Over the course of the previous ten or so years, a few technical advancements have been compared to one another in an imaginary war over the direction of business. Machine learning is perhaps the one with the greatest potential for use in HR in the real world.
Machine learning is the idea that a machine (which is essentially a piece of software, though it makes use of significant advancements in computational power in modern hardware) “learns” over time by leveraging the results of its past computations and decisions to expedite and improve the quality of its future computations and decisions.
Unfortunately, the whole tale becomes muddied with false expectations and promises of things that will either never come to reality or that will take decades to show any useful potential, as is typically the case with new (at least in the form we see today) technology.
Because of this, it’s critical to distinguish between machine learning trends that may, and might not, have immediate use in human resources. The former is of significance to us now.
Employers utilize
Assume that a large company gets ten thousand resumes annually. Assume that they recruit a thousand people annually. Assume that 500 of them succeed and the other 500 fail.
Assume that this sizable company retains all of the information pertaining to these 1,000 hires and 10,000 applicants.
They maintain tabs on who saw the job posting where. They maintain a record of each applicant’s resume and devise a system for classifying all the information found in those applications. They also incorporate the social media activity of candidates into the information they collect and retain. They maintain a log of their systematic, regulated interviewing procedure. They monitor the vocabulary employed in the correspondence. Every piece of information pertaining to the applicants is retained by them.
From the first day onward, they continually feed all of this data into machine learning software. Eventually, specific patterns start to show
The program finds that successful hires come from a certain job ad website. Some interviewers are more adept than others in spotting the proper talent. Individuals who utilize a certain kind of social media are more productive workers. The options are virtually limitless, particularly when you take into account the combinations of distinct elements and patterns.
The only type of “entity” that has a chance of sifting through all of this data and identifying the patterns is software that makes use of machine learning. Something like this could never be accomplished by a human HR specialist. This is something that “traditional”, programmed HR software could never accomplish.
There are existing businesses that carry out this work and provide their clients this sort of study.
It should be noted, though, that after all is said and done, a human will need to make the ultimate choice because some of the patterns and tendencies will prove to be false positives. Having said that, employing sophisticated analysis and pattern recognition can significantly raise the hiring decision success rates.
Lastly, it’s important to note that a large number of HR professionals concur that the greatest approach to eliminate human bias from recruiting decisions and enhance them even more would be through machine learning and software built on this foundation.
Staff attrition
The actual consequences of high employee turnover have come to light in recent years, and organizations are making every effort to stem this talent leak. As a result, employee attrition and the ensuing staff turnover have become a contentious subject.
The issue is that, even in cases when internal communications inside the organization are handled correctly, it is not feasible to conduct a thorough study of the remarks, queries, intents, and choices made by individuals that might result in employee turnover. For a human HR specialist, it is, at the very least, impossible.
Nonetheless, certain patterns become recognizable for a machine learning-based piece of software. As an instance, specific answers on employee satisfaction surveys and declines in productivity might be seen as signs of staff attrition and resignation. These signals are many and frequently take on significance in certain combinations that are beyond the comprehension of the average human. This is already something that machine learning software can do—it can even go one step further—and the likelihood is that it will only become better at it.
The legendary marriage
For a time now, there has been a lot of agitation in the HR community around employee engagement. People are saying that employers are unable to stop their staff from quitting or, at the very least, from mailing it in, and that these are the Last Days of Engaged Employees.
Without a question, employee engagement is and will always be a human-to-human endeavor. When it comes to keeping staff engaged and keeping them from moving on to “greener pastures,” however, there is much to be gained from the astute application of machine learning and software that helps uncover trends.
Companies like Glint, whose software solutions give employers continuous insights into how their employees are feeling about their workplace and how engaged they are, are able to do so because of the growing usage of machine learning in natural language processing. This isn’t just comparing employee satisfaction surveys; it goes a step beyond. It is now feasible to get understanding from seemingly insignificant utterances that were previously thought to be too ambiguous or emotionally charged to be realistically analyzed, thanks to new machine learning-based software.
Closing Word While some technical advancements have enabled machine learning to make significant progress in the previous several years, it is reasonable to conclude that its full influence on the business and HR domains has not yet been fully realized.
It’s crucial to avoid being too pessimistic and viewing it as a sign of impending disaster. Human-machine cooperation will almost certainly play a role in HR in the future, and that may work to HR’s advantage.
