Things are different with the activities of Cloud Engineers which are classified as moderately affected by AI in the ranking. In order to be able to make the most accurate statement possible, you would of course have to break down the entire area of responsibility individually. What can be said, however, is that cloud engineering as a whole will also be noticeably changed by AI – but not to the same extent as the jobs that are at the top of the ranking.
The job role of the Security Engineer attested. His work also includes sub-steps that can easily be handled via LLMs, such as creating security documentation or developing policies. It is still up to the expert to decide whether these guidelines meet the regulatory requirements and whether the corresponding risk is acceptable for the business.
Developers remain in demand – especially because of AI
In order to seriously evaluate the value creation of IT jobs under the influence of AI, looking at job profiles alone is not enough. First, each position must be broken down into its specific individual activities. One person who knows this particularly well is Sebastian Unterreitmeier-Lessig. The expert from strategy consultancy Mercer works with his customers to examine what activities individual IT roles consist of and which of them can be automated or supported by AI in the future.
“The decisive factor here is the data situation. In other words, whether it is possible to connect an AI to the respective job in such a way that all relevant data is available to it in a processable form in order to take over parts of the work smoothly and thus make a value-adding contribution,” explains Unterreitmeier-Lessig. The influence is currently particularly evident in software development. Programming languages are highly structured and many tasks are clearly modularized. In some cases there is even a seemingly “Tayloristic” division of labor. Such work steps could easily be taken over by AI, says Unterreitmeier-Lessig.
But implementation is rarely as simple as it sounds in theory. In manageable environments, AI solutions can often be tested comparatively quickly. In large organizations, on the other hand, they encounter mature IT landscapes, complex dependencies and numerous interfaces. A pure activity analysis is therefore not enough, as the consultant suggests: “I have to look at the processes, the interaction of the different levels and the interfaces. This is about reducing complexity and making these interfaces AI-ready.”
In addition to the technical and organizational analysis, the human dimension also comes to the fore. IT specialists and software developers in particular usually identify very strongly with their specific work – they are primarily concerned with the meaning of their work. When AI intervenes in this, it not only changes processes, but also the way we see ourselves, as Unterreitmeier-Lessig knows: “People really enjoy doing what they do. And they don’t want it to simply be taken over by AI.”
