Why AI is not a “Magic Sauce” – The Reality Behind the Hype
Focusing on boring groundwork can bring more success than chasing the latest trend.
Advances in digital technologies continue to spawn trends that are touted as solutions to solve many problems in one fell swoop. The latest example is artificial intelligence, which promises to finally shift organizations up a gear in the implementation of their digital transformation. But especially with deep digital maturity, it would be better to take care of the basics instead of putting resources into “magic” solutions like artificial intelligence.
Index
Meeting the increasing pressure to innovate
In addition to the many opportunities that the digital transformation offers companies, it also poses a major challenge. Many companies, especially SMEs, are facing great pressure in the digital economy to innovate and digitize internal processes and rethink business models. Startups are entering markets they thought were safe, tech companies are undermining established business models, and new customer demands are increasing the demands on their own IT and digital presence. At the same time, expensive and sometimes unsuccessful IT projects, hacker attacks, and data breaches remind you that digital maturity is usually low.
With limited resources, rapid technological change and great uncertainty, you quickly find yourself in a difficult starting position. Where do I start? How can I get as far as possible with the digital transformation as quickly as possible?
The promise of “magic sauce”
This is where the “magic sauce” comes into play, the silver bullet, the one solution that solves my problems in one fell swoop. The promise of technologies marketed accordingly is: all you have to do is bet on me and I will solve your digital problems. According to the idea of “leapfrogging,” long phases of hard work can simply be skipped and I can get to the top of the maturity curve in one fell swoop. This is, of course, a very attractive promise, especially when one is plagued by the fear of being left behind. No matter what state an organization is in, no matter how analog processes are set up, you just pour a little magic sauce on it, et voilà: you’re already one of the digital champions.
Currently, artificial intelligence is presented as such a magic solution, from the generative AI variant like the Large Language Models à la ChatGPT to other products based on machine learning. The systems promise efficiency and insight gains, are supposed to generate new insights in the unmanageable data warehouses of companies and optimize internal processes as well as relieve employees in their work.
He who does not honor the penny…
What is often overlooked when such magical solutions are touted is that certain basic requirements must be met in order for them to take effect. Just as a sports car is only useful if you have a driver’s license and enough fuel, the most fascinating technology won’t help you if you can’t use it.
Quite apart from the fact that even in organizations with high digital maturity, a successful AI project is not a foregone conclusion, as is often the case in magic, deception plays a big role here: you may have made progress at first glance even with successful AI projects, but a closer look often reveals the sad truth: namely, that you missed working on the root causes of the original deep innovation or efficiency and it will catch up with you. Decades of transformation failures cannot simply be made up for with a bought-in AI solution – or other magic.
Example from administrative digitization
In the public sector in particular, it is always necessary to link and match different data. However, there are major problems due to data silos, lack of interoperability, unclear standards and other construction sites. For example, linking tax data and account information in Germany proved to be an almost insurmountable challenge (German Article in “Der Standard“). Artificial intelligence cannot add any value here because the necessary foundations, e.g. access to data, simply do not exist. Nevertheless, the temptation is likely to be great in the public sector as well, in the hope of rapid progress, to prefer to rely on the magical promise of artificial intelligence and other trends rather than deal with the tedious fundamentals of digital transformation.
Example from the private sector
A company is under pressure because, on the one hand, it has received digitally fit competition in its market, and on the other hand, customer surveys show that it is not considered very digitally savvy. In addition, customer satisfaction in customer service in particular is low. In the hope of killing several birds with one stone, the management is betting on a chatbot solution. From a marketing point of view, the idea seems to work out, and the company is one of the first in the industry to rely on a chatbot à la ChatGPT. However, the historically complex support processes still exist and even if customers initially find it interesting to chat with an AI, they have contacted the company to have their customer query taken care of, which then continues to fail due to the lack of transformation.
Conclusion
So what follows from these considerations and examples? Certainly not that leapfrogging is never an option, nor that one should not experiment with new technologies. But it hopefully follows that some skepticism is in order when one is promised a too-convenient way out of deep digital maturity. As in other areas, the following also applies to digital transformation: he who does not honor the penny is not worth the talent. In other words, don’t be dazzled by the latest fad, especially if you haven’t done your homework for a long time. Instead, it is important to focus clearly on the basic requirements for successful digital transformation: Data strategy, cybersecurity, clarity in processes and outsourcing, etc. On this basis, your own organization will be empowered to benefit effectively from new trends quickly instead of wasting money and nerves on a disappointing magic show in the end.
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