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Automation of workers: How can human laborers stay relevant?

As automation becomes increasingly widespread over the decades ahead, human workers have to be retrained or else become unemployed.

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Quantumrun Foresight
November 6, 2023

Insight summary

Automation is shifting the dynamics of the labor market, with machines taking over routine tasks, thus pushing both educational institutions and the workforce to adapt to technological advancements. The rapid pace of automation, particularly in the fields of robotics and artificial intelligence, can lead to significant worker displacement, prompting a need for enhanced education and training programs tailored toward the jobs of the future. While this transition presents challenges, such as wage inequality and job displacement, it also opens doors for improved work-life balance, new career opportunities in tech-centric fields, and the potential for a more geographically distributed workforce.

Automation of workers context

Automation has been happening for centuries. However, it is only recently that machines have begun to replace human workers on a large scale due to the advancement of robotics and software technology. According to the World Economic Forum (WEF), in 2025, 85 million jobs will be lost globally in medium and large enterprises in 15 industries and 26 countries due to automation and a new division of labor between humans and machines.

The “new automation” of the next several decades—which will be far more sophisticated in robotics and artificial intelligence (AI)—will broaden the kinds of activities and professions that machines may execute. It can result in considerably more worker displacement and inequality than in previous generations of automation. This may have a greater impact on college graduates and professionals than it ever has before. In reality, emerging technologies will see millions of jobs disrupted and automated in part or in full, including vehicle drivers and retail employees, as well as those for healthcare workers, lawyers, accountants, and finance experts.

Innovations in education and training, job creation by employers, and employee wage supplements will all be advanced by their respective stakeholders. The greatest obstacle is enhancing the breadth and quality of education and training to complement AI. These include communication, complex analytical abilities, and innovation. K-12 and postsecondary schools must modify their curricula to do so. Nonetheless, workers, in general, are happy to hand over their repetitive tasks to AI. According to a 2021 Gartner survey, 70 percent of US workers are willing to work with AI, especially in data processing and digital tasks.

Disruptive impact

The transformative wave of automation is not an entirely bleak scenario. There's substantial evidence to suggest that workers have the capacity to adapt to this new era of automation. Historical instances of rapid technological advancements did not culminate in widespread unemployment, indicating a certain degree of workforce resilience and adaptability. Moreover, many workers who are displaced due to automation often find new employment, although sometimes at reduced wages. The creation of new jobs in the wake of automation is another silver lining; for instance, the rise of ATMs led to a decrease in the number of bank tellers, but simultaneously spurred a demand for customer service representatives and other support roles.

However, the unique pace and scale of contemporary automation pose significant challenges, especially during a time of sluggish economic growth and stagnant wages. This scenario sets the stage for escalating inequality where the dividends of automation are disproportionately accrued by those equipped with the necessary skills to leverage new technologies, leaving the average workers at a disadvantage. The diverging impacts of automation underscore the urgency for a well-orchestrated policy response to support workers through this transition. The cornerstone of such a response is bolstering education and training programs to equip workers with the skills required to navigate a technologically-driven labor market.

Transitional assistance emerges as a viable short-term measure to support workers adversely affected by automation. This assistance could include retraining programs or income support during the transitional phase to new employment. Some companies are already implementing upskilling programs to better prepare their workforce, such as telecom Verizon’s Skill Forward, which gives free technical and soft skills training to help the future workforce establish technology careers.

Implications of the automation of workers

Wider implications of the automation of workers may include:

An expansion of additional allowances and benefits for workers, including enhanced earned income tax credits, improved child care and paid leave, and wage insurance to mitigate wage losses attributed to automation.
The emergence of new educational and training programs, focusing on imparting skills pertinent to the future such as data analytics, coding, and effective interaction with machines and algorithms.
Governments imposing employment mandates on companies to ensure a specified percentage of work is allocated to human labor, fostering a balanced co-existence of human and automated labor.
A notable shift in career aspirations with more workers retraining and reskilling to venture into technology-centric fields, causing a new brain drain for other industries.
The rise of civil rights groups advocating against the widening wage inequality propelled by automation.
A shift in business models towards offering value-added services, as automation takes over routine tasks, enhancing customer experiences and generating new revenue streams.
The emergence of digital ethics as a crucial facet of corporate governance, addressing concerns around data privacy, algorithmic bias, and the responsible deployment of automation technologies.
A potential reconfiguration of demographic trends with urban areas possibly witnessing a population decline as automation renders geographical proximity to work less critical, promoting a more distributed population pattern.