The Disappearing First Step: The Experience Ladder Is Breaking

“Are we still creating the conditions through which experience and judgement are formed?”

A recent Deloitte article on the growing “experience gap” really fascinated me.

It discussed what happens when organisations automate the very work people once learned from.

For decades, most careers followed a predictable structure. Entry-level roles provided exposure, repetition, and low-risk responsibility. People learned by doing. They made mistakes in contained environments, gradually developed judgement, and built the confidence needed for more complex work later on. But we see that structure changing.

Across industries, AI and automation are increasingly absorbing the types of tasks that once formed the foundation of early careers. This includes drafting documents, administrative coordination, first-pass analysis, note-taking, customer interaction, and routine operational work. In many cases, this has been rightly framed as a productivity gain.

According to Deloitte's research and certainly the feedback I get from customers, we may be removing some of the very experiences that historically helped people become capable professionals in the first place.

Deloitte describes this as an “experience gap”, a growing disconnect between the experience organisations expect people to have and the opportunities available to gain it.

It’s a deceptively simple observation, but the implications are significant.

This is absoltely a workforce issue but increasingly, it may become a safety and performance issue too.

Why Experience Matters More Than Skills Alone

Experience is not just “time served", it is repeated exposure to ambiguity, consequence, uncertainty, prioritisation, and judgement. It is the gradual development of professional intuition, the ability to recognise patterns, navigate nuance, and make decisions when situations do not fit neatly into a process.

Historically, entry-level work acted as that developmental buffer and gave people opportunities to practise thinking in relatively low-risk environments before being held fully accountable for outcomes.

However, many of the tasks now being automated were never purely administrative, they were also developmental.

Writing a first draft.

Summarising a meeting.

Reviewing source material.

Reconciling conflicting information.

Documenting decisions.

Preparing reports.

Structuring thoughts before presenting them.

These activities often seem low value when we think about what technology can help us with, but cognitively they served another purpose: they helped people learn how to think within a profession.

When those steps disappear, the learning hidden inside them can disappear too.

Healthcare Already Shows What This Looks Like

Healthcare, one of Kafico's core sectors, may be one of the clearest examples of this shift already emerging.

Many clinicians describe writing notes before entering them into the electronic health record as an important part of learning and reflection. Drafting an assessment, documenting a patient interaction, or summarising a consultation often helped clinicians organise information, process uncertainty, and develop clinical reasoning over time.

In practice, the act of writing itself became part of the learning process.

Junior clinicians learned to identify what mattered, structure their thinking, justify decisions, and reflect on what they may have missed. Much of that development happened quietly through repetition.

Now, AI systems are increasingly capable of generating summaries, drafting clinical documentation, suggesting coding, and even proposing treatment pathways automatically.

There are clear benefits to this because the administrative burden in healthcare is very real, and reducing unnecessary documentation load is important.

But there is also an question underneath the efficiency gains:

If parts of the reflective process are removed too early, where does that developmental experience come from?

The concern is not that clinicians will stop writing notes.

The concern is that some of the low-risk cognitive work through which professional judgement traditionally develops may begin to disappear before capability is fully formed.

Healthcare is unlikely to be the only sector facing this challenge.

It is simply one of the first environments where the consequences of capability gaps become visible quickly.

The Emerging Capability–Accountability Gap

What makes this shift particularly important is that accountability is not decreasing alongside experience.

Deloitte's work tells us that in many sectors, expectations are increasing and managers are expected to oversee increasingly complex systems. Professionals are expected to justify decisions, manage risk, and demonstrate governance. Regulatory frameworks continue to expand. AI itself introduces new oversight responsibilities around assurance, bias, safety, and decision-making.

At the same time, the workforce entering these environments may have had fewer opportunities to develop foundational judgement through practical exposure.

The result is a growing capability–accountability gap:

• People are expected to make higher-stakes decisions

• With less prior exposure to similar situations

• In systems that are becoming more complex and automated

• While carrying greater responsibility than ever before

We are increasing accountability while quietly reducing some of the mechanisms through which capability has traditionally been built.

Over time, that can lead to hesitation, over-reliance on systems, poor judgement in unfamiliar situations, or reduced confidence when automation fails to provide clear answers.

Rethinking How Experience Is Built

If AI changes the structure of entry-level work, organisations cannot assume experience will simply emerge naturally in the way it once did.

Experience may need to become something organisations intentionally design for.

That could include:

• Structured exposure to real-world decision-making with appropriate oversight

• Protected opportunities for reflection and reasoning, not just task completion

• Deliberate developmental pathways rather than assuming experience accumulates automatically

• Better support for managers responsible for developing others

• Designing AI systems that augment professional learning rather than bypassing it entirely

A More Honest Question

It seems then, that my customers will need to stop asking:

“Do we have the right skills?”

And start asking

“Are we still creating the conditions through which experience and judgement are formed?”

Final Thought

The disappearance of entry-level work represents a structural change in how professional capability develops.

AI will undoubtedly remove friction from many forms of work. In some cases, that will be transformative and positive. But organisations also need to recognise that some tasks carry developmental value beyond their operational purpose.

And eventually, that becomes a performance, governance, and safety problem.

Emma Cooper, AI Privacy in Healthcare Nerd