AI - not a panacea for data chaos

When the buzzword „AI“ is used today, the same thing often happens as with „digitalization“ in the past: suddenly a tedious, complex task becomes an innovation project with future potential. „Phew, tricky problem - why don't you put AI on it?“ is then said, as if AI were the panacea for data chaos. The reality is often different. AI is like a mirror - and what we give it comes back in a surprisingly similar form.

Myth 1: „The more data we put into AI, the better.“ Sounds logical - but is often wrong. Data is only useful if it is accessible, comprehensible, well-structured and up-to-date. In practice, however, information is often stored independently and isolated in data silos, spread across departments, systems and formats. If AI can access it, it often ends up there: garbage. Shit in, shit out, as the saying goes. What many forget: Anyone talking about good AI must also talk about data maintenance, process clarity and governance.

Without clearly defined responsibilities, transparent data flows and proper structures, even the best algorithm is of little help. AI does not work despite bad data - it only works because of good data. This means that data management is crucial for the profitable use of AI. Studies show that only a third of companies manage their data efficiently. The average global data maturity level is 2.6 on a 5-point scale. There is a lot of room for improvement. Only when the exponentially growing volume of data meets certain criteria will AI be able to analyze it, recognize patterns, make predictions and ultimately form a reliable basis for decision-making.

Myth 2: „AI wants to take over the world!“ This myth persists. Probably because it sounds more exciting than what AI really is. AI is not a thinking robot or a sentient being. Algorithms can do a lot, but it has nothing to do with real intelligence. AI wants nothing. It understands nothing. And it doesn't act of its own free will either - but exclusively according to patterns with which it has been trained. Admittedly: It is getting better and better at this. Recently, a new language model was presented that simulates human behavior with astonishing precision. But even for this ability, it first had to be trained with more than ten million decisions from psychological experiments.

This shows that AI will change professions, make jobs obsolete and create new ones. But it will not replace people - it will change working methods. It is not a humanoid colleague, but a tool. Admittedly, it is a very powerful tool. And therein lies the rub: we like to talk about potential, but rarely about responsibility. Who is responsible when AI makes mistakes? Can machines take responsibility at all? These questions have yet to be answered.

Myth 3: „AI is neutral and doesn't make mistakes.“ It would be nice. AI is only as neutral as the data it has been trained with - and this usually corresponds to stereotypes. Anyone who believes that AI is free of bias should take a look at automated credit checks or application procedures. Spoiler: Discrimination does not take place despite AI, but because of it. Bias in, bias out. Without critical examination in all phases of the AI life cycle, human control and good training data, AI simply reproduces what it knows - and sometimes this not only leads to unequal treatment, but even to unwanted interactions with cyber security.

ConclusionAI is not a panacea, but it is a tool. AI can do a lot. But not everything. And especially not by itself. If you want to use it, you have to take care of things that are often overlooked: Data quality. Responsibilities. Processes. Training effort. Governance. Sometimes it seems as if AI has become a convenient excuse to avoid the actual work. But if you avoid creating good structures, you won't end up with smart AI - just an expensive system that makes the same mistakes as before. Only faster. So maybe we should put less AI on it - and think more.