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Machine learning is making the global workforce a reality

The idea of global conglomerates is not a new one – it’s as ancient as human history. For as long as we have had travel there have been people willing to offer banking services, transportation, import and exports, and other endeavors involving long-distance travel. 

Of course, the days of colonies and handwritten letters with wax seals are long gone. We now live in a world of emails, virtual offices, cloud computing, smart devices, and interconnectivity. The internet has changed society and shaped the economy in ways that no one could have predicted.

It’s amazing to think that 30 years ago international conglomerates only relied on fax machines and long-distance conference calls to communicate between branches, while nowadays the issue we are facing is an overabundance of information channels. 

It’s quickly becoming apparent that with the communication technology we currently have companies are no longer constrained by geographical limits. In fact, we’re already living the age of the global workforce.

What is a global workforce?

In the simplest of terms, a global workforce is an idea that a company, business, or group can have access to an international labor pool of workers connected through a global network of communication and production. 

If we take that definition at face value, it’s quite clear that the notion of a global workforce has existed for quite a while. In fact, the term actually came to the forefront in the 1970s with the rise of globalization. As a concept, the notion of a global workforce is not new, but the way we use it has changed dramatically in the last 50 years. 

Originally the term was used to reference the relationship between highly capitalistic societies and developing countries, in which corporations from the former would outsource the manufacturing process to the latter. This meant reduced production costs and a boost to foreign economies. 

But as communication technology advances so does the concept, and in the 21st century, it has begun to take another meaning: the idea that a single team can be composed of members from all across the globe. It’s no longer a division of labor but rather multicultural long-distance teams.

Multicultural teams

The idea of multicultural teams is often at odds with what we have been taught about efficiency. It’s a common belief that a team of like-minded individuals sharing an office and with a strong sense of identity will produce better results than a team that focuses on diversity.

It’s undeniable that multicultural teams pose a series of challenges, but once they are overcome, the end result is a much richer experience for everyone involved. Workers get to see the world from new perspectives and are presented with fresh ideas which lead to better discussions and better outcomes. To put it succinctly, diversity breeds creativity.

From a strategic point of view, hiring multicultural virtual teams can provide several benefits to a company. The first and most notable is access to a much wider talent pool. There are literally millions of candidates looking for work all across the world, some of them quite brilliant but unable to show it because of structural constraints such as specializing in a field that’s underdeveloped in their respective countries. 

On top of that, multicultural members can help companies find new niches in their respective countries. They can have lower target incomes than other candidates and have specialized knowledge in other fields that may come in handy. 

The recruitment problem 

The obvious problem is that a bigger talent pool means a longer hiring process. If 1 out of 100 candidates is the perfect person for the job, searching for that 1 in 1,000,000 can be grueling. Sure, you can find more candidates that fit your role among the increased number of candidates, but the search would still be hard to do. 

Keep in mind that the proportion might be the same, but the amount of work required to go over a million resumes is a lot more than for 100. In other words, the second issue is that by widening your talent pool you are also increasing the number of resumes that will not be a good fit for your company, so there is a lot more digging to do.

Finally, recruiting candidates from all across the world requires good infrastructure. A whiteboard and a phone might be enough for local hiring, but when you are dealing with international calls, different time zones, different languages, and different cultures, you need a system that can help you keep things organized.

Solving the issues with Machine Learning

For many companies, the prospect of global hiring might seem intimidating, and with good reason: there are plenty of challenges to overcome!. But, just like communications changed the way we understood the global workforce, machine learning has the potential to reshape the way we understand global hiring. 

There has been a growing interest in developing AI systems to aid with recruitment. The idea of having an intelligent algorithm that can quickly assess a pool of candidates to find the best fit for a company is enticing, to say the least. 

How does it work exactly? At its most basic, a machine learning algorithm takes data and makes a prediction about the fit. Depending on the real result the algorithm self-actualizes and “learns” to provide better predictions next time. For example, if the algorithm decided that candidate A was a better choice than candidate B, but it turned out to be the other way around, it takes this into account the next time it faces a similar problem. 

In other words, the algorithm improves automatically through the experience without the need for human intervention.

Let’s go back to the problems we faced with multicultural hiring:

  1. Having too many false positives (resumes that aren’t a good fit for the company)
  2. Having too many real positives (resumes that are a good fit for the company)
  3. Requiring a good infrastructure.

Problems A and B can both be solved with artificial intelligence. Imagine that we have an algorithm that assigns a score from 1 to 100 to every candidate, where 100 represents a perfect match for the job. Then the algorithm automatically discards those who don’t reach a certain threshold and outputs a list of potential candidates from the best match to the worst match.

In this way, the AI is acting as a sort of pre-screening process automatically disqualifying anyone who doesn’t meet certain criteria. Those who do are then organized to maximize the probability of finding the best candidate in the least amount of time.

With current trends in computing power, this process can take minutes at most. In other words, we can scan literally hundreds of resumes per second, a superpower any recruiter would want.

That leaves us with one question, though: what about problem C?

Outsourcing your recruitment

Opening the floodgates to millions of resumes from all across the globe requires a certain level of infrastructure that most businesses don’t want to deal with. Fortunately, there are many companies out there that have already gathered a pool of screened candidates ready to be recruited.

Think of these services as an intermediary between your company and potential recruits. They use AI-powered strategies to filter potential candidates and create a top-tier talent pool that is tailor suited to your company’s needs.

AI-assisted hiring might still have a few quirks that need to be ironed out, and as every good recruiter knows, instinct plays a huge part in the hiring process. The idea here isn’t to replace the human but to provide more tools so that recruiters can spend their valuable time interviewing and assessing people who are truly worth the effort.

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