By the way: How LinkedIn talks about AI - and what gets lost in the process

You come across them almost every day on LinkedIn: people who explain how artificial intelligence is changing the way we work, what ingenious new models are available today and, last but not least, how much money you can already earn with AI - with their AI, of course. These self-proclaimed AI superheroes post tools, show prompts and summarize what others have already said. The posts are clearly designed, well-structured and visually appealing. But - they are often surprisingly similar if you take the trouble to read a little.

Much of this content looks professional - but hardly anyone asks: Where does the knowledge actually come from? And further: What of it was thought up in-house - and what was simply "taken over", so as not to use the word "copied"? And probably the most important question is, who benefits from this?

Knowledge in series

A large proportion of these contributions follow a simple principle:

• Start with a promise ("How to use AI better")
• a list of recommendations
• a tool or a prompt
• A short sentence to take away

It seems to work, probably because it is familiar and easy to read.

But it almost always remains on the surface or is not comprehensible. Because - what is described here as "knowledge" is repetition. What sounds like experience is often appropriation.

When AI writes about AI - does that work?

Many of the texts about AI are now created with AI. That's not a problem - as long as you disclose what you yourself have contributed. But that rarely happens - in fact, almost never ... instead, the impression of clarity, depth and knowledge is conveyed.

However, if you take a closer look, you usually find little that would provide orientation: What does what has been written or published mean for me - for my everyday life? What is really useful for clarification and gaining knowledge?

What is missing today ...

In a time full of AI content, it would be helpful if articles not only showed what is possible, but also what cannot be answered straight away or not at all. When not only tools are presented, but also the thoughts and considerations behind them.

It would also be respectful towards other people if the uncertainty associated with innovation is not hidden, but made visible as part of the debate.

As for ourselves ...

This text was created with the help of AI. And - I also use structure, language and condensation to convey a thought. But I strive to remain authentic - with the process, with the source, with myself.

I, too, have to keep "looking at my own nose" and critically scrutinize the questions or critical comments that I ask others - what is really meant - and what is just "well packaged" retelling?

By the way ...

Perhaps this is precisely the difference: not whether AI is or was involved - but whether you still check yourself to see how you arrive at what you post.

Laura Colledani and Klaus Ammon have more than 40 years of combined experience in consumer research and marketing research. Over the years, they have developed a keen sense of what moves people - even beyond the numbers. Both authors advocate a communication culture that listens before it broadcasts. With the new approach of consumer listening management tools, they want to support marketing managers in not only questioning consumers in the traditional way, but also entering into a genuine relationship with them.