JANUARY 22 — There is a growing anxiety among employers that artificial intelligence is reshaping career pathways too quickly, especially at the entry and early-career levels. 

Many organisations are quietly asking the same question: if AI is now doing the work that juniors used to learn from, how do we develop the next generation of capable professionals?

It is a fair concern. But it is also the wrong place to focus.

Recent conversations in global media, including a widely discussed piece in The Globe & Mail, suggest that AI is disrupting the traditional “start small, learn slowly, move up” career model. 

Entry-level tasks are disappearing. Junior roles look different. Some organisations respond by tightening hiring, delaying recruitment, or raising expectations for young candidates.

From an employer’s perspective, that instinct is understandable. From a leadership perspective, it is short-sighted.

The real challenge is not that young workers lack experience. It is that many organisations are still hiring and developing talent as if AI were merely a tool, rather than a structural change to how work gets done.

For decades, companies relied on a familiar progression. Juniors handled routine tasks. Mid-levels supervised and refined. Seniors made decisions. 

AI has compressed that model. Routine work is automated. Decision-making requires better judgment earlier. And learning now happens through exposure to real problems, not repetitive tasks.

This is where many employers feel stuck. They expect graduates to arrive “AI-ready,” yet struggle to define what that actually means. 

Too often, AI readiness is reduced to surface-level familiarity with tools, rather than the deeper capability to apply them meaningfully in real work contexts.

From where I sit, working closely with both employers and early-career professionals, the gap is not about effort or attitude. It is about alignment.

Young workers are adapting faster than we give them credit for. Many are experimenting with AI tools independently, building side projects, automating personal workflows, and learning in public through platforms like LinkedIn or GitHub. 

What they often lack is not initiative, but structured pathways inside organisations to translate that curiosity into value.

At the same time, employers are understandably cautious. They worry about accuracy, governance, data security, and quality control. 

So AI use gets restricted, sandboxed, or quietly discouraged at junior levels. 

The result is an unspoken contradiction: we want innovative, AI-literate talent, but we do not give them the space to practise.

This tension cannot be resolved by hiring more “experienced” candidates. Experience itself is being redefined. 

Five years of experience in a pre-AI workflow may not prepare someone for today’s challenges as well as one year of thoughtful experimentation with modern tools.

The organisations that are navigating this transition well are not asking, “How do we replace entry-level work?” They are asking, “How do we redesign early careers?”

That redesign starts with a mindset shift. Entry-level roles should no longer be framed around task volume. They should be framed around problem exposure. 

Give young employees real questions to explore, clear boundaries, and access to tools. Let them propose solutions, test ideas, and reflect on outcomes. 

Supervision still matters, but it should focus less on checking outputs and more on developing judgment.

This also requires managers to evolve. Many middle managers were promoted for technical competence, not for coaching in ambiguous environments. 

AI-enabled work demands a different leadership posture: one that encourages exploration while maintaining accountability. That is a skill organisations must deliberately build.

There is also a branding implication employers should not ignore. Young professionals are watching closely. 

They are paying attention to which companies trust juniors with responsibility, which organisations invest in learning, and which ones still expect obedience before contribution. In a tight talent market, reputation travels fast.

If employers want graduates who can think, adapt, and grow with AI, they must become places where learning is visible and safe. 

That does not mean tolerating recklessness. It means creating clear frameworks for experimentation, shared standards for quality, and honest conversations about mistakes.

AI is not eliminating early careers. It is exposing which organisations actually know how to develop people.

Those that treat AI as a cost-cutting shortcut will struggle to build depth. Those that treat it as a developmental accelerator will produce stronger teams faster. 

The difference lies in whether leadership sees talent as a resource to extract from, or a capability to cultivate.

As we move deeper into this transition, employers have a choice. They can wait for a mythical “perfect” candidate who arrives fully formed. Or they can participate actively in shaping the kind of professionals the future demands.

The companies that choose the second path will not just survive AI disruption. They will lead through it.

And they will not ask where all the entry-level talent went. They will know exactly where it is, because they helped build it.

* Ts. Elman Mustafa El Bakri is CEO and Founder of HESA Healthcare Recruitment Agency and serves on the Industrial Advisory Panel for the Department of Biomedical Engineering, Universiti Malaya. He may be reached at elman.asia@gmail.com 

** This is the personal opinion of the writer or publication and does not necessarily represent the views of Malay Mail.