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Despite rapid advances, robotics still struggles in unstructured environments (Roel Dierckens/Unsplash)
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What to ask when choosing a career in the age of AIWhat to ask when choosing a career in the age of AI
Students around the world are making decisions about their future while trying to understand how artificial intelligence will shape the careers they are stepping into. They are not alone. Parents are anxious about whether university degrees will still hold value, early career workers are uncertain about how their roles may evolve, and many mid-career professionals are wondering whether they should retrain or change direction altogether. Across countries and generations, people are confronting career choices at a moment when work is changing faster than at any time in recent memory.
These individual decisions, taken at scale, are also shaping national skills pipelines, economic resilience, and how societies adapt to technological change.
This pace of change raises a deceptively simple question: how do you choose a future when almost every profession is being reshaped by intelligent systems?
Predicting which jobs will disappear has proven unreliable. A more useful approach is to ask a set of practical questions that help people understand how exposed a career may be to automation, how it is likely to evolve, and where resilience is more likely to be found.
One starting point is to consider how much of a role relies on routine tasks. Artificial intelligence is particularly effective at predictable work, whether that involves processing data, recognising patterns, or following standardised procedures. Occupations dominated by repeatable tasks are often the first to change, as automation can perform these functions faster and at scale. Roles that involve variation, judgement, and adaptation tend to be more resistant.
That leads to a second consideration: the extent to which a profession depends on human judgement, negotiation, and trust. Work that centres on relationships, context, and emotional intelligence remains difficult for machines to replicate. Teaching, counselling, leadership, diplomacy and stakeholder engagement all rely on social understanding and human presence. As automation expands, these qualities are becoming more valuable rather than less.
Work that centres on relationships, context, and emotional intelligence remains difficult for machines to replicate (Homa Appliances/Unsplash)
It is also important to look at how industries are investing in AI. In some sectors, intelligent systems are being introduced primarily to reduce labour costs. In others, they are being deployed as support tools that extend human capability. Health care, engineering, and many creative fields increasingly use AI to assist decision making, analysis, or design rather than to replace professionals outright. Understanding whether a field is pursuing automation or augmentation can offer important clues about its future shape.
Closely related is the question of skills. Most jobs are not disappearing; they are changing. Data literacy, systems thinking, and the ability to work effectively with AI tools are becoming baseline expectations across a wide range of professions. For students choosing degrees, or for workers considering retraining, it is worth examining whether education pathways explicitly teach these capabilities or assume they will be picked up informally.
The aspects of work that generate value are hard to replicate with machines – creativity, ethical reasoning, cultural understanding, leadership, and strategic decision making.
When education systems fail to adapt to these shifts, the result is not just individual mismatch but broader labour shortages, productivity constraints, and uneven economic adjustment.
Physical presence and situational awareness also continue to matter. Despite rapid advances, robotics still struggles in unstructured environments. Trades, field-based roles, and hands-on technical work remain difficult to automate fully. These occupations are evolving as technology is introduced, but they are not being replaced at the same pace as purely digital roles.
Regulation is another often overlooked factor. Professions with strong safety, ethical, or accreditation requirements tend to adopt new technologies more cautiously. Medicine, aviation, law, and engineering operate under regulatory regimes where mistakes carry serious consequences. The military has adapted with drone technology and digital systems, yet these advanced weapons continue to rely on human action and judgement to function. This acts as a buffer against rapid automation and slows the pace of change, even as AI is introduced.
At the heart of all these considerations is the question of value creation. The aspects of work that generate value but are hard to replicate with machines – creativity, ethical reasoning, cultural understanding, leadership, and strategic decision making – remain distinctly human. These capabilities increasingly differentiate people from intelligent systems and define the most resilient career paths.
Finally, there is the question of preparation. The strongest education programs and workplaces are already integrating AI into learning and practice. Rather than teaching people to compete against machines, they focus on how to work with intelligent systems, how to question their outputs, and how to use them responsibly. Those who understand both the power and the limits of AI will be better equipped for workplaces that are evolving rather than disappearing.
Career decisions will always involve uncertainty, particularly in times of rapid change. Asking the right questions does not eliminate risk, but it does provide a clearer way to think about the future. Artificial intelligence will shape the world of work, but with thoughtful guidance from education systems, employers, and policymakers, people – and societies – can still shape their place within it.
About the author
Niusha Shafiabady
Professor Niusha Shafiabady is an internationally recognised expert in the field of Computational Intelligence with many years of professional experience in both academia and industry.
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