What Happens When AI Does the Work That Used to Train Tomorrow’s Leaders?

For decades, the early stages of a career have been built around doing the groundwork. Junior employees researched, prepared reports, drafted documents and handled administrative tasks, gaining experience along the way.

AI is beginning to change that learning curve. Many of those tasks can now be completed in minutes, raising a bigger question for employers: if technology removes some of the work people traditionally learnt through, how do businesses ensure the next generation still develops the skills needed to lead?

The missing first rung

“The first rung of the career ladder was never only about completing relatively basic work. Junior employees sat in on customer conversations, made mistakes, watched how more experienced colleagues responded to them and gradually learnt which details mattered,” said George Mienie, CEO of AutoTrader. “Over time, that exposure helped build the context and judgement required to make more consequential decisions.”

“The question, then, isn’t just whether AI will replace junior jobs. It’s how we develop the managers and leaders of tomorrow if AI is doing much of the work they traditionally learnt through.”

This is a question shaped by experience. He has led AutoTrader through several fundamental shifts in technology and business structure, from its origins in print classifieds to a digital marketplace, then to a data-led business, and now to pioneering AI-powered automotive intelligence. Each transition changed more than the product AutoTrader offered its customers. It changed the work people did, the skills the business needed and where people added value.

“Organisational structures were built, at least partly, around the fact that information was historically slow and expensive to gather, interpret and move. Junior employees gathered it, managers interpreted it and senior leaders made decisions from it. AI can now do some of that work almost immediately, shortening the chain and changing the role people play within it,” he pointed out.

“That creates an important distinction between removing low-value work and removing the opportunity to learn.”

“Companies can automate repetitive tasks without allowing the experiences that develop judgement, accountability and commercial understanding to disappear with them. But doing so will require deliberate changes to early-career roles, training and mentorship.”

Building a new career ladder

That could mean bringing younger employees into important decisions earlier, rather than expecting them to earn that exposure through years of administrative work. They will need opportunities to engage with customers, understand problems, question recommendations and take responsibility for outcomes.

“The ability to produce a plausible answer is becoming inexpensive, while knowing whether that answer makes sense in a particular market, for a particular customer still depends heavily on experience and context,” he noted. “The more easily information can be generated, the more valuable it becomes to know which information matters, what should be questioned and where an apparently logical answer may be incomplete.”

That also changes what employees need to learn. Knowing how to prompt an AI system will not be enough. People need to understand the problem they are trying to solve. Communication, curiosity, and relationship-building become more important, not less.”

AutoTrader’s own experience with AI has reinforced that point: the technology itself is only one part of the equation.

Technology alone isn’t the answer

AutoTrader has spent roughly a decade developing AI capability on top of more than 30 years of automotive market data, supported by an in-house team of data scientists and engineers. That experience has shown that as access to sophisticated AI becomes more widespread, the technology itself becomes less of a differentiator. Proprietary data, well-understood processes and deep market knowledge are far harder to replicate.

For Mienie, that distinction matters because foundation models are improving so quickly that their capabilities are increasingly likely to converge. What appears exceptional today can become standard within a relatively short period, which makes a strategy built around access to the newest or most capable model inherently fragile.

But ultimately, those assets still depend on people who know how to use and interpret them.

“A model may be available to every competitor, but knowing what to ask of it still requires human experience and judgement,” said Mienie.

“Businesses can automate the work, but they can’t automate the experience people need to make good decisions. If AI is changing how that experience is gained, then we need to change how we develop people. The companies that get this right won’t simply be the ones that work out how to use AI. They’ll be the ones that work out how their people continue to learn and grow alongside it,” he concluded.