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Automation for Minimal Human Intervention


Automation is the technology used to perform a process with minimal human intervention. Automation is concerned with discovering methods of increasing productivity, reducing idle time, cutting waste, and improving product quality. Nowadays, producers in every industry are active users of various software systems, aiming at the efficiencies of substitution of manual tasks with machine-assisted ones. In this essay, central issues regarding the automation process will be discussed with the examination of the long-run perspectives.

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The main area of concern related to the increased use of technology appears to be persistent job losses as machines replace people. Cellan-Jones (2019) finds the skill level to be a considerable factor, where low-skilled labor is more unprotected from potential unemployment (para. 6). Indeed, a more repetitive and straightforward working process has higher chances to be reproduced by a robot, leading to lower production costs and enhanced productivity. Furthermore, Cellan-Jones (2019) implies a neutral outlook on the future conditions, stating that “jobs will be created at the rate they are destroyed” (para. 21). It means that the rising productivity level, as a result of extreme automation, can boost the GDP level, contributing to higher economic growth. Thus, jobs lost due to employees’ poor qualities can be offset by the increased economy growth.

Taking into account the scale of possible unemployment in the United States, it is evident that automation will lead to severe and irreversible consequences. Semuels (2017) offers numerical data, saying that more than half of Riverside’s current jobs will be automated by 2025 (para. 3). At the same time, computerization will lead to a fifty-five percent loss of employment in all major American cities (Samuels, 2017, para. 4). Undeniably, the perspective is not promising for millions of workers and will worsen with higher rates of automation.

It is clear that with the current pandemic situation and its strict social-distancing rules, the rate of automation has skyrocketed. Corkery and Gelles (2020) take an example of the recycling industry, which with the Covid-19, prevents people from going to work as they struggle to “find enough protective gear for all of their employees” (para. 24). What is more, it is expected to reduce future rehiring after the crisis since it caused a massive reduction in output (Corkery and Gelles, 2020, para. 18). Undoubtedly, loss in revenue makes producers cut on expenses by employing lower-cost robots. Thus, the pandemic meltdown is the reason for a rise in automation, which may contribute to further job losses.

The adaptation in the new automated reality is another problem to consider. The time-span between now and the fully-computerized world can be used to learn new skills to adjust to the situation. As Miller (2017) covers in her article, people should be taught alternatively by educating “creativity, critical thinking, emotional intelligence, adaptability and collaboration” (para. 6). Without question, these are the qualities machines cannot be trained to possess. Additionally, Miller (2017) provided a mostly positive future view as far as people will try to be flexible (para. 20). Hence, appropriate and adjustable education can let workers harmonize with technologies.


It can be concluded that job reductions, generated by boosted exploitation of technologies, seem to be common ground for all four articles. However, Cellan-Jones (2017) takes a fair-minded view, arguing that there will be zero effect on the employment level, while Semuels’s (2017) prospects are pessimistic, providing predicted information. Corkery and Gelles (2020) focus on the rise in automation due to the global pandemic with a detrimental impact on the labor demand level. Compared to the rest, Miller’s (2017) standpoint sounds the most positive because of the learning ability and unique talents only humans can have.


Cellan-Jones, R. (2017). Robots ‘to replace up to 20 million factory jobs’ by 2030. BBC News. Web.

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Corkery, M. & Gelles, D. (2020). Robots welcome to take over, as pandemic accelerates automation. The New York Times. Web.

Miller, C. C. (2017). How to prepare for an automated future. The New York Times. Web.

Samuels, A. (2017). The parts of America most susceptible to automation. No, they’re not in the Rust Belt. The Atlantic. Web.

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