Machine learning is giving computers the ability to learn without explicit programming. This is primarily accomplished through various pattern recognition processes. For example, if you tell a computer to find the best candidates for a job by identifying patterns in data that produce the best results. In this way, a computer will find correlations and patterns that a human would overlook — leading to increasingly higher quality candidates.

Beyond that, the possibilities are infinite! Imagine that we’re able to predict the crash of a certain market, and know that there will be a surplus of available candidates in that specific field, you can then focus your efforts as a recruiter to take advantage of that situation. The ability to predict upticks and downticks in a given market is a revolutionary new possibility thanks to machine learning. It would give recruiters a serious advantage over their competitors and would allow them to reach out and make connections much earlier in the recruiting process.

The biggest issue for recruiters right now is that we all have these massive networks but we haven’t really had an effective way to leverage those connections without committing a significant amount of time and resources. If you have 5,000 LinkedIn connections, what does that do except allow you to brag about it?

This is where machine learning comes into play. Recruiters can start to recognize pure data points of candidates’ contact information, their profile, their work history, etc. and be able to match those with opportunities. Machine learning does not automatically select the best candidate, instead it narrows the field of search and allows us to focus on analyzing the intangibles. From this, a stronger hire is made, leading to a greater R.O.I. as well as a high L.T.V. from each candidate.

Going deeper, on top of quickly sorting through all of this data, machine learning will be able to take a broader view of trends in specific industries and even specific job titles.

For example, machine learning could determine that a certain developer has been at their job for a year and a half and there is a, let’s say, 98% chance that they will leave their job in the next three months. That is a huge insight that can be determined very quickly with machine learning. There is an enormous R.O.I. for every singular effort that the recruiter makes. Time equals money, and machine learning will save recruiters unimaginable amounts of time.

This is sensibly applied technology, not the end of days. Anyone who claims they can create a full replacement for the human recruiter anytime in the immediate future isn’t being realistic. First of all, current technology is not refined enough to produce the results needed for a fully autonomous system. I also don’t think the demand for a fully automated recruiting experience exists at the moment. In fact, I can see some significant hurdles that such a product would face in the current market climate. There is a human element to the hiring process that is essential to both sides of the process. There are ways for technology to streamline and improve the hiring process, but at the end of the day, no one wants to feel like they are some sort of cog in a machine.

Consider this, Google is currently developing autonomous driving cars but have yet to even attempt to create an autonomous recruiting system. This is because, as easy it would be for a company like Google to develop an autonomous recruiting platform, the amount of time it would take to create compared to the extremely narrow application it would offer inhibit it from becoming a truly viable product. That being said, just because we can’t automate the entire recruiting process doesn’t mean that we can’t dramatically improve it, but it needs to develop over time. Cars could park themselves before they became fully autonomous. Automated recruiting will evolve in the same way. You must crawl before you can walk, and even though we are only in the crawling phase of machine learning applied recruiting, the possibilities are as thrilling as they are endless.