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Three Ways Machine Learning Has Changed the Recruitment Process Forever

Recruitment Process

Over the years and with the ever-growing businesses, recruiting new talents has been a time consuming and tiresome exercise. Talk about the sift through a sea of resumes with an aim of finding the right candidate for a vacancy in your business. Isn’t it discouraging? More so if none of them gives an indication of a potential match for the job. Well, not anymore. Thanks to machine learning, an AI technology driving force that extracts and analyses information and use it to make a more correct prediction. Employers nowadays apply this technology in their recruitment process and it has forever changed the process. Wondering how? Here are three critical ways.

Photo by Tim Mossholder on Unsplash

“From professional networking sites and job boards to online applicant systems, technology has revolutionized recruitment, profoundly changing how employers and recruiters find potential candidates.” – CIO.com

Fast evaluation and recruitment of candidates

The algorithms enable the HR to effectively identify and recruit the right candidates from the vast majority. The labor- and time-intensive tasks that could otherwise be stressful to the human resource departments such as resume screening and interview scheduling have been accomplished through machine learning. The pattern recognition and statistical analysis have made it easier to determine whether a candidate has the required skills and experience. Impressive, right? If you’re running a development company, having a scalable process for hiring developers will put you in front of your competitors not using the same process. 

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Bias elimination and employee satisfaction

Humans are inherently biased. A number of companies have had their reputation affected by claims that the process applied in recruiting new talents was biased. With the old method, you probably would’ve experienced some biases from management during recruitment. Your gender, background, or the university you graduated from could play a part. But machine learning algorithm focuses on skilled-based data regardless of your pedigree. Each potential candidate has equal exposure to available opportunities based on their career. You will get to be Informed of the salary data matching your skills and roles to play. And if you’re intending to recruit, machine learning can access the skills and experience of a candidate to determine the suitable salary offer. 

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Thanks to machine learning, an AI technology driving force that extracts and analyses information and use it to make a more correct prediction. Employers nowadays apply this technology in their recruitment process and it has forever changed the process.

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From expensive to a cheap exercise

The recruitment process is an expensive task right from advertisement to training of the selected candidates. Several adjustments and approaches have to be implemented. What if a few weeks or months down the line you realize the candidate(s) chosen lacks one or more crucial qualities? Perhaps you will incur more expenses looking for someone else to replace the victim. And this is not a good thing in terms of the growth of the business. But machine learning technology allows you to collect more accurate data for the potential candidate. The algorithms can analyze the candidate’s social media profiles and predict the attributes of the candidates. This way you will be able to avoid disappointments from the selected candidates.

Conclusion

The old recruitment process was a hectic process that could affect the overall success of any organization. But machine learning has made the process faster, easier, efficient, and perhaps cheaper. The algorithms are used to identify the best-fit candidates out of the thousands and millions interested. The previously time-consuming task can now be completed within a very short time, allowing time to concentrate on other important responsibilities. The search-based system has brought about satisfaction and trust by eliminating the previously experienced biases.

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