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The early career paradox: Saving the entry-level role in the age of AI 

Employees in the emerging talent pool are increasingly facing higher expectations earlier in their careers, while receiving inadequate guidance and support. At the same time, roles, pay, and career progression opportunities lag behind expectations. Unless employers address these issues, they risk disengagement, higher staff turnover, and weaker future leadership pipelines.

The early career challenge at a glance

  • AI is automating foundational tasks, increasing responsibility while making progression pathways less clear for emerging talent.
  • Flatter organizational structures and AI integration require the intentional redesign of work, team compositions and employee development pathways.
  • As promotions slow, skills-based rewards and transparent progression are becoming essential to retain and engage emerging talent.
  • To future-proof their talent pipelines, organizations must measure AI adoption accurately and embed strategic workforce planning.
Today, entry level job vacancies that require no prior experience feel increasingly rare. This is partly because many foundational skills and responsibilities can be automated or performed by AI. To see this change for yourself, ask this question: How many of the tasks I did in my first entry-level role would be absorbed by AI today?

This means that people starting out in their careers face a number of growing challenges:

  • They are applying to a smaller pool of relevant roles that carry higher decision-making responsibilities. 
  • Often, these jobs do not provide any built-in time for employees to build functional skills through coaching or shadowing. 
  • Emerging talent must navigate the tension of working effectively with AI while leaders reiterate the importance of uniquely human skills and networking. 
  • Despite the increasingly ‘senior’ reality of early-career roles, higher status or streamlined progression pathways are not generally part of the package. 

In response, some Gen Zers are abandoning the traditional career ladder and using AI to take the leap into entrepreneurship

Business leaders and HR should therefore look at the way they design roles, ways of working, rewards, and development pathways. They should ask: Are we responding to the challenges that emerging talent faces — and how can we make things better?

This is already happening: Some businesses are doubling-down on their investment in early careers. For example, IBM and Tesco have recently announced an increase in entry-level intakes.

Pressure mounts on entry-level career opportunities

Early career opportunities were already under strain before the AI effect started to become apparent. Indeed, youth unemployment has been a growing challenge for many years, with rates notably higher than adult rates in many regions around the world. Some business leaders are raising the alarm that entry-level pathways are narrowing further, warning that a drop in entry-level hiring will only intensify the pressure on young people trying to enter the workforce.

This means that many highly qualified graduates are competing for a smaller pool of entry-level positions, frequently finding their applications are rejected at the first stage of the recruitment process despite their employability on paper. This is a key issue, as estimates suggest that by 2030, 70% of the workforce will be Gen Z.

That said, AI will be a major driver of this trend moving forward. Almost all executives (99%) expect that between 1%–20% of roles face headcount reductions due to AI in the next two years.[1] Recent evidence suggests entry-level roles are among the first to go.

At the same time, as already highlighted, AI is changing the nature of entry-level work. Employers are increasingly feeding starter tasks to AI — these are the jobs that were once seen as a rite of passage for anyone getting their foot on the career ladder.

This is why those early-career employees who manage to secure a role face higher expectations. The work left to humans is higher‑stakes. It often involves interpreting outputs, challenging assumptions, shaping narratives for stakeholders, and making judgment calls independently. Such judgment calls are not easy for someone with little experience.

This problem is compounded by the fact that in many companies there are now fewer layers of management to coach younger talent or take final accountability. It is also exacerbated by the reality that in a flatter organizational structure with fewer middle managers, traditional managerial responsibilities are pushed downward.

Ironically, as emerging talent is pressured to prove their value faster, AI may well become their point person for guidance. But early-career employees may lack an understanding of the business context in which they work or have the depth of critical thinking skills to challenge AI’s assumptions. They may therefore make the “wrong call”. Given the responsibility they have been given, this may be costly.

Payback for employees fails to match higher responsibility

While employers expect more immediate readiness, broader skills and stronger interpersonal behaviors from emerging talent, this does not often translate to improved benefits for employees. In today’s job market, compressed job levels and slower promotions have led to stagnant wages, a lack of development opportunities and limited recognition for those in work. This is shown by the fact that, today, only 27% of Gen Z say people are promoted quickly enough in their organization; a similar proportion of other age groups feel the same.[2]  

The hard truth is that, without clear paths to growth and improved assessment of both what and how business outcomes are achieved, organizations will struggle to keep their remaining talent engaged. Leadership pipelines have already been thinned by the reduction in early career roles. They will suffer further as talent drops away.

Reward the skill, not the role

As entry level roles absorb more senior work, and flatter structures change the nature of promotion, organizations must rethink how employees are recognized and rewarded. 

There is currently an information gap in many organizations, with only 32% of employees saying they know which skills are most valued and that their organization pays a premium for those considered “critical”.[3]

This means that in flatter organizations:

  • Employers must communicate how fewer job levels have an impact on progression routes.
  • Opportunities should be made viable and visible to all.
  • Progression must be redefined through visible, skills based stretch and flow to work opportunities that are formally recognized and rewarded, not dependent on traditional promotions. 

Only 27% of employees say horizontal or lateral career moves are prevalent in their organizations.[4] However, for the sake of agility, continuous upskilling and growth, these moves will need to become more common.

There are clear benefits for effective engagement. Abundant opportunities to learn new skills and a clear view of how they can progress are cited by over 45% of employees as factors that help them thrive at work.[5However, to be sustainable, these opportunities must also bring recognition. 

Skills-based merit must therefore connect to performance management. Employees should be tangibly rewarded not just for learning, but also for applying business-critical skills. Employers should lean into this as part of their employee value proposition (EVP), including by providing evidence of how they are investing in employee growth and wellbeing. They should also provide employees with flexibility and a good work-life balance (which remain central aspirations for employees).

How to reset the trajectory of early careers

One effective way to reset early careers is to build deep generalist talent pools that can be deployed via a talent marketplace into outcome delivery teams. This can be done as demand shifts, and will reduce the need to hire into narrowly defined roles. 

This approach aligns with Mercer’s fixed–flex–flow view of how work is increasingly organized.

  • Fixed work sits in traditional, defined roles with a stable scope.
  • Flex work sits in defined roles, but a portion of responsibilities shifts to special projects and priorities — often enabled through internal talent marketplaces.
  • In flow-to-work models, talent moves to tasks, assignments, and projects based on skills and capacity rather than a static job scope.

In this model, early-career employees develop a strong core of transferable skills. These capabilities include problem-solving, stakeholder communication, data interpretation, and project delivery skills. These employees can then be deployed into time-bound initiatives in multifunctional teams. They can be selected based on skills, often operating like internal consultants.

These outcome-delivery teams increase organizational agility by exposing employees to end-to-end value chains. They also enable more visible, impactful work compared to siloed roles. Over time, these generalists accumulate a real depth of experience, while the business benefits from faster skills deployment. 

In many organizations, every hour of work is allocated to delivery. As a result, learning and development suffers. Today, only 34% of employees say they are encouraged to spend time learning new skills during work hours.[6]

Furthermore, although many employees are asked to set personal learning goals, when project deadlines loom, the necessary time for learning is often sacrificed. This can be due to a lack of governance around the issue of protected learning time. In such systems, the informality of skills development means access to learning is uneven, and too often determined by team culture or manager discretion. 

AI accelerates this dynamic, as it can cause tasks to shift quickly. In addition, access to the right tools, training and coaching remains inconsistent. Because of these factors, capability gaps can widen, inequity can compound, and churn risk can tick up. 

The business case for training and learning is clear: losing and rehiring early-career talent is typically far more expensive than building skills internally. Protecting dedicated time for structured development through supervised practice and learning is therefore vital. This time should be designed into the workweek to help employees build the “muscles” they need to succeed.

Ensuring that job design includes capacity for learning also benefits the EVP because it signals that the business is committed to future-proofing their workforce’s employability. Building spare capacity into job design also creates more room for part-time work, phased schedules, and job shares. This, in turn, helps improve work-life balance for caregivers and makes work more inclusive.

Case study

BT is broadening access to structured, employer-recognized learning beyond traditional early-careers pathways, treating reskilling as a whole-workforce priority as skill needs evolve. In the UK, BT is channeling the apprenticeship levy to support employees. Learning programs were recently launched in BT Business and People & Culture teams investing in Data and AI apprenticeships from Levels 3 to 6 to help colleagues build the confidence and capability to use data more effectively and unlock AI-driven efficiencies. This complements BT Business’s wider commitment, set out by Jon James, to provide AI training for all 14,000 colleagues and introduce new AI-focused apprentice and graduate roles, while creating inclusive, future-focused pathways for existing and future talent. Early benefits are already emerging, with further impact expected as the programme grows.

While the funding mechanism is UK-specific, the underlying approach is globally relevant: organizations can repurpose existing learning investments (publicly supported programs, funded credentials, or vendor partnerships) to create credible reskilling routes at any career stage to avoid reliance on external hiring and ease pressure on shrinking early-career talent pipelines.
With lifelong learning becoming a necessity, all of us will have multiple ‘early career’ moments, each time we move onto a new project or build a new skill set. The priority is creating structured, time-protected learning pathways that the business recognizes and that translate into real mobility into priority roles. The mechanism will vary by market, but the principle is the same: to stay agile, reskilling must be enabled by credible learning pathways for every employee.
Laura Pocock

Director of People & Culture, Business BT

Work design example: Software Developer

While many organizations are reassessing their early-careers and emerging talent strategies, software development remains a priority hiring area. At the same time, pay, limited manager support, and slow progression are among the top five reasons people consider leaving their employer.

Against this backdrop, intentional work design can be transformative. By redesigning tasks to free capacity, organizations can build structured skills development into day-to-day work so learning becomes part of the role, not an extra. And in the spirit of multiple early-career moments, the same setup supports not only emerging talent but the wider workforce, creating ongoing upskilling and reskilling pathways as capability requirements continue to shift.

Taking an illustrative Software Developer role, our Work Design analysis shows how redesigning tasks unlocks additional capacity that can be reinvested in development, or redirected to higher-value projects:

  • Within this role, 5/12 tasks have a high potential for redeployment
  • Through partial and full redeployment across 7 tasks, there is clear opportunity to release an average of 37% capacity per job by leveraging AI, RPA and task redistribution opportunities
  • This capacity release equates to an opportunity for approximately €33,300 of cost savings per job

How to get started

Answer these questions and then take action.
  1. How accurate is your measurement of AI adoption?
    Action: Measure actual usage (by role, level, and function) and capture the tasks AI is used for, how often it is used, and which AI tools are employed. Then compare your findings to leadership assumptions. If these are misaligned, further work is needed on accurate measurement.
  2. Are your skill development efforts focused solely on AI literacy?
    Action: AI literacy should be treated as a foundational skill, but it is only one part of the skills picture. For entry-level roles in particular, other capabilities (such as critical thinking) will become increasingly sought after. Ensure that the current design of work in your organization provides opportunities to build such key skills. Protect skills development time and measure progress using our skills assessment approach.
  3. Does your work and organization design enable optimal human-machine teaming?
    Action: Examine the tasks within your jobs and workflows and determine the level of human-machine collaboration required. Redesign work with this in mind and consider what your findings mean for the shape, size, and skills composition of the teams within your organization. 
  4. Do you plan with today’s or tomorrow’s skills in mind?
    Action: Strategically plan skills needs and embed skills development into your talent governance framework. Having the right skills development support in place will mean that early-career talent will have the potential to perform. It will also avoid you reverting to “experienced hire” criteria in situations where the work has already changed.
  5. Does your rewards strategy need a reset?
    Action: Audit whether early-career employees are taking on higher-level work without receiving aligned rewards and recognition. Reset your rewards program using bonuses linked to verified skills acquisition, or recognition tied to outcomes delivered. This will avoid tensions linked to slower title progression or flatter structures. 
About the author(s)
Paul Habgood

Partner, Global Skills Solutions Lead

Rebecca Jordan

Principal & UK Op Model & Org Design Lead, Workforce Transformation

Frankie Watkinson

Manager & UK SWP Lead, Workforce Transformation

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