AI Is Not a Panacea for Data Chaos

Today, when the buzzword "AI" is used, the same thing often happens as with "digitalization" in the past: a tedious, complex task suddenly becomes an innovative project with potential for the future. "Phew, tricky problem—why don't you use AI for it?" is said, as if AI were a cure-all for data chaos. However, reality is often different. AI is like a mirror—what we give it comes back in a surprisingly similar form.

Myth 1: "The more data we put into AI, the better." This sounds logical, but is often incorrect. 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—in the trash. As the saying goes, "garbage in, garbage out." What many forget is that 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 one-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. AI will only be able to analyze the exponentially growing volume of data, recognize patterns, make predictions, and ultimately form a reliable basis for decision-making when it meets certain criteria.

Myth 2: "AI wants to take over the world!" This myth persists. This is probably because it sounds more exciting than AI really is. AI is neither a thinking robot nor a sentient being. Algorithms can accomplish many things, but that has nothing to do with real intelligence. AI wants nothing. It doesn't understand anything. It doesn't act on its own free will either, but rather according to the patterns with which it has been trained. Admittedly, it's getting better and better at this. Recently, a new language model was presented that simulates human behavior with astonishing precision. However, even this ability required training with more than ten million decisions from psychological experiments.

This demonstrates that AI will transform professions, rendering some jobs obsolete while creating new ones. However, it will not replace people; rather, it will change working methods. It is not a humanoid colleague but a tool. Granted, it is a very powerful tool. The problem is that 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. However, AI is only as neutral as the data it has been trained with, which usually corresponds to stereotypes. Anyone who believes AI is unbiased should look into automated credit checks or application procedures. Spoiler—discrimination does not happen despite AI; it happens because of it. Bias in, bias out. Without critical examination in all phases of the AI life cycle and good training data, AI simply reproduces what it knows. This can lead not only to unequal treatment but also to unwanted cyber security interactions.

Conclusion:  AI is not a panacea, but rather a tool. AI can do a great deal, but not everything, and especially not by itself. If you want to use it, you must take care of things that are often overlooked, such as: data quality, responsibilities, processes, training effort, and governance. Sometimes it seems as if AI has become a convenient excuse to avoid doing the actual work. However, if you neglect to create effective structures, you won't end up with intelligent AI—just an expensive system that perpetuates past errors, just on a faster scale. So maybe we should put less emphasis on AI and more emphasis on thinking.