Let's take a look at how AI and automation are affecting the hiring process and what this means for your career choices.
AI systems (or, more precisely, machine learning systems) have started being utilized in nearly all sectors of the business world. This has sparked interesting and important discussions about the effects of AI on the current and future workforce.
These conversations and the questions they conjure are reminiscent of conversations (and fears) that entered popular culture when automation began appearing in assembly lines and shaking up the manufacturing industry.
Even though the economic landscape and workplace environments have changed significantly since the rise of automation in manufacturing, the parallels with automation and AI in today's business environment are so striking that we have a track record of what to expect.
With a high degree of confidence, we can anticipate the effects the current iteration of automation (digital, AI-powered) is likely to have on the labor market.
When manufacturing plants began installing robotics and automated machines into the production lines (starting in the early 1960s and reaching into its heyday of the mid-1980s), many people in the US feared this would lead to mass layoffs and the loss of manufacturing jobs.
These fears turned out to be true, but not really.
The truth is that manufacturing jobs have been on a steady decline in the US since the peak of the industry in 1979.
In fact, the U.S. Bureau of Labor Statistics reports a 34-percent net loss in the last 40 years. But the reason is not automation. Instead, the loss of manufacturing jobs has more to do with a complex calculation of trade deficits offset by an influx of foreign capital, as explained by CSIS (Centre for Strategic & International Studies).
The loss of manufacturing jobs coincided with the rise of automation. But this is more of a case of correlation without causation – a coincidence, if you will.
If we step back and take a big-picture look at the labor market, a compelling case could be made that automation led to, not the loss of jobs, but to the creation of jobs – albeit of a different variety.
Undoubtedly, automation has contributed to a significant increase in manufacturing efficiency, which in turn has freed up the workforce to concentrate on innovation (which, in turn, is the greatest single factor contributing to economic growth).
Automation in the manufacturing industry has also had a significant positive effect on workplace safety. While, ultimately, this has no bearing on job creation vs. job loss, it's still a significant advantage worth pointing out.
On the other hand, the loss of jobs (due to a multitude of factors) and the threat of automation significantly reduced any negotiating leverage workers and labor unions might have enjoyed.
In consequence, over the last 40 years, the compensation offered for manufacturing jobs has not increased at the same rate as salaries in other sectors.
Since the loss of manufacturing jobs, we have seen very little c for their return. Instead, the majority of the workforce has embraced the push toward innovation and efficiency automation has spearheaded.
Currently, the US and the UK are in the midst of an unprecedented labor shortage. Some industries in the US specifically report an estimated two job offers needing to be filled for every one job seeker.
This labor shortage is further exacerbated by an unusually high quit rate. This tells us two things: firstly, that workers are finding jobs. (There are more job openings than job seekers.)
And secondly, they are not happy with the jobs they find. (They don't stay at the job long.)
What we have in today's labor market is not a lack of jobs, but a lack of meaningful jobs, jobs workers want to stay at and grow in.
Faced with this problem, more and more recruiters and HR professionals are turning to AI-powered job search engines, like Lensa, to match the right candidate with the right job opportunity.
The goal is to comb through the thousands (if not millions) of job seekers to find the select few that possess the background and skill sets that would make for ideal employees. And AI algorithms can do just that.
AI systems can analyze millions of resumes, and job offers nearly instantly. From these data points, the algorithms look for keywords and parallels to make matches. They can also detect patterns and even make predictions on the likelihood of a candidate's success at a given job.
Implementing AI in the hiring process has the added benefit of significantly reducing the chances of the process being corrupted by hiring bias – whether conscious or unconscious.
Instead, AI and machine learning algorithms allow recruiters and HR professionals to make data-driven decisions, increasing their chances of a successful hire and thus boosting employee retention and reducing the overall quit rate of their employees.
How to measure employee engagement has become a priority for HR professionals. The metrics used to measure employee engagement, as well as the initiatives taken to improve upon it, can all be improved in efficiency and effectiveness with AI.
This is especially true when tasked with successfully leading and managing a remote team.
Again, the problems of employee engagement and employee retention are problems that are better faced with data-driven decisions. And to put it simply, AI facilitates data-driven decisions.
Since 2021, workers in the US have been quitting their jobs at a record pace.
This phenomenon has been coined "The Great Resignation." Recent studies on why everyone is quitting their jobs suggest that the main reasons are poor relations with colleagues and bosses and an unhealthy, toxic, or unsupportive work environment. In essence, the reasons for employee dissatisfaction are interpersonal.
This shows us the importance of strong, healthy relations at work.
This is not something that can be easily fixed with algorithms and apps.
On the other hand, with HR professionals and team leaders relieved of many of their menial and repetitive tasks, they are now free to focus on these more complex interpersonal challenges that are contributing to this "Great Resignation."
When we take a look at the problem of joblessness in its current iteration (it is not a lack of jobs but rather a lack of meaningful jobs that people want), we see in automation a fantastic tool that, when used right, can mitigate the factors that lead to dissatisfaction with a job.
Automation is, above all, a tool that carries out the more menial and repetitive tasks of a given position. This ends up saving employees so many working hours that, for all intents and purposes, it virtually redefines the role of the employee. Now, the employee is freed up to focus on more complex tasks, especially those that require creativity.
When an employee expresses his or her creativity, they are expressing their identity, their own unique perspective on the world and on the task at hand. It is in this expression of creativity that they assert their individuality; they assert their identity.
Imagine a job where the employee is encouraged to express and assert their identity, where he or she is relieved of menial tasks and focuses on creative expression. To a vast number of people, this is the definition of the "dream job," and it is what automation inevitably leads to (to varying degrees of success).
It is clear that automation can contribute to reducing the factors that lead to joblessness.
Additionally, there are several benefits to implementing automation in the recruitment process that have already been shown to reduce joblessness.
While the benefits of automation are numerous and unimpeachable, we cannot expect AI and automation to solve all the problems we are currently facing in the labor market. Studies have repeatedly shown that the #1 cause for employee satisfaction remains the human factor: poor or toxic relations with colleagues and/or superiors.
Successful companies will be those which can strike the right balance and implement AI and automation, reap the many benefits while not neglecting the human factor that is essential to employee satisfaction.
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