4 Use-Cases of Machine Learning in Recruitment

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Automation technology has quite steadily seeped into almost every field and task today. Recruitment is one such area where Artificial Intelligence (AI), and its subset Machine Learning (ML) have a great potential that can optimize the hiring process altogether.

According to Glassdoor, in today’s times, any corporate job opening on an average attracts about 250 resumes from applicants. And among these huge piles of applications, picking the best suitable candidates for a role is a cumbersome task. AI & ML helps make it much simpler and convenient by automating the repetitive elements of the hiring process.

Read further to know about the significant use-cases of Machine Learning in recruitment in today’s time.

1. Job Advertising

Manually writing and placing the job adverts is one of the time taking activities for recruiters. This challenge gets simplified through machine learning technology.

ML automates the candidate sourcing process by sorting the resumes database to find those candidates who are the best fit for the open positions. Diving deep into the details, people who visit your website’s career page can be tracked with the help of machine learning algorithms.

Further, this data is used for explicitly targeting candidates through various social media platforms- they are active on. Additionally, AI helps in improving the overall experience of a candidate as it offers real-time feedback throughout the recruiting process. This helps reduce the candidate’s frustration associated with the lack of recruiter feedback.

2. CV Screening

Screening CV’s manually is a time-taking and tedious task to perform, especially in case of big recruitment drives. All thanks to ML, resume screening can be automated to help in identifying specific traits, experience, and skills that signify whether a candidate would be a good fit for the job profile at a company.

This encourages unbiased resume screening and shortlisting that is solely data-driven and not based on the personal opinion of the HR recruiters.

3. Candidate Assessment & Pre-selection

There exists a bunch of useful tools for pre-employment assessment that facilitate a comparison of the critical abilities of the candidates. This helps in deciding the most suitable match for the job profile.

Among such tools, the one which is AI-driven help recruiters to predict the qualitative effectiveness of candidates, by utilizing machine learning algorithms and applicant data.

Apart from this, AI and ML help in measuring the prospective employee’s soft skills, aptitude, and culture fit, and other abilities that are requisite for the said role he/she has applied for.

4. Predicting Hiring Needs

Employees may head for early retirements or simply quit to look out for better opportunities. However, this may adversely impact the responsibilities and work they leave behind. For such scenarios, smart succession planning is extremely crucial for the continued sustenance and growth of the organization.

Machine learning algorithms can be trained and put to use for keeping information about the talent pool in the pipeline. In fact, some effective AI platforms even suggest a rough expenditure and time that may be involved in hiring fresh talent.

To Wrap Up

Undoubtedly, machine learning-powered recruitment applications are numerous. As a whole, using AI and ML in recruitment, simplifies the process, makes it unbiased, minimizes cost, improves candidate’s interview feedback, and helps source the best talent for the company.

Eyeing such great benefits, several organizations are switching their recruitment process from manual to machine learning algorithm-based.

If you are looking to leverage AI and ML-driven recruitment solutions, please feel free to reach out to us.

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