68% of employees at Russian companies either do not understand how AI tools work or regularly get incorrect results from them. That is the conclusion reached by researchers at the corporate learning platform Teachbase. Notably, the figure is high among Gen Z too — 54% — even though they are the most frequent users of AI: one in four uses it daily.
So the habit of using AI and the ability to work with it are two different things. And it is precisely this distinction that is becoming more and more important for the labour market. According to World Economic Forum estimates, by 2030 around 39% of key skills will change, and 59% of workers will require retraining. At the same time, some 92 million jobs could disappear, while around 170 million new ones could be created.
Dmitry Breitenbikher, Deputy Chairman of the Management Board of VTB, considers which skills genuinely gain value in this environment in an article for the latest issue of BRICS Business Magazine.
His central argument: professional value is less and less determined by the sheer volume of accumulated knowledge, and more and more by how a person thinks, how they work with AI, and how quickly they are able to reinvent themselves:
The fastest-growing skills are increasingly related to AI and data. But it is particularly important to avoid a common misconception: AI literacy cannot be reduced to knowing how to formulate the right prompt for a chatbot. True AI literacy means understanding both the capabilities and limitations of a model: what it does well, where it can make mistakes, how to verify its output, what data can safely be shared with it, and what risks its use may create.
Breitenbikher identifies several groups of skills. Among the technical ones are analytical and systems thinking, and working with data and AI. Among the human ones are leadership, communication and the ability to navigate change. He singles out meta-skills separately: the ability to learn, creativity and interdisciplinary thinking.
The ability to critically check what the machine proposes becomes especially important. AI can quickly find information, write a text or carry out an analysis, but responsibility for framing the task, assessing the result and making the final decision remains with the human being.
Another key skill is the capacity to continually renew one’s own experience:
If almost half of the skills people need are changing within a few years, it is impossible to build a career solely on accumulated knowledge. People must not only continuously acquire new skills but also let go of what worked until recently. This is a crucial point. The ability to learn is not only the ability to acquire something new. It is also the ability to unlearn. Experience can be an advantage, but it can also become a trap. The longer a person works according to a particular model, the greater the risk of confusing what is familiar with what is right. Professional resilience today is therefore determined not by the volume of accumulated experience, but by the speed at which that experience can be renewed.
In other words, in the age of AI the winner is not the person who knows the most, but the one who updates their knowledge fastest, knows how to doubt a ready-made answer, and uses technology as a tool rather than as a substitute for their own thinking.
