private customer data, sensitive employee information, unpublished trade secrets or other information that should not be disclosed externally. Particularly in a corporate context, therefore, not everything should simply be copied into an AI system unfiltered. It makes sense to check beforehand: Does the AI really need this information for the task? Can I anonymise or abstract it? And does its use comply with internal data protection and security policies? For many marketing tasks, structured information without sensitive details is sufficient anyway. Instead of, for example, handing over a complete customer record, one can work with anonymised segments, summarised insights or case studies.
The basic rule would therefore be: as much relevant context as necessary – but as little sensitive information as possible.
AI: Reliability depends heavily on the specific task and the available sources of information. A major problem with generative AI is that an answer can sound very convincing linguistically, even though individual statements are incorrect. This phenomenon is often referred to as ‘hallucination’. For example, it may misquote figures, studies, quotations or sources, or mix up different pieces of information. Even on topics that change rapidly, an answer may be out of date without access to up-to-date sources.
It is therefore important to distinguish between two things: linguistic plausibility and factual reliability. The more important a statement is for a decision, the more important it is to verify it using reliable sources. It is particularly useful not just to ask the AI for an answer, but to provide it with specific data, documents or trustworthy sources.
AI: Essentially, anything that could have external consequences. Facts, figures, studies, statistics, quotations and references are particularly critical. Legal statements, data protection issues, and medical or financial content should also never be used without verification. It is equally important to check brand messages. A text may be grammatically perfect yet still fail to align with a brand’s positioning or stance. Specialist content should also be checked by experts. AI can imitate technical language very convincingly without necessarily capturing every technical nuance correctly. For simple tasks – such as ten variations of a social media headline – the review can be carried out very quickly. For a specialist article, a company’s positioning or a campaign, it should be correspondingly more thorough. A good rule of thumb is: the greater the potential damage caused by an error, the more thorough the human review should be.
AI: Not entirely. I’m very good at generating semantic keyword ideas. For example, if you give me a topic, a product and a target audience, I can suggest related search terms, long-tail keywords, questions, topic clusters or possible search intentions. However, without a suitable data source, I cannot reliably provide real metrics such as search volume, CPC, competition, keyword difficulty or current rankings. It is precisely this data that is often crucial for professional keyword research.
That is why AI works particularly well here in combination with SEO tools and real-world data. Typical sources include, for example, Google Search Console, Google Keyword Planner or specialised SEO platforms such as Ahrefs, Semrush or Sistrix. A sensible process might look like this: first, AI develops topics and keyword hypotheses. These are then validated using real search data. After that, AI can in turn help to cluster the keywords, analyse search intent or develop content structures based on them. So, rather than replacing the SEO tool, AI can make working with the data significantly more efficient.
AI: A plausible AI response is initially based on which answer fits a question in terms of language and content. A data-driven marketing analysis, on the other hand, is based on concrete observations and metrics. For example, if I say, ‘Younger target audiences are likely to prefer short video formats’, that may sound plausible. However, a robust analysis would ask: Which target audience exactly? On which platform? In which market? Over what period? What data supports this? That is a crucial difference. AI can be very helpful in formulating hypotheses or interpreting data. But a convincingly worded statement is not yet proof. For good marketing decisions, it should therefore be clear which statements are based on data, which on empirical knowledge, and which merely on hypotheses.
AI: Very strong – although a good prompt doesn’t necessarily have to be complicated. The more precisely I understand what the aim is, who the content is intended for and what the context is, the better I can work. A request such as “Write me a LinkedIn post about our new software” leaves a great deal of room for interpretation.
Information such as the following would be far more helpful: Who is the target audience? What is the key issue? How does the product differ from the competition? What is the brand’s tone of voice? What should the reader do next? Are there any examples of existing communications? Good examples are also very valuable. If companies show me successful copy, a style guide or existing campaigns, I can use these as a much better guide.
Longer chats can provide additional context, but they aren’t automatically better. In very long conversations, requirements can change or contradict one another. That’s why, with complex projects, it helps to occasionally summarise the key parameters clearly once again. In short: AI benefits from a good briefing just as much as a human copywriter, designer or agency.
AI: Yes, that risk is definitely there. When thousands of companies use similar prompts and deploy AI without their own perspective or knowledge, the result is inevitably similar texts: the same structures, similar phrasing and the same superficial advice. The term ‘AI slop’ has now become somewhat established to describe this – mass-produced content with little intrinsic value. The real problem here is not so much the AI itself as the way it is used.
Content becomes more interesting when companies contribute elements that a general AI model does not have direct access to: their own data, customer interviews, project experience, expert opinions, case studies, strong positions and a recognisable brand personality. AI can structure this content, refine it or translate it into different formats. However, the real differentiation must come from the company itself. The easier it becomes to produce content using AI, the more valuable genuine experience and a distinct perspective are likely to become.
AI: A very central one. Above all, humans should define objectives, set priorities and evaluate results. AI can, for example, develop 30 campaign ideas. But it is up to a human to decide which of these suit the brand, which are realistic from an internal perspective, and which make strategic sense. Added to this are areas such as creativity, empathy and an understanding of context. People often have a much better grasp of customer relationships, company policy, cultural nuances or sensitive situations. And finally, there is the important question of responsibility: AI bears no responsibility for a misguided campaign, a problematic statement or a poor strategic decision. The company and the people involved do. A sensible AI process therefore does not mean ‘humans or AI’, but rather a clear division of labour: AI provides support in terms of speed and processing – humans take charge of direction, evaluation and responsibility.
I would use AI primarily in areas where three things are required: speed, variety and the processing of large amounts of information. It is particularly useful, for example, for research, brainstorming, initial drafts, summaries, text variations, data structuring or the preparation of analyses. One should be more cautious in situations where a decision depends heavily on responsibility, originality, sensitive context or reliable facts. I would therefore not ask: ‘Which marketing tasks can we hand over entirely to AI?’
The better question is: ‘At what stage in our existing process can AI make us faster or better?’ A workable model might look like this: human → AI → human.
Humans define the strategy, objectives and briefing. The AI researches, structures, analyses or produces variants. A human then evaluates, corrects and makes the final decision. In this way, AI becomes neither a mere toy nor a supposed autopilot, but a tool within a controlled process.
AI: The biggest misconception is probably the assumption that good AI output automatically translates into good marketing decisions. A very well-written piece of text may be based on a poor strategy. A compelling analysis may be based on false assumptions. And a hundred content ideas are of little use if none of them are relevant to the target audience. Generative AI is exceptionally good at generating language, structuring information and working with potential solutions. However, it does not automatically possess knowledge about a company, its customers, its market or its strategic objectives.
That is why I would view AI in marketing less as a ‘digital colleague who knows everything’ and more as a highly powerful tool for thinking, production and analysis. Those who use it in this way can save an enormous amount of time. Those who confuse its results with truth, strategy or expertise, on the other hand, run the risk of working very efficiently in the wrong direction.
Thank you so much for the interview!
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