For generations, the architecture of a professional career was relatively easy to understand. You entered an organization near the bottom, learned the fundamentals, accumulated experience, earned promotions, and gradually moved upwards.

Artificial intelligence is beginning to complicate that familiar trajectory.

The World Economic Forum’s 2026 report, Artificial Intelligence and the Future of Entry-Level Work: A Framework for Safeguarding and Reinventing Early Career Pathways, suggests that AI is not only changing individual jobs. It is putting pressure on the organizational structures through which people traditionally build careers.

This distinction matters. A company may automate a task and achieve an immediate efficiency gain. But if that task once helped a junior employee acquire the knowledge required for a more senior position, its disappearance creates another question entirely: where will tomorrow’s experienced professionals come from?

Part Three of the report examines this issue through the lens of talent pipelines. Its findings suggest that organizations may need to reconsider what career progression looks like when roles become more fluid, hierarchies become less rigid, and professional capability is developed through a broader range of experiences.

AI is putting pressure on the traditional organizational hierarchy

The conventional organizational pyramid was built around progression.

A large population of junior employees formed its base. A smaller number progressed into management, and fewer still advanced into senior leadership. Entry-level positions therefore performed two functions at once. They supplied organizations with employees for immediate operational needs while developing the talent that could eventually assume more complex responsibilities.

AI is challenging parts of this structure.

According to the World Economic Forum report, 23% of CEOs believe their present organizational structures are constraining performance. Business leaders interviewed for the study described possible shifts towards flatter organizations and even “diamond”-shaped structures rather than conventional pyramids.

There is no broad consensus, however, on what the organization of the future will ultimately look like. What is clearer is where many leaders expect the greatest disruption.

Expectations of significant AI-related structural change at the entry level are almost twice as high as expectations for mid-level or senior roles. Across industries, three-quarters of leaders expect significant realignment at the entry level, including leaders in Financial Services, Health and Technology.

This places early-career work at the centre of organizational transformation.

Why entry-level roles remain strategically important

The temptation might be to interpret a flatter organization as one that simply requires fewer junior employees. The report presents a more complicated picture.

Entry-level work has historically been one of the principal mechanisms through which organizations develop capability internally. Junior employees learn the fundamentals of a profession, gain exposure to increasingly complex situations, develop judgment, and eventually become the experienced professionals on whom organizations depend.

Changing the bottom of the organizational hierarchy therefore has consequences further up.

The World Economic Forum frames the management of talent pipelines as a strategic issue. Its recommended objective is not to preserve every existing role exactly as it is. Rather, organizations should intentionally design talent pipelines instead of allowing them to emerge as an accidental consequence of hiring patterns.

The report proposes a movement away from fixed, role-based workforce structures and job architectures towards more capability-based approaches to talent development. Talent should be recognized and proactively matched with roles as those roles evolve.

In other words, the career pipeline may remain essential even if the career ladder changes.

Career progression may become less linear

The traditional career model has been closely associated with hierarchy.

An employee moved from one defined position to another. Greater responsibility generally came with a more senior title, higher compensation, and another step upwards through the organization.

The report suggests that AI-enabled workplaces may increasingly develop capability differently.

Rather than relying primarily on hierarchical sequence, organizations may build skills through non-linear movement and exposure. Employees can move between projects, functions and areas of responsibility, developing capabilities through a wider range of experiences rather than waiting for the next formal position to become available.

This can allow people to develop new skills faster and more flexibly.

The shift is already visible in discussions around more fluid and hybrid roles. Leaders interviewed for the report described individuals moving across projects and, in some cases, operating as “multi-players” across different domains as work requirements evolve.

The implication is not that specialization or hierarchy will disappear. The report does not make that prediction. Rather, it suggests that hierarchical progression may no longer be the only significant mechanism through which professional capability develops.

Employees still want to know when they are moving forward

There is, however, a distinctly human complication.

Organizations may be moving towards more fluid definitions of development, but employees still recognize the traditional language of career advancement.

The World Economic Forum reports that 31% of entry-level workers plan to ask for a promotion within the next year.

This creates a potential tension.

An organization may provide an employee with new projects, cross-functional exposure and increasingly sophisticated responsibilities. From the organization’s perspective, that employee may be progressing considerably. Yet the employee may still judge progress through familiar signals such as promotion, title and compensation.

The report therefore identifies the need to redefine and communicate progression more clearly.

If career development becomes less dependent on moving vertically from one job title to another, organizations will need credible ways to show employees how their capabilities are growing, how those capabilities are recognized, and where that development can lead.

Career progression does not cease to matter simply because its structure becomes less linear.

Capability may become more important than position

This leads to one of the more consequential ideas in the report: organizations may increasingly need to think about talent in terms of capability rather than fixed roles.

A traditional job architecture begins with positions. An organization defines a role, lists its responsibilities, and finds an individual who fits it.

A more capability-based model asks a somewhat different question: what can this individual do, and where can those capabilities create value as work changes?

That distinction becomes increasingly relevant when AI can alter the composition of a role quickly. Certain tasks may become automated. Others may grow in importance. New responsibilities may emerge. Employees may therefore need to move between different combinations of work rather than remain within static job descriptions.

The World Economic Forum recommends that organizations move from fixed role-based workforce structures towards capability-based models of talent development, with talent proactively matched to evolving roles.

Such an approach does not make professional expertise less important. It changes how that expertise can be deployed and developed.

What AI-enabled career development looks like in practice

The report’s case study of Hitachi offers a useful example of how these principles can operate within an organization.

Hitachi is redesigning roles as routine and transactional activities become increasingly automated. Early-career employees in parts of its digital businesses are working with AI-enabled workflows from the beginning of their careers and contributing to more complex activities sooner than they might have under traditional role structures.

But the company does not automate every task simply because it can.

Experiences that develop judgment, critical thinking and business understanding are intentionally preserved. Early-career engineers, for example, are still expected to develop coding fundamentals before relying heavily on AI-generated code. The purpose is straightforward: they need sufficient understanding to validate, question and challenge AI outputs.

In operational environments, employees similarly remain responsible for interpreting information, questioning system recommendations and applying human judgment, particularly where safety is involved.

At the same time, Hitachi is increasing cross-functional exposure as progression becomes less closely tied to traditional hierarchy and more connected to demonstrated capability and impact.

The example captures an important distinction in the report. AI proficiency and foundational expertise are not presented as substitutes for one another. Organizations still need people who understand the work well enough to evaluate what the technology produces.

The first years of a career could become more demanding

There is another consequence to giving junior professionals access to more sophisticated tools.

AI can allow entry-level employees to contribute to complex work earlier.

On one level, this presents an opportunity. New professionals may gain exposure to responsibilities that previously required years of experience before they could meaningfully participate.

But acceleration introduces its own risks.

If technology allows someone to reach a complex task without first developing the underlying knowledge traditionally acquired through simpler work, an important stage of professional development may be skipped. The report specifically highlights concerns about the loss of critical learning steps and the potential consequences for quality and decision-making.

The challenge for employers is therefore not simply to move junior employees towards higher-value work as quickly as possible. It is to determine which foundational experiences remain necessary for developing sound judgment.

For students and early-career professionals, this distinction is equally significant. Access to sophisticated technology can increase what someone is capable of producing. It does not automatically provide the experience required to evaluate whether that output is correct.

What this means for universities and future graduates

The transformation of talent pipelines is principally an organizational issue in Part Three of the report, but its implications reach naturally into higher education.

If careers become less linear and organizations increasingly recognize capability across different projects and functions, preparing for professional life becomes about more than mastering the requirements of a first job.

Students may enter workplaces in which responsibilities change quickly, AI tools are embedded into everyday processes, and opportunities for advancement depend increasingly on demonstrated capability and impact.

This places greater importance on experiences that require students to apply knowledge across different situations.

Internships, industry projects, collaborative work and exposure to professional environments can give students opportunities to encounter the ambiguity that accompanies real work. A classroom can establish knowledge. Applied experience asks the student to decide what to do with it.

This connection is particularly relevant to an institution such as Enderun Colleges, where internships, industry engagement and applied learning form part of the educational experience across disciplines. The value of such exposure is not that it predicts exactly what a graduate’s future job will look like. In an AI-enabled economy, that may be increasingly difficult to do.

Its value lies in giving students opportunities to develop and demonstrate capabilities across different environments before their careers formally begin.

The future career may look less like a ladder

For students entering university today, the career ahead may be less orderly than the one previous generations were taught to expect.

That need not mean fewer opportunities.

The World Economic Forum’s analysis suggests the possibility of careers in which people encounter complex work earlier, move more frequently across projects and functions, and develop through exposure rather than hierarchy alone.

But greater flexibility requires greater clarity.

Organizations will need to ensure that talent pipelines continue producing the capabilities they will require in the future. They will need to identify how employees progress when advancement does not always mean another rung on a ladder. And they will need to preserve the experiences through which professional judgment and expertise are built, even when AI can perform some of the underlying tasks faster.

For future professionals, the lesson is equally consequential. A career may increasingly be defined not only by the positions a person has held, but by the capabilities that person has developed, demonstrated and carried from one challenge to the next.

AI may be changing the organizational chart.

The more important question is whether organizations can redesign the journey through it.