With relatively recent pop culture examples of how AI has been imagined to work with humans—like HAL maintaining the ship’s operations in “2001: A Space Odyssey,” and AI-driven androids taking on dangerous work in “Blade Runner”—sci-fi has painted a colorful canvas of the topic over many decades, albeit possibly starting quite late in the AI genre with “Star Trek” in the 1960s.
It is easy to simplify the impact of AI—specifically ChatGPT or other generative AI, in combination referred to as “AI” in this article—on the future of work in a way that can dilute such considerations, given significant variations in impact that depend on the sector, the roles and even the nature of the individual in question. That said, there is a general pattern in a career lifecycle that can usefully guide understanding the impact of AI on the nature of work.
Early Career Employees
Early career employees are adaptable and open to learning new skills, given their relatively limited work experiences. Their strength, however, is having grown up comfortable with digital technology that enables their strong digital literacy and the speed with which they acquire new technical skills.
Opportunities
Early career work is generally about “following the process.” In this context, AI-powered tools can help junior employees to automate and streamline the processes they become responsible for, allowing them to focus on the kind of value-added work once reserved for mid-career employees.
Assuming good organizational leadership and mentorship, this would enable even junior employees to help with the digital transformation of an organization’s processes to benefit the productivity of the entire organization. Early career employees who use AI technologies in this way can differentiate themselves and position themselves for higher-level roles and responsibilities more rapidly than a traditional path might have enabled them to do.
Challenges
However, ambitious career entrants should be careful about their early job choices. Much has been written on the future of work, and much has been written on the kinds of jobs that are growing and the kinds of jobs that are in decline. What remains is for the inquisitive candidate to digest this information to understand the job landscape when choosing a direction and ensure that the path taken is not likely to be a dead end. The key challenges are to learn about the skills and knowledge that complement AI and to realize that keeping up with advances is going to be a greater requirement for career success than it ever was.
Summary
Early career employees have the potential to fast-track their careers if they take the opportunity to adopt AI as part of their responsibilities in the interests of further enabling the overall digital transformation of their organization. Keep alert for jobs that are in decline.
Mid-Career Employees
Mid-career employees have practical, hands-on knowledge gained from years of applying the skills learned during their schooling. They have a broader perspective of the place of their skills and experiences in their organization, and they find solutions to complex operational problems in the broader context of the organization’s overall goals and strategies.
Opportunities
Mid-career employees could invest in learning new skills or deepening their existing AI expertise in such a way that their work integrates more seamlessly with AI systems. This is a form of process optimization like that of early career employees, albeit the emphasis is on strategic initiatives and on complex problem-solving at this level rather than on process automation.
AI can also provide such employees with additional insights and analysis for management decision-making, helping them to increase their effectiveness, especially amid low levels of information. Leveraging universal past experiences is a strength of AI that would help give mid-career employees confidence in their decisions under high levels of uncertainty.
Challenges
However, an organization’s operating environment changes over time. Roles that were once valuable become redundant, and new roles emerge to take advantage of changing market opportunities. AI will also continue to develop, making more process-type jobs redundant, impacting the need for mid-career employees to readily adapt to new responsibilities and requirements to stay relevant. AI may also threaten higher-level jobs: much middle management work—like approvals (rubber stamping) and managing exceptions—already falls into the realm of AI’s capabilities.
Summary
While mid-career employees also have the potential to digitally transform the processes they are involved in, they have the more powerful potential to adopt AI as part of their work to add deeper insights for operational and strategic decision-making, especially in areas where local experience and/or supporting information is in short supply. However, be careful about assuming that management roles cannot be performed by AI.
Late Career Employees
Late career employees have accumulated wisdom over a wide variety of domains and/or as deep specialists, as well as gaining deep institutional knowledge in the case of long-standing employees. Given their experience, they intuitively understand the opportunity and risk impacts of changes to an organization’s strategy, policies or processes. They have also learned resilience, no doubt, after having faced numerous organizational change initiatives during their careers.
Opportunities
AI is a learning tool, and it can be used to capture the experiences of late-career employees to retain and use their institutional, specialist and wide-domain knowledge, creating a sustainable instrument for knowledge transfer to early and mid-career employees.
Increasing levels of AI automation can move late career employees onto value-adding employment of a different kind: that of mentoring younger employees, or consulting into the organization as part of future organizational change initiatives.
Challenges
If their roles are AI-automatable, or if their roles change totally due to changing market dynamics, late career employees may face redundancy challenges if they don’t pursue additional training and support to keep themselves relevant. Furthermore, adapting to new technologies with more flexible user interfaces than they are used to is a challenge late career employees must overcome to maintain their currency in an increasingly AI-enabled work environment.
Summary
The modern workplace’s great tragedy is that so much experience is lost when employees retire. AI provides opportunities to capture this knowledge and to make it available to current and future employees. This ensures that instead of time being wasted with lessons having to be relearned, that learning can be accelerated by building upon a growing base of recorded institutional knowledge, bringing to life the knowledge management dream of decades past. Retirement should be a time to reflect on great work done, and AI provides a good opportunity to record and to build on the legacy of late career employees.
AI’s Impact Across All Career Stages
The impact of AI on employees at every stage of their careers involves a combination of opportunities for growth and cautions with respect to the changing world of work. Lifelong learning, upskilling and a willingness to embrace new technologies are more important than ever at all career stages in an AI-driven world. It is rare that an emerging technology is a shining light for late career employees, but AI provides an extraordinary opportunity for both employers and employees in this respect, offering the kind of on-the-job support sci-fi imagined, but that we could once only dream about. The remaining question is whether senior organizational leadership is ready and willing to make all this happen.