The Missing Rung: How AI Is Breaking the Career Ladder and Who Will Train Tomorrow's Leaders

Every senior leader you admire started somewhere small. Building the first rough version of a financial model. Sitting at the back of a client meeting taking notes. Drafting the memo a partner rewrote in red ink. Fixing the bug nobody else wanted to touch.

None of that work was glamorous. Almost all of it is now work AI can do in seconds.

That is the quiet shift happening inside organisations across Australia, New Zealand, Singapore, Hong Kong and well beyond. It rarely makes the headlines because there is no dramatic redundancy announcement. Junior roles simply aren't backfilled. Graduate intakes get a little smaller. A team of five becomes a team of three plus a set of AI tools.

On a spreadsheet it looks like efficiency. Over ten years it looks like a leadership crisis.

This article looks at what is really happening to the first rung of the career ladder, why it matters far more than most boards realise, and what organisations and professionals at every stage can do about it right now.

The First Rung Is Disappearing Quietly, Not Dramatically

The data is uneven, and it's worth being honest about that. But the direction is clear.

In the United States, Stanford's Digital Economy Lab found in its August 2026 update that the employment shortfall for workers aged 22 to 25 in the most AI-exposed occupations had widened to 19%, up from 15% a year earlier. In the UK, job board Adzuna reported graduate listings fell below 10,000 in January 2026, down 45% on the year before.

Closer to home, the picture is mixed:

• Australia: Indeed Hiring Lab found graduate job opportunities fell almost 15% in 2025 before steadying in early 2026, with postings still above pre-pandemic levels. Yet research from Anglicare, reported in The Age, found entry-level and graduate roles now make up just 11% of all vacancies, the lowest share in a decade.
• Hong Kong: Reporting by the South China Morning Post points to graduate-level vacancies falling by more than half over recent years, with AI, a softer economy and stronger regional competition for talent all playing a part.
• Singapore: The Ministry of Manpower reports around nine in ten 2025 university graduates found work within 12 months. Government traineeship programmes such as GRIT have been extended to the 2026 cohort, a deliberate move to keep the entry path open in sectors adopting AI quickly.

Different numbers, same pattern. Analysts keep finding a seniority tilt: hiring is shifting towards experienced people while junior roles thin out. Most organisations aren't cutting graduates outright. They are simply asking whether they need as many, and New Zealand, the UAE, Europe and Canada are facing the same question.

Why Entry-Level Work Was Never Really About the Work

Here's the part most AI business cases miss. Junior roles were never just a cheap way to get routine tasks done. They were the training ground.

The reps you can't download

Expertise is built through repetition with feedback. Your first 50 financial models teach you where models break. Your first 100 customer complaints teach you what customers actually mean. Those early years of drafting, checking and being corrected build an instinct for what good looks like.

In Hidden Potential, Adam Grant makes the case that people grow fastest with the right scaffolding: structure and support that is gradually removed as capability builds. Entry-level work was that scaffolding. When AI takes the tasks, it takes the scaffold with them.

Judgement is built, not hired

AI is very good at producing a clean answer. What it can't yet do reliably is know whether that answer fits this client, this board, this culture or this moment. That contextual judgement is exactly what organisations pay senior people for.

The uncomfortable truth is that judgement only comes from years of doing the work AI is now absorbing. Remove the early reps and you don't just lose junior staff. You lose the senior leaders you would have had in 2035.

The Ten-Year Cost Nobody Is Modelling

Most AI business cases measure the saving in year one. Very few measure the cost in year ten.

Behavioural scientist Gleb Tsipursky has suggested a simple discipline: before automating a block of entry-level work, stress-test the career ladder above it. Look at the role directly above the junior one. Ask where the next person for that role will come from, and how they will have learned the job.

If the answer is "we'll hire them in from outside", ask the follow-up question. From where? If every organisation makes the same decision, the market for experienced mid-level talent becomes tight, expensive and fiercely competitive within a few years.

That is the hidden bill. Organisations that hollow out their talent pipeline today will pay for it later through premium salaries, slower decision making and a thin bench of future leaders.

What Smart Organisations Are Doing Instead

The good news is that a response is already taking shape. Large professional services and law firms are among those redesigning early-career roles to put apprenticeship back into work AI has hollowed out. Here are five moves any organisation can make, whatever its size.

1. Redesign junior roles around steering AI, not competing with it

McKinsey's research on who trains the next generation suggests flipping the purpose of entry-level work. Instead of producing first drafts, junior people learn by checking, challenging and improving what AI produces, with an experienced person alongside them. They still get the reps. They are simply reps in judgement rather than reps in typing.

2. Run a career-ladder stress test before you automate

Before any role is removed or left unfilled, map the progression path above it. Which capabilities did that role build? Where will people build them now? If nobody can answer, the saving may be a false economy.

3. Make teaching an explicit part of senior roles

Ravin Jesuthasan and John Boudreau have spent years showing that work is best understood as a set of tasks rather than fixed jobs. Apply that lens here. As AI frees senior people from some of their own routine tasks, deliberately reallocate part of that time to coaching, reviewing and explaining. If teaching isn't in the role description, the scorecard and the calendar, it won't happen.

4. Bring in experienced experts who build capability, not dependency

This is where flexible talent models earn their place. A fractional expert or interim executive who embeds in your team for six months can do far more than deliver a project. Done well, they leave behind people who think differently, work differently and are ready for the next step.

This is the model we believe in at Maestro. The best engagements aren't the ones where an expert solves the problem alone. They are the ones where a senior operator works shoulder to shoulder with a rising team member and passes on decades of pattern recognition in months. We've written more about how to build a team of independent experts to tackle your toughest challenges.

5. Borrow from traineeship models that work

Singapore offers a useful lesson. Rather than leaving graduates to fend for themselves, it has built structured traineeships that give them real industry experience while employers adjust to AI. Businesses in Australia, New Zealand and Hong Kong don't need to wait for government to act. Paid traineeships, rotations and project-based apprenticeships can be designed inside any mid-sized organisation.

What This Means for You, Wherever You Sit on the Ladder

This isn't only a problem for boards and HR teams. It changes career strategy for everyone.

If you are early in your career

Go looking for apprenticeship, not just a job title. Choose roles and employers where you'll sit close to experienced people and get honest feedback. Become genuinely skilled with AI, but use it to learn faster, not to skip the learning. And build experience wherever you can find it. Projects, internships, freelance work and volunteer roles all count.

If you are mid-career

You may feel squeezed from both directions: fewer juniors to delegate to and more expectation to deliver with AI. Lean into it. People who can translate between what AI produces and what the business actually needs are becoming some of the most valuable in any organisation. Teaching others to do the same is a fast route to leadership.

If the pace of change is leaving you uneasy, you're not alone. We explored this in our piece on capability anxiety and how to turn skills uncertainty into growth.

If you are a senior leader or independent expert

Your experience has never been more valuable, but where that value sits is shifting. Knowledge is easy to look up. Judgement, context and the ability to grow others are not.

Lynda Gratton's work on longer, multi-stage working lives suggests many of us will reinvent our careers several times. For a growing number of senior professionals, the next stage is a portfolio career that blends advisory work, fractional leadership and mentoring. The organisations that need you most may be the ones that have lost their own pipeline. Programmes like Maestro Mentors exist for exactly this reason.

Heather McGowan has long argued that in a world of constant change, the ability to keep learning is the real source of career security. That applies to the people doing the teaching as much as the people being taught.

The Opportunity Hidden Inside the Problem

It would be easy to read all of this as bad news. It doesn't have to be.

AI is forcing organisations to answer a question they have avoided for decades: how do we actually build expertise? For a long time the answer was accidental. Put bright young people near experienced people, give them plenty of grunt work and hope the learning happens.

Now it has to be designed. That is a chance to build something better. Faster and more intentional development. Closer connection between generations at work. A deliberate role for experienced professionals as teachers and multipliers.

The organisations that get this right will have a deep bench of future leaders while everyone else fights over the same small pool of experienced talent. The professionals who get it right will be the people everyone wants on their team.

The bottom rung is disappearing. The ladder still matters. Someone has to build the new one, and the best time to start is now.

Frequently Asked Questions

Is AI really replacing entry-level jobs?

AI isn't wiping out entry-level work entirely, but it is reducing the number of junior roles in the most AI-exposed fields such as software, finance, research and administration. The impact varies by country and sector, with Australia and Singapore showing more resilience so far than the UK, the US and Hong Kong.

Why does losing entry-level roles matter for businesses?

Entry-level roles are where people build the judgement and context that make them effective senior leaders later. Removing them may save money now but can create a shortage of experienced talent in five to ten years.

How can organisations keep developing junior talent when AI does the routine work?

Redesign junior roles so people learn by reviewing and improving AI output with experienced guidance, make coaching an explicit part of senior roles, and use fractional experts and interim leaders to transfer capability into teams.

What should graduates and early-career professionals do?

Prioritise roles with strong mentoring and feedback, build AI fluency as a learning tool, and gather experience through projects, internships and freelance work while looking for a permanent role.

How can fractional experts help close the experience gap?

Fractional experts and interim executives bring decades of experience into a team for a defined period. When engagements are designed around knowledge transfer, they speed up the development of internal talent rather than creating long-term dependency.

Join the Conversation

Is your organisation still investing in early-career talent, or is the first rung quietly disappearing? We'd love to hear what you're seeing in your team and your industry. Follow Maestro on LinkedIn and Peter Bauld for more practical thinking on the future of work.