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In day-to-day project work, communication functions surprisingly well. Not because we\u2019re always particularly precise, but because we\u2019re there to support one another.<\/p>\n
Anyone who\u2019s worked in consulting long enough knows this: clients and team members rarely say exactly what they mean. Statements are contextualized, gaps are silently filled, and ambiguities are invisibly corrected. Our brain is a high-performance pattern-recognition machine. Precision takes energy. Filling in the blanks is faster. So we fill in the gaps. Automatically. Mostly unconsciously.<\/p>\n
In a typical status meeting, a sentence like this comes up at the end: \u201cIt\u2019s important to us that the result is technically sound and makes strategic sense.\u201d No one bats an eye. No one asks for clarification. The project manager thinks about risks, the business analyst about technical completeness, and the Scrum Master about deliverability. Everyone has a picture in mind\u2014just not necessarily the same one. And yet things go surprisingly well for a long time, because somewhere along the way, someone quietly corrects the course.<\/p>\n
This invisible safety net of interpretation, experience, and social goodwill isn\u2019t a weakness in the organization. It\u2019s often the organization itself.<\/p>\n
Then the machine takes over. And with it, a new form of honesty.
AI doesn\u2019t fill in the gaps. It doesn\u2019t ask for clarification when something is ambiguous; instead, it decides\u2014silently, consistently, without a guilty conscience\u2014on a single interpretation. What previously looked like confident brevity suddenly seems surprisingly shallow. The sentence has remained the same. Only the web beneath it has disappeared.<\/p>\n
The obvious reaction is: \u201cThe machine doesn\u2019t understand that.\u201d That\u2019s true. But it doesn\u2019t go far enough.<\/p>\n
Because the real break occurs earlier\u2014even before anything is formulated at all. Prompting poses a question that, in everyday project work, surprisingly often remains unanswered: What do I actually want? Not vaguely, not implicitly, not in the sense of \u201cit\u2019ll work itself out\u201d\u2014but concretely enough to be able to delegate it.<\/p>\n
In consulting, this kind of clarity is sometimes mistaken for being overly detailed. Anyone who asks too precisely is quickly labeled as difficult. Yet precise communication merely shifts the work: forward, to the sender, rather than backward, to the team. The silent extra work doesn\u2019t disappear as a result. It becomes visible. And thus, for the first time, negotiable.<\/p>\n
Communication can be viewed, in simple terms, as a chain of probabilities. Let\u2019s assume: In seven out of ten cases, I clearly articulate what I mean. The message is conveyed accurately in eight out of ten cases. My counterpart understands it the way I intended in six out of ten cases.<\/p>\n
This results in:
70% \u00d7 80% \u00d7 60% = 33.6%.<\/p>\n
In roughly one out of every three cases, exactly what was intended comes across. The rest is handled by interpretation, experience, and context. Or to put it another way: work that someone does without having been explicitly asked to do so.<\/p>\n
Communication science says: The truth lies with the recipient. It\u2019s not what the sender intended to say that counts, but what reaches the other person\u2019s mind\u2014filtered through experience, expectations, emotion, and context. A misunderstanding doesn\u2019t require catastrophic communication. All it takes are three reasonably human moments.<\/p>\n
The internet catches the rest. Until it no longer does.<\/p>\n
This is where the real transfer lies. It\u2019s not just AI that improves when we provide more precise prompts. We do, too.<\/p>\n
Anyone who seriously begins to formulate prompts inevitably develops a habit that is taken for granted in consulting but is often neglected in practice: thinking before speaking. Not structuring your thoughts after speaking, not clarifying on request\u2014but clarifying your own intent before a task leaves the room.<\/p>\n
That sounds trivial. It isn\u2019t. <\/p>\n
Imagine you\u2019re delegating a task to a new colleague. First day, no shared context, no unspoken understanding. What would you tell them? You\u2019d state the goal, not just the steps. You\u2019d give them what they need to know\u2014not everything you know. And you\u2019d explain how you\u2019ll know when it\u2019s been done well.<\/p>\n
These three steps aren\u2019t just AI techniques. They\u2019re communication best practices\u2014and they work just as well in a briefing, when delegating a task, or during a client meeting.<\/p>\n
The only difference is: When talking to people, you can get by without them. The safety net catches you. But not when talking to a machine. And that\u2019s exactly why it\u2019s such an effective training ground.<\/p>\n
The machine isn\u2019t the problem. It\u2019s the first counterpart in everyday project work that doesn\u2019t provide an interpersonal safety net\u2014and thus shows us just how much weight others have been carrying all along.<\/p>\n
In almost every team, there are people who have been working between the lines for years. They catch mistakes, fill in the gaps, and quietly correct things. Yet they rarely appear in project status updates and are never praised in retrospectives.<\/p>\n
What is described here is not theory. It is project practice in action in some places. In our team at Germany\u2019s largest insurance company, we\u2019ve started treating prompting as a tool for better project communication\u2014not just for better AI results. By consistently using prompting in our projects, we\u2019re embedding principles of clear task delegation, precise goal formulation, and explicit expectation-setting. We also use our work with AI tools as a mirror and a training ground for this.<\/p>\n
No magic. No paradigm shift. Just the consistent further exploration of a question the machine posed to us: What exactly do you mean?<\/p>\n
The next level of communication in consulting doesn\u2019t start with better tools. It begins with the admission that a language model without a nervous system is currently giving us some of the clearest feedback we\u2019ve received in years: Unclear communication creates more work. It\u2019s just that someone else often ends up doing it.<\/p>\n
By the way, the exclamation points were mine.<\/p>\n
– – – – –<\/p>\n
\ud83d\udc49 www.adesso.de<\/a> Photo: unsplash<\/i><\/p>\n","protected":false},"excerpt":{"rendered":" June 17, 2026. Prompting as a model for the next level of communication in consulting. I recently had an argument with an AI agent. A real argument. With exclamation points, capital letters, and a sharpness of tone that I would never have allowed myself to use with a human being. The prompt hadn\u2019t delivered the result I\u2019d expected\u2014even though it had been optimized beforehand by that very same AI. What followed was a low point in my communication skills, which I mention here not without a touch of self-deprecating humor. The machine, as we know, has no nervous system\u2014it didn\u2019t flinch. But that very moment taught me more about communication than many a seminar in recent years. And this is the story of it.<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[4764],"tags":[4759,11381,2043,250],"class_list":["post-541505","post","type-post","status-publish","format-standard","hentry","category-software-en","tag-artificial-intelligence-ai","tag-automation","tag-digitalization","tag-it"],"acf":[],"yoast_head":"\n
\ud83d\udc49 GenAI Impact Report 2026<\/a> <\/p>\n