AI is making it possible to automate more entry-level work, but organizations still need to develop the judgement, adaptability and expertise their future workforce will require. The real challenge is redesigning how human capability develops as the work itself changes.
Cutting Entry-Level Jobs Creates a Bigger Question
As AI becomes capable of performing more of the research, analysis and drafting traditionally assigned to junior employees, reducing entry-level hiring can look like an obvious efficiency gain. Experienced professionals can produce more with AI, organizations require fewer people to complete the same volume of work, and the economics appear compelling.
Yet this raises a question that receives far less attention than the immediate productivity calculation: how will organizations develop the people they will need five or ten years from now? Entry-level work has never existed solely because companies needed someone to perform relatively simple tasks. It has also provided the environment in which people accumulated experience, received feedback, learned from mistakes and gradually developed the judgement required for more complex responsibilities.
AI Changes the Development Path
This matters because many of the human capabilities becoming more valuable as AI advances are precisely those that cannot be acquired simply by learning how to use the technology. Professional judgement, critical thinking, adaptability and the ability to evaluate whether an AI-generated answer is appropriate all depend on knowledge and experience accumulated over time.
The emerging evidence reflects this tension. Microsoft engineering leaders Mark Russinovich and Scott Hanselman have described how experienced professionals can receive a significant productivity boost from AI because they already possess the expertise required to direct and evaluate it, while less experienced employees may struggle for precisely the opposite reason. The observation points to a broader organizational challenge: AI can amplify expertise remarkably well, but organizations still have to create that expertise in the first place.
Human Capability Development Has to Become Deliberate
This is where we believe organizations need to rethink their approach. The objective should not be to preserve entry-level jobs simply because they existed before AI. Many routine tasks can and should disappear. The more important task is identifying what people were learning while performing those tasks and ensuring that the capabilities still required by the organization have another pathway through which to develop.
That requires a much more deliberate approach to human performance. If junior employees no longer spend years producing first drafts, how will they develop the expertise required to recognise a weak one? If AI conducts much of the initial analysis, where will employees learn to interrogate assumptions and identify what has been missed? If technology allows experienced professionals to complete more work themselves, how will younger colleagues gain the exposure to difficult decisions through which professional judgement develops?
These are questions of work design, but they are equally questions of leadership and management. Managers need to provide younger employees with progressively more complex experiences, create opportunities to exercise judgement and give feedback that helps them understand why a decision succeeded or failed. Organizations also need to be much clearer about which human capabilities they expect to remain strategically important and then ensure that their AI-enabled ways of working continue to develop them.
This Is What AI Workforce Readiness Actually Requires
At MLC Advisory, this is the kind of question we believe should sit at the centre of workforce preparation for AI. Organizations need to understand not only where technology can increase productivity, but how changes to work affect the human capabilities on which future performance will depend. That means examining roles, managerial practices, learning opportunities and performance expectations together rather than treating talent development as something that will take care of itself after the technology has been deployed.
A useful starting point is deceptively simple: when AI removes a task, what capability was previously being developed through that task, and where will people develop it now? Applied systematically, that question can reveal gaps in a talent strategy long before they appear as shortages of experienced people.
The organizations that address those gaps early have an opportunity to build better development pathways than the ones AI is disrupting. Junior employees can spend less time on repetitive work and gain earlier exposure to analysis, judgement and meaningful responsibility, while managers become more intentional about developing the capabilities that technology cannot supply on an organization’s behalf. AI can then become part of a stronger human performance system rather than simply a mechanism for reducing its headcount.