The Network We Can’t See
In day-to-day project work, communication functions surprisingly well. Not because we’re always particularly precise, but because we’re there to support one another.
Anyone who’s 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.
In a typical status meeting, a sentence like this comes up at the end: “It’s important to us that the result is technically sound and makes strategic sense.” 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—just 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.
This invisible safety net of interpretation, experience, and social goodwill isn’t a weakness in the organization. It’s often the organization itself.
What happens when the net disappears
Then the machine takes over. And with it, a new form of honesty.
AI doesn’t fill in the gaps. It doesn’t ask for clarification when something is ambiguous; instead, it decides—silently, consistently, without a guilty conscience—on 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.
The obvious reaction is: “The machine doesn’t understand that.” That’s true. But it doesn’t go far enough.
Because the real break occurs earlier—even 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 “it’ll work itself out”—but concretely enough to be able to delegate it.
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’t disappear as a result. It becomes visible. And thus, for the first time, negotiable.
A calculation that should cause some unease
Communication can be viewed, in simple terms, as a chain of probabilities. Let’s 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.
This results in:
70% × 80% × 60% = 33.6%.
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.
Communication science says: The truth lies with the recipient. It’s not what the sender intended to say that counts, but what reaches the other person’s mind—filtered through experience, expectations, emotion, and context. A misunderstanding doesn’t require catastrophic communication. All it takes are three reasonably human moments.
The internet catches the rest. Until it no longer does.
What machines could teach us—if we listen
This is where the real transfer lies. It’s not just AI that improves when we provide more precise prompts. We do, too.
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—but clarifying your own intent before a task leaves the room.
That sounds trivial. It isn’t.
Imagine you’re delegating a task to a new colleague. First day, no shared context, no unspoken understanding. What would you tell them? You’d state the goal, not just the steps. You’d give them what they need to know—not everything you know. And you’d explain how you’ll know when it’s been done well.
These three steps aren’t just AI techniques. They’re communication best practices—and they work just as well in a briefing, when delegating a task, or during a client meeting.
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’s exactly why it’s such an effective training ground.
The machine isn’t the problem. It’s the first counterpart in everyday project work that doesn’t provide an interpersonal safety net—and thus shows us just how much weight others have been carrying all along.
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.
What is described here is not theory. It is project practice in action in some places. In our team at Germany’s largest insurance company, we’ve started treating prompting as a tool for better project communication—not just for better AI results. By consistently using prompting in our projects, we’re 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.
No magic. No paradigm shift. Just the consistent further exploration of a question the machine posed to us: What exactly do you mean?
The next level of communication in consulting doesn’t 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’ve received in years: Unclear communication creates more work. It’s just that someone else often ends up doing it.
By the way, the exclamation points were mine.
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Further Reading
👉 www.adesso.de
👉 GenAI Impact Report 2026
Photo: unsplash
