AI is likely to impact careers. How can organizations help build a resilient early career workforce?

Anxiety beneath the surface

Evidence suggests that the number of entry-level roles is declining,¹¹ mainly because organizations are increasing their requirements for the number of years of work experience for junior-level roles. In sectors such as cybersecurity, for instance, where AI is already playing an increasingly bigger role,¹² it is not uncommon to see entry-level analyst positions demanding at least four years of work experience.¹³

Early career workers are also more likely than their tenured colleagues to express concerns about AI’s impact on their learning and development opportunities, in addition to increased workload and job displacement (figure 2). One analyst notes that opportunities for on-the-job training and experiential learning have been declining since the COVID-19 pandemic pushed workers into remote settings.¹⁴ These opportunities to learn foundational skills and experiences (for example, preparing reports, analyzing simple data sets, and note-taking in meetings) may be further restricted with AI automating such tasks. In the past, new hires gained experience and confidence by gradually taking on responsibility in a supportive, task-driven environment. With AI’s ability to perform many of these foundational tasks, early career workers may find themselves advancing to more complex work without the same safety net, leading to gaps in skill development.

Junior analysts in investment banking, for example, typically learn the higher-order skill of evaluating risk in new investments through foundational tasks like gathering economic and financial trends data and updating charts in pitch decks or company valuation comparison tables with the guidance and coaching of more senior investment bankers. But as AI takes over these tasks, analysts may transform from being data gatherers to being checkers of the data that AI gathers.¹⁵ Without the domain knowledge gained through foundational tasks and the ability to learn from senior leaders in a safe environment, how will entry-level workers learn how to effectively check the AI outputs or even know what to ask the AI for?

One early career worker we interviewed worried that AI may eventually replace the very skills in which they felt the strongest and that efficiency may cost them capabilities they don’t want to lose. “I’ve always been a super strong writer. But seeing how just giving an AI tool some rough notes and asking it to clean them up … how good the tool is at that ... I could see it stopping me from writing an email out in full and just defaulting to AI.”¹⁶ This echoes the perspective of another early career worker who said they are afraid they “have become dependent on it [AI].” Research has shown that as people’s dependency on AI tools increases, it can lead to skills atrophy in areas such as critical reading and writing skills,¹⁷ and even human capabilities such as creativity and critical thinking.¹⁸

These two concerns—fear of job being impacted by automation and decreased learning opportunities—may be contributing to a third anxiety: that even as early career workers have fewer opportunities to gain skills and experience, they are expected to perform at higher levels due to advancing capabilities of AI. Among those we surveyed, both early career workers (77%) and tenured workers (67%) believe that AI sets higher expectations for what workers should achieve in their early-level roles, including performing more complex and strategic tasks.

Despite these high expectations, many of the workers we surveyed, regardless of their experience level, said that the current AI tools have limitations that make it difficult to use them effectively in their daily work. While gen AI has reduced the amount of time workers spend on certain tasks, they still have to spend time confirming whether the task is done properly or the information is correct. Quality issues were one of several concerns that both early career workers and tenured workers flagged. Others include ethical and privacy concerns, fewer chances to collaborate with others, and a perceived loss of personal touch in the workplace (figure 3).

These findings are in line with reports suggesting discrepancies between leaders’ expectations for productivity gains and what workers say they can actually do with current AI tools. According to a recent report,¹⁹ while 96% of C-suite leaders believe AI will boost productivity, 77% of employees using AI tools report increased workloads and nearly 50% are unsure how to leverage these tools to achieve the expected productivity gains.

How early career workers are adapting to AI impact

Despite challenges, early career workers are not passive in the face of AI disruption. Many are showing adaptability and are proactively seeking new ways to develop the skills, experience, and careers necessary for an AI-enhanced future. Specifically, a significant percentage mentioned they are interested in developing technical as well as nontechnical skills and upskilling with professional qualifications (figure 4).

When asked how they could adapt to the potential impact of AI on their role, early career workers also seem much more open to nontraditional work when compared with tenured workers. For instance, 32% of surveyed early career workers say they are considering starting their own business or becoming self-employed, and 30% are interested in creating a new career for themselves that does not currently exist.  While AI is already transforming the job market, we’re only beginning to grasp the potential it holds for creating entirely new roles. Early career workers already seem to be considering these new opportunities, proactively seeking ways to future-proof their skill sets.

Similarly, around 25% of early career workers say they may become full-time contractors or transition to a less tech-dependent industry or field to minimize AI’s impact. This is something that has emerged as a recent trend among the younger generations, with Generation Z workers increasingly turning to blue-collar jobs involving hands-on work that may be less susceptible to automation.²⁰ A growing number of early career workers are also embracing an entrepreneurial mindset, looking for opportunities to become more independent, have greater job security, and create a faster path to high potential earnings.²¹ This outlook could be linked to generational trends. Growing up amidst the rise of the “creator economy” and social media influencers, younger generations may be more inclined toward entrepreneurial paths. Still, as younger workers shift away from work offered by white-collar and knowledge-based organizations, this trend could exacerbate an already shrinking workforce making talent acquisition and retention for these organizations increasingly difficult.²²

There are also notable differences in where early career workers and tenured workers turn for help with career development questions (figure 5). While roughly 50% of both groups look toward a manager for support, early career workers are less likely than tenured workers to look to colleagues for help with career development (47% versus 54%) but more likely to consult social media than tenured workers (46% versus 33%). Both groups are also struggling to find either formal or informal mentors at work, with fewer than one in three early career workers and fewer than one in four tenured workers reporting they have received guidance from mentors.

With regard to upskilling, early career workers prioritize developing both technical and nontechnical skills, whereas tenured professionals are more focused on developing their leadership skills and their AI fluency (figure 6). This divergence likely stems from their respective comfort levels with technology. Early career workers, generally more familiar with emerging technologies, seek to complement their technical proficiency with nontechnical skills. Conversely, tenured professionals, whose roles are likely more focused on people management tasks, prioritize developing the leadership acumen and AI literacy needed to effectively integrate these tools into existing workflows and guide their teams through this technological shift.

Notably, early career workers are also more likely to say they are developing nontechnical skills that cannot be replaced by AI, including communication skills, teamwork, emotional intelligence, and ethical reasoning. These findings suggest that early career workers may be interested in approaches to skill development that include not only technical and AI skills, but also human capabilities like the ability to collaborate, practice emotional intelligence, and think creatively. These capabilities can not only provide a foundation for ongoing learning and development, but also the critical skills needed to advance and take on meaningful roles in an age in which AI continues to take on more of the functional and technical sides of work. Research suggests that early career workers just joining the workforce often lack such capabilities so it makes sense that they place a premium on developing them.²³

Opportunities for building early career agility

The potential of AI and other transformational technologies cannot be realized without a workforce that sees opportunity in these AI-driven transformations. Early career workers may be a particularly important workforce segment as those surveyed are simultaneously more excited about these technologies and more concerned about the potential impact of these tools on their own career paths and opportunities. To leverage the capabilities of early career workers while addressing their anxieties, organizations may need to adopt a holistic approach: creating opportunities for early career workers to accelerate growth, prioritizing apprenticeship and mentorship opportunities, cultivating both technical skills and human capabilities, and embracing nontraditional career paths.

• Use AI tools strategically to create space for accelerated learning and career growth
  
  As our study shows, early career workers are excited by the opportunities to adapt and take advantage of emerging technologies but also see these tools potentially impacting their current work and developmental opportunities. This group of workers is also highly motivated to develop into leadership roles and engage in more meaningful work: Among those we surveyed, 45% of early career workers want to advance to a leadership position as a long-term career goal and 55% want to make an impact through their work and have a fulfilling job.
  
  As such, organizations can benefit by using AI to replace routine tasks of early career workers while simultaneously creating pathways for those workers to accelerate their learning and take on added responsibilities more quickly. Recent research suggests that gen AI tools provide the greatest benefit for workers with less experience, in terms of increased productivity as well as improved quality of work.²⁶
  
  These findings suggest that there are significant potential benefits for both workers and organizations in augmenting the work of early career workers with AI and other emerging technologies. Early career workers who adopt AI can gain opportunities to work on tougher, higher visibility problems while also identifying new ways to use AI to improve the work of the organization. For instance, law firms have been experimenting with using AI tools to enable junior lawyers to engage in more complex legal work (for example, reviewing complex contracts) that would have previously been assigned to attorneys with several years of experience.²⁷ This can help junior lawyers to develop faster and the firm to make their own work more efficient.

• Prioritize apprenticeship and mentorship opportunities to strengthen development across job levels
  
  Leaders should strike a balance between the near-term efficiency of AI tools and providing learning opportunities for early career workers. While routine tasks such as summarizing meeting notes can be outsourced to technology, early career workers may still need help developing other important skills required to advance to the next level. Organizations may also want to consider “moments that matter” to development when working with these technologies. For instance, there are still important learning opportunities involved with synthesizing and extracting insights from generated notes that may be important to consider in helping early career workers develop.
  
  Prioritizing on-the-job learning through apprenticeship models and investing in mentorship can help bridge this gap and encourage further development, especially considering that only a third of early career workers in our study say they have a mentor at work to help them navigate their careers. Apprenticeships can offer a more structured approach to day-to-day learning, offering early career workers the opportunity to gain practical work experience in a safe setting, while encouraging knowledge-sharing. By pairing early career workers with more experienced workers to learn new technologies together, organizations can also foster important learning experiences, often helping early career workers learn not just about the technologies, but also about other aspects of the role as they integrate the technology into their day-to-day work. For example, research found that while robotic surgery enabled senior physicians to work more efficiently, this efficiency often came at the cost of significantly reduced training opportunities for residents, resulting in longer-term skills gaps.²⁸ But when junior and senior physicians worked together to collaborate on the use of the robotic technology, both groups benefitted and residents were able to more effectively learn the skills they need to be independent physicians. As early career workers can be more fluent in the use of AI tools, they may be well-suited to provide reverse mentoring to more tenured workers about how to effectively use AI and other emerging technologies.

• Intentionally cultivate curiosity, imagination, and other human capabilities
  
  “Something I have done throughout my days is to set aside just 10 to 15 minutes a day just to sit with nothing, with no technology. To just think through some things, just to keep flexing that brain muscle,”²⁹ says one early career worker we interviewed, who is a heavy AI user. This is one example of how workers may try to simultaneously stay on top of new advances in technology while also maintaining space to disconnect, reflect, and strengthen human capabilities such as critical thinking and imagination. This is in line with our survey findings showing early career workers are often focused on developing nontechnical skills such as communication, teamwork, and emotional intelligence. Such capabilities may be at a premium in the age of AI,³⁰ as advances in technology enable organizations to continue to automate functional and technical aspects of many kinds of work. By helping workers develop imagination, problem-solving, and other human capabilities, they can also learn to effectively leverage AI to craft more insightful prompts and to take a more critical lens to evaluating the quality of AI-generated work.
  
  Paradoxically, these capabilities may become more difficult to develop as AI advances. A recent study found that roughly three-quarters of workers say they are feeling pressured to increase production due to the rise of AI,³¹ which may inadvertently incentivize busy work rather than work that contributes the most value to the organization through the exercise of capabilities like problem-solving or creativity.³² In addition to taking simple steps—such as enabling workers to make space in their schedules to disconnect and reflect—organizations can focus on communicating the value of human capabilities and their connection to organizational outcomes. The Talent Development program at Walt Disney Animation Studios, for instance, trains upcoming artists with the support and mentorship of seasoned Disney Animation professionals.³³ This initiative promotes creativity through ongoing exploration, innovation, and collaboration within a real-time mentor-mentee framework. By empowering early career workers to question established practices, they are placing greater focus on creativity, which benefits not only workers and their professional development, but the organization itself.
  
  
• Support nontraditional career paths to attract the best early career talent
  
  To attract and retain top early career talent, C-suite leaders can consider actively supporting nonlinear career growth. Our research indicates a growing desire among early career workers for alternative work models, including gig work and starting a new business. This desire aligns with the increasing prevalence of workforce ecosystems, where organizations recognize the value of nontraditional workers. For instance, in a recent Deloitte survey of nearly 5,000 executives, 87% said they consider their workforce to include other kinds of workers, such as gig and long-term workers, as part of a larger workforce ecosystem.³⁴ These ecosystems can be particularly beneficial to workers as well as the organization when executive leadership has bought into the idea of encouraging flexible approaches to talent development.
  
  One approach to facilitating nontraditional development paths is through internal skills marketplaces. For example, banking and financial services organization HSBC uses an internal talent marketplace that encourages networking and part-time project opportunities by connecting colleagues based on the skills individuals have and want to develop rather than their current job role, grade, or business area.³⁵ These kinds of approaches can be applied to external workers and can be especially helpful in recruiting early career workers with nontraditional backgrounds who may have the skills or experience needed to do a job. For instance, Google demonstrated an innovative approach to recruiting by identifying individuals with an interest in coding and inviting them to online coding challenges on a special platform, fast-tracking promising candidates directly into their recruitment pipeline.³⁶ By tapping into online communities and engaging with potential candidates through interactive experiences, companies can uncover high-potential individuals across different backgrounds and gain a competitive edge in attracting top talent.

As organizations continue to invest in AI-powered transformation and growth, they will need to rely on the workforce to bring their plans to life. While these transformations are likely to require both tenured and early career workers, our research suggests that early career workers could be particularly important in helping strengthen these transformation efforts. Not only are they more excited by the opportunities afforded by AI, but they also view themselves as more familiar with using these tools to get work done. Furthermore, they represent a critical pipeline of future leaders who can play a pivotal role in shaping these transformations.

For organizations, engaging these early career workers while addressing concerns about the potential impact of AI will likely include identifying workforce strategy opportunities that help workers forge new kinds of career paths and learning opportunities that strengthen their engagement and involvement in work. By prioritizing accelerated growth opportunities, empowering early career workers to hone uniquely human capabilities, and fostering robust learning and mentorship programs, organizations can appeal to this important group of workers in ways that can help them develop and stay relevant in the age of AI, while also enhancing the organization’s larger growth and transformation goals.