There is something remarkable about watching a young professional work with artificial intelligence today. A 24-year-old can enter an unfamiliar subject, interrogate a large body of information, analyse data, prepare a presentation, draft a note and test several ideas before a more experienced executive has finished deciding where to begin. The tools available to this generation would have seemed extraordinary to those who started their careers even a decade ago.
Gen Z should embrace that advantage. They should be faster. They should question unnecessary processes and challenge inherited ways of working. If technology can remove repetitive work, there is little reason to preserve it simply because previous generations had to endure it. A young employee who knows how to use AI well can potentially contribute far beyond what their years of experience might suggest.
But there is a paradox emerging inside this productivity revolution. Some of the work AI is taking away is also the work through which young professionals traditionally acquired experience.
The Work That Taught Us How To Work
The first years of a career were rarely glamorous. Young employees read documents, checked numbers, prepared research, wrote first drafts, attended meetings and watched experienced colleagues deal with difficult situations. They made mistakes that were usually small enough to fix. Over time, repetition built familiarity, and familiarity gradually became judgement.
A young analyst did not become good at analysis merely by studying finance. They became good by looking at hundreds of sets of numbers and learning what mattered, what looked unusual and what deserved another question. A young executive did not learn stakeholder management from a course. They learnt it by sitting in uncomfortable meetings, observing experienced leaders and eventually being trusted with difficult conversations themselves.
The apprenticeship was hidden inside ordinary work.
AI is beginning to change that equation. A young employee can ask a machine to conduct the first round of research, analyse information, produce a draft or suggest possible solutions. That is an enormous productivity gain. It can free people to spend more time on thinking, creativity and problems that genuinely require human judgement.
Yet companies need to ask a difficult question: if the machine does the first 80 per cent of the work, where does the young employee learn what the remaining 20 per cent actually means?
Producing a polished answer is increasingly easy. Knowing whether it is right, incomplete, commercially sensible or dangerously wrong is considerably harder.
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GenZ Has Advantage Older Generations Never Had
The conversation about Gen Z, therefore, needs some generosity. Young professionals are not entering an easier workplace. They are entering one in which the ground is moving beneath everyone.
They will have to learn technologies that change rapidly, work alongside increasingly capable machines, develop expertise while information becomes abundant and make career choices when the useful life of many skills is shortening. They are also entering organisations where traditional markers of seniority may matter less because technological fluency can arrive very early.
A 25-year-old may understand an AI workflow better than someone who has spent three decades in an industry. That is an advantage organisations should use, not diminish. At the same time, the experienced executive may understand customers, regulation, institutional memory, organisational politics and the consequences of a bad decision in ways that cannot be acquired from a prompt.
This creates an unusual intergenerational exchange. Technological fluency can flow upwards, while institutional wisdom flows downwards. A Gen Z employee may teach a senior executive how work can be redesigned, while that executive can teach the young professional why an apparently sensible solution sometimes fails when it encounters customers, regulators, colleagues or reality.
Both forms of knowledge matter.
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Future of Work Needs New Apprenticeship
The answer is certainly not to deny young employees access to AI. Asking them to reproduce inefficient methods merely so that they can "learn the hard way" would be a poor response to technological change.
The answer is to redesign the apprenticeship.
Companies need to become deliberate about which experiences young employees must still have personally. AI should be used aggressively for research, drafting and repetitive analysis, while young professionals are given opportunities to make decisions, defend recommendations, deal with ambiguity, interact with customers, manage disagreement and experience consequences.
Managers will have to change too. Their role cannot remain primarily about correcting work produced by juniors. If AI produces the first draft, the manager's greater contribution may be teaching the young professional how to interrogate it. What assumption could be wrong? What information is missing? What would change the conclusion? Who could be affected? What happens if the recommendation fails?
These are questions that cannot simply be automated away.
This distinction between information and judgement will become increasingly important. AI can give a young professional access to enormous amounts of knowledge, but professional maturity comes from knowing which knowledge matters, recognising uncertainty and taking responsibility when the answer is unclear.
That is why boards and CEOs should put a new question alongside discussions about AI productivity, workforce redesign and cost savings: how are we developing tomorrow's decision-makers?
Every organisation is continuously building its leadership pipeline, whether it calls it that or not. Today's graduate becomes tomorrow's manager, business head and potentially senior executive. The experiences accumulated during those early years shape the judgement the organisation will eventually depend upon.
If AI removes the apprenticeship layer without replacing its developmental function, companies could face an unexpected weakness. They may have young professionals who are technologically sophisticated, remarkably productive and capable of producing impressive work, but who have had too little exposure to the messy situations in which judgement is formed.
Gen Z does not need to become like previous generations to succeed. In many respects, previous generations will need to learn from them. Their technological confidence, speed and willingness to question old processes can be valuable assets for organisations struggling to reinvent themselves.
But Gen Z will also encounter a truth every generation eventually discovers: there are forms of understanding that cannot be accelerated simply by having better tools.
Experience still has to be lived.
The organisations that thrive will therefore use AI to remove drudgery while deliberately creating new forms of apprenticeship. They will give young people extraordinary tools without confusing capability with judgement, and speed with maturity.
AI may shorten the time it takes Gen Z to produce good work.
The leadership challenge is ensuring it does not lengthen the time it takes them to become good decision-makers.
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