I’m in conversation with many companies and professions. And when a conversation turns to someone’s employer, I like to ask two simple questions. “What kind of company is it?” “What does it stand for?” Sometimes, if the group is up for it, a third: “If this company were a car brand, which one would it be?”
It gets interesting when you know several people from the same company. Does it all sound similar? Or do you end up with a Mercedes, a budget Dacia and a Rolls-Royce, from the same company, to the same question? Because internally, a brand is exactly that: the answer everyone gives in roughly the same way.
No drama. In many organizations this is perfectly normal. But now AI enters the picture. And it adds a twist hardly anyone has on their radar.
Many post a lot. Nobody says in one sentence what they stand for.
AI tends to make this problem worse rather than solve it. From what I see, AI use almost always starts at the operational end: texts, posts, images, videos. Fast. Visible. In bulk.
And that’s exactly the catch. If the underlying narrative hasn’t been internalized, AI mainly scales one thing: inconsistency.
So I don’t see the strongest lever at the end. I see it a few levels earlier. In strategic communication itself. Two applications I think are underrated.
First: making patterns visible
In many organizations, communication is a system that has grown over time. Website, press releases, job ads, LinkedIn, brochures, talks. All public, all built up over years, by many hands. Plenty of material. But the actual logic underneath (what do we stand for, where do we draw lines?) is rarely spelled out. Rarely spelled out, never tested.
This is where AI can make patterns visible. Which justifications keep coming up? Which tensions repeat? Growth versus risk. Speed versus quality. And the one hardly anyone names: efficiency versus responsibility. Which values dominate, and which topics get avoided every single time?
The result isn’t a strategy. It’s a hypothesis about an organization’s communicative reality. It only becomes fit for strategy through human judgment. Through samples and cross-checks. Through professional expertise. And through comparing it with what actually happens out there.
Second: bringing the reality check forward
Strategic communication work rarely fails at analyzing the situation. It fails at imagination. Writing a strategy paper is the easy part. Picturing what that paper will do once it’s put into practice, across channels, audiences and months, is hard. I don’t exempt myself.
Here AI helps you quickly play through what could come out at the end. Wording, examples, variations. The misunderstandings, too. You see how a decision feels before it’s rolled out. That beats most strategy papers.
The blueprint comes from the machine. The direction from people.
AI can deliver the blueprint, as a hypothesis. People draw the boundaries. And they make the decision. That moves the strongest use of AI to the beginning. To where no word has been written yet.
Which leaves the one question no machine answers: when is a strategy clear enough to withstand communication at scale?