For decades, junior tech roles have been where people learn the basics of a profession. But what happens when AI starts doing much of that work?
Tasks that once took hours can now be completed with incredible speed, from generating code and analysing data to producing content and handling routine processes.
For businesses, that jump in productivity has many obvious benefits. For people starting their tech careers, there is a glaring question:
If AI takes on more and more of the routine work traditionally given to junior employees, how do they develop the experience needed to become tomorrow’s experts?
The debate around AI and employment often focuses on whether technology will replace jobs. A more immediate issue seems to be how it changes the way people learn to do those jobs.
Early-career roles have traditionally provided an opportunity to build technical knowledge through practice.
People learn by tackling relatively straightforward problems, making mistakes, working with colleagues and gradually taking on greater responsibility.
As AI increasingly handles that routine work, the development of those vital capabilities cannot be left to chance.
Someone using AI to generate code, interpret data or produce outputs still needs a thorough understanding of the underlying discipline to recognise when something is wrong, question an unexpected result and decide what should happen next.
In a technology-driven workplace, durable skills are a core part of being technically proficient.
Critical thinking, communication, problem-solving, collaboration and adaptability were once called ‘soft skills’, which understates their value.
Consider an AI-generated response to a complex technological problem. The ability to produce the answer is becoming less distinctive.
The invaluable skill now lies in the ability to assess the quality of the answer, understand its implications, explain it to others and make a sound, informed business decision.
The shift in the importance of durable skills as AI dominates the workplace means there are implications for how the next generation of technology professionals is educated and trained.
Higher technical education providers know that curricula must keep pace with innovations like AI.
For example, computing students need opportunities to work with emerging technologies and real-world data, while developing an understanding of how those technologies are applied in professional environments.
At the same time, education needs to create opportunities for people to practise the skills that technology cannot automate.
Project-based work, employer engagement, presentations, teamwork and problem-solving all provide opportunities to develop confidence and professional judgement alongside technical competence.
Assessment will also need to evolve in time. If AI can produce a convincing final answer to any question, with limited input, asking only for that answer tells us less about what a learner actually understands.
Asking them to explain their approach, evaluate alternatives and justify their decisions provides a much better test of understanding.
The responsibility for championing durable skills in the age of AI doesn’t just sit with educators.
If employers remove the routine work from junior roles, they can’t complain that people lack experience.
In this ever-changing economy, it isn’t enough to expect people entering the workforce to arrive with experience if they aren’t being given meaningful opportunities to gain it.
This doesn’t mean resisting AI. Young professionals will need to become comfortable using these tools. But they also need the technical foundation and professional judgement to use them well.
The technology sector will continue to change, and the specific skills in demand today may look very different in a few years. The people best prepared for that uncertainty will be those who know how to learn, question, adapt and apply what they know.
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