AI in the workplace is forcing younger tech workers to rethink their career paths

Entry-level workers increasingly fear job loss or significant changes to their careers due to automation made possible by generative AI (genAI). Nearly one-in-four “early career” employees (24%) believe their job could be replaced by automation, according to survey results from professional services firm Deloitte.

Early-career workers (those with five years or less on the job) are more likely than their senior colleagues to voice concerns about AI’s impact on learning opportunities, workload, and job security. The increasingly unsettled view of the workplace as AI tools advance echoes similar sentiments from other recent surveys.

Nearly four in 10 Americans, for instance, believe genAI could diminish the number of available jobs as it advances, according to a study released in October by the New York Federal Reserve Bank. And the World Economic Forum’s Jobs Initiative study found that close to half (44%) of worker skills will be disrupted in the next five years — and 40% of tasks will be affected by the use of genAI tools and the large language models (LLMs) that underpin them.

The Deloitte results highlight younger workers’ growing anxiety around AI replacing jobs — and the actions they’re taking to improve their own job security. Deloitte’s survey of 1,874 full- and part-time workers from the US, Canada, India, and Australia — roughly two-thirds of whom are early career workers — found that 34% are pursuing a professional qualification or certification courses, 32% are starting their own businesses or becoming self-employed, and 28% are even adding part-time contractor or gig work to supplement their income.

Deloitte

The tech market is already showing signs of a decline in entry-level roles, as organizations increasingly require more years of experience, even for junior positions. In fields such as cybersecurity, where AI is rapidly advancing, entry-level analyst roles often demand at least four years of experience, according to Deloitte.

Workers’ fears are not misplaced. The rapid advance of AI could lead to significant job displacement: Goldman Sachs, for example, predicts that AI could displace 300 million full-time jobs globally, affecting up to two-thirds of jobs in Europe and the US. Similarly, McKinsey Global Institute estimates that 12 million people might actually need to change professions by 2030. And a study published by the European Student Think Tank found that AI and machine learning automation, particularly in roles traditionally filled by young workers, such as data entry, could reduce job availability.

The Think Tank also found that the arrival of AI technologies could be creating a skills mismatch, as automation increases demand for non-routine analytical and interpersonal skills. Without proper training in AI-related fields, young workers might struggle to adapt, leading to unemployment and exacerbating labor market inequalities.

Despite job security fears, Deloitte found many workers remain hopeful that genAI tools can take over more mundane, repetitive tasks, allowing them to focus on more interesting and creative roles. Even as the technology promises to disrupt past patterns, 79% of younger employees are excited about AI’s potential, compared to 66% of older workers. Similarly, more early-career workers (78%) view AI skills as essential, even in non-tech fields. Just 62% of older workers felt that way.

Additionally, 75% of early-career employees believe AI will create new job opportunities in their field (compared to 58% of tenured workers), and 77% think AI will help them advance their careers (versus 56% for older employees).

“It’s difficult to generalize, but most studies show employees vacillating between fear and excitement,” said Arthur O’Connor, academic director of the City University of New York’s School (CUNY) of Professional Studies. O’Connor, who wrote a book on AI in the workplace titled “Organizing for Generative AI and the Productivity Revolution,” disagreed with Deloitte’s findings that concern about genAI is slanted toward younger workers.

“The exposure seems to be a function of what type of job you have, not how long you’ve been doing it,” O’Connor said. “But there is evidence to suggest that jobs that are most threatened may be senior-level, as research studies show…that genAI offers disproportionate benefits [to] junior-level employees over senior-level employees.”

Anyone in knowledge work — creating, summarizing, or visualizing content such as writing, coding, analyzing, or illustrating — should explore and learn to use the many genAI tools and platforms available, O’Connor argued. “With the evolution of intelligent agents, these tools are becoming increasingly sophisticated, with very powerful analytic and integrative capabilities.”

An uneven playing field

One study on software development found that less experienced coders using Microsoft’s Copilot improved the most, completing tasks 56% faster than the control group, O’Connor said. Similarly, a study on customer service showed that AI-assisted agents increased productivity by 14%, with the greatest benefits going to novices and low-skilled workers.

The job market, often referred to as being in a “white-collar recession,” could already be reflecting that shift. A 2023 LinkedIn study showed significant declines in hiring for high-paying roles since 2018: IT jobs (down 27%), quality assurance (off 32%), product management (a drop of 23%), program/project management (down 25%), and engineering (down 26%).

There is a skills transformation occurring right now, but how fast or pervasive it will be remains to be seen, according to Peter Miscovich, global future of work leader at Jones Lang LaSalle IP (JLL), a commercial real estate and investment management services firm. “Clearly 70% of the workforce will need to be upskilled in terms of genAI and AI. Whether you’re an entry level, mid-level or senior employee, we’re seeing a lot of focus on upskilling,” Miscovich said.

Early career workers are more concerned than tenured colleagues about AI’s impact on learning opportunities, workload, and job security. By 2027, genAI will likely be well on its journey of being orchestrated across workplace processes and embedded into workflows. As that’s happening now, enterprises are also grappling with re-evaluating existing processes, such as data mining and analytics as well as employee upskilling.

At the enterprise level, Miscovich said, from 50% to 75% of enterprises are already piloting genAI tools.

Fear of job disruption from automation and reduced learning opportunities may be fueling a third anxiety: early-career workers face fewer chances to build skills but are expected to perform at higher levels due to AI advances. Among surveyed workers, 77% of early-career and 67% of tenured workers believe AI raises expectations for entry-level roles, including handling more complex and strategic tasks, according to Deloitte.

The prevailing career advice seems to be, ‘Gen AI may not replace you, but others using it will,’” O’Connor said.

Getting workers up to speed on genAI

Many workers, regardless of experience, find current AI tools challenging to use effectively. While genAI tools can reduce time spent on some tasks, workers still need to verify accuracy and quality, which remains a significant concern. Other issues flagged by survey respondents include ethical and privacy challenges, reduced collaboration, and a perceived loss of personal connection in the workplace.

One issue for companies is getting new employees involved in inclusive training and encouraging them to be mentored and sponsored by mid-level and senior level manager, according to Miscovich.

Organizational training models, where skills-focused outcomes intentionally engage younger workers through microlearning and immersive spatial computing, are increasingly crucial for onboarding and training, helping workers feel supported, included, and developed, Miscovich said.

“This is a key challenge for many organizations, especially in the wake of workforce shifts during and after the pandemic. The Great Resignation has highlighted the need for greater guidance across teams, not just in adopting generative AI,” he said. “From a personal experience, it’s vital to invest in intentional, one-on-one time with younger team members to keep them engaged and continuously learning. Programs must also evolve to keep pace with rapidly advancing technologies.”

As AI automation infiltrates workflows, basic tasks like reporting and data analysis might limit early career workers’ opportunities to build skills gradually. Without these experiences, they risk advancing to complex tasks without a supportive learning environment, leading to skill gaps, according to Deloitte.

Miscovich pointed to research from PricewaterhouseCoopers, McKinsey & Co., and the World Economic Forum that shows most workers becoming comfortable with genAI. For example, last year, accounting and consulting PricewaterhouseCoopers announced it was spending $1 billion on expanding AI products and training for its 75,000 workers.

A major obstacle to organizational adoption is that data science expertise is not sufficiently diffused — for both senior managers and among rank-and-file workers, O’Connor said. “Such expertise tends to be concentrated in IT departments, most of which still operate as secret guilds with their own mysterious language and practices that are organizationally and functionally isolated from core business units.

“The most direct way would be to teach employees how to leverage the tremendous potential, as well as manage the considerable risks, of applying current AI tools in their everyday workflows. But this is a lot easier said than done, as most organizations aren’t currently staffed or structured to do this,” he said.

The European Student Think Tank recommends companies develop inclusive education and training programs to help employees adapt to rapid AI use. In particular, the organization recommends:

• Regular training that emphasizes promoting STEM education to boost employability in AI-related areas — especially for low-income youth who often lack access to such opportunities to ensure programs are accessible and inclusive.
• Employee involvement in decisions about AI implementation so workers better understand which tasks can be effectively automated. AI-related decisions should be made inclusively and transparently to align with employees’ insights and needs.
• Investments in AI research and development to foster innovation that enhances job automation without overshadowing human contributions. Research should prioritize streamlining tasks and integrating workers’ insights to empower young employees.
• Better collaboration among governments, the private sector, and academia to address AI-related employment challenges through public-private partnerships.

For employees hoping to keep up with the evolution of AI and its affects on their careers, O’Connor said pointed to an abundance of free content and opportunities to learn about AI. The challenge lies in using those resources effectively.

“Teaching and empowering employees to move past the experimental stage to embed these technologies into core business processes requires multi-disciplinary roles, functions and organizational structures most businesses don’t currently have,” O’Connor said. “As a data scientist who studies organizational behavior, I believe the coming Productivity Revolution calls for new types of roles and functions, in which data expertise is not a distinct organizational unit but an interconnected discipline spanning almost every aspect of a business.”