So, what are these AI skills?

The formula for successful careers in AI times remains unchanged: talent + hard (study) work + expertise + a bit of luck, writes UNSW Business School's Frederik Anseel

I think most predictions about the skills of the future are wrong. At best, they’re meaningless. Yet it’s a crucial question: how do we prepare young people for careers that will be strongly influenced by AI? As Dean of a Business School, this keeps me up at night. And I’m not alone. Concerned business leaders ask me the same question. AI isn’t on the agenda, AI is the agenda. But despite its enthusiastic adoption by many individuals for their day-to-day work, it’s too early to make predictions.

Organisations like the World Economic Forum create annual lists of future skills. Every year, I find these lists completely useless. The most recent report mentions that skills in generative AI – the type of AI that produces new things, like texts or images – are becoming more important and in demand. Yes, I could have told you that too.

But what are those AI skills then? Certainly not being good at using (or ‘prompting’) ChatGPT, Claude, or Grok. These are early apps, the BASIC or Yahoo of their generation, which will very quickly be overtaken by better models. Surely AI skills must be something broader and more general. Often, as a response to such challenges, this list follows: critical thinking, problem-solving, creativity, and social skills.

Sure. Again, of course, these are important qualities. However, they are so vague and so fundamentally human that they are timeless and, therefore, completely useless. You might as well say: ‘In the future, we’ll need smart people.’ That’s also true, but it doesn’t help you one bit. Despite all the fancy ads from universities, we don’t have learning systems that specifically teach such general human skills.

The need for subject knowledge

Let’s take the example of ‘critical thinking.’ Let’s be very clear: There is no separate muscle for critical thinking in the brain. How can you arm yourself with critical skills against misinformation, such as the claim that trade tariffs will help the economy? Is it even possible?

Yes, it is possible – through expertise and knowledge acquisition. Only those with thorough knowledge of fundamental economic principles and the history of economic trade can debunk nonsense about trade tariffs.

The lesson is that we don’t necessarily need to teach our pupils and students how to critically engage with AI. You don’t need to tell them ‘be careful, this information might not be trustworthy.’ They will see that for themselves if they have fundamental subject knowledge and expertise.

The future of education will therefore look surprisingly like its past. Deep knowledge and fundamental cognitive skills to process and analyse knowledge will still provide the foundation for future career success.

The need for vision

Are there no specific AI skills then? Yes, perhaps managing an army of AI agents will require specific coordination and management skills. But anyone who is closely watching the evolution of recent apps like Claude Code, Manus and OpenAI Deep Research realises that only those who already have much expertise in a domain will significantly benefit from these. Also, those who want to specialise in AI development and machine learning undeniably need strong quantitative skills, which means mathematics in human language.

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And what companies need today, above all, are people with a vision of how AI can change their industry. That requires experience and, again, expertise. In the 1970s, Japan became successful, not necessarily because people there invented new technology themselves, but because they adopted technology to innovate production processes. The need today is not to make each individual a few percent more efficient in their individual jobs with ChatGPT, but we need innovation to spark new business models and to find new ways of organising and producing smarter.

On my nightstand lies Source Code, Bill Gates’ autobiography. The founder of Microsoft predicted in 1973 that software development would change the world. That didn’t come out of nowhere. He was an exceptional mathematical talent with an unhealthy work ethic and he was able to develop expertise at a very young age because his school had one of the first computers. Talent + hard (study) work + expertise + a bit of luck is also the formula for successful careers in AI times.

Frederik Anseel is a Professor of Management and Dean of UNSW Business School. He studies how people and organisations learn and adapt to change, and his research has been published in leading journals such as Journal of Applied Psychology, Journal of Management, American Psychologist, and Psychological Science. A version of this post was first published in De Tijd.