A decade ago, we considered recruitment to be an industry that could rise only with the help of humans. HR managers and the human resource department were solely responsible for hiring candidates. However, this trend is changing over the last few years with artificial intelligence planting its roots in the recruitment system. This is an area where humans are availing the benefits of AI to reduce their efforts and accelerate the recruitment process.

Who are the Stakeholders that would be affected by adopting Machine Learning and AI? Although technologies like AI and machine learning have been knocking on the doors of recruitment for quite some time now, most organizations are still reliant on keyword-matching algorithms which don’t take a candidate’s values, likes, and behavior into account. As a matter of fact, many organizations are still using job boards as their one-stop-shop. These can be like a black hole for resumes and often feature job posts that have long been closed out.

Another consideration is the various players within the industry that include recruiters, MSPs, VMS, and SMEs, all of which have their say in the recruiting process. With no set protocol for inclusiveness, the process is often laden with several loopholes which the candidates exploit to make their way inside. The technology could potentially help to streamline these various factions and allow for better identification of talent.

Companies Like Diatoz Solutions Pvt. Ltd. are already Using Machine Learning in Recruitment!

We’re talking about machine learning algorithms that can train themselves over time to predict future trends. The more data you supply to a machine learning system the better it performs. From anticipating the best sourcing channels for a job role to predicting candidate performance based on skill set, behavior, and values, these algorithms help organizations increase their talent quality while decreasing the time-to-hire.

Machine Learning could reshape every stage of Hiring and can broadly be classified into

1. Recruiter’s Assistance

Below are the use cases for AI and Machine Learning divided into the three different stages of hiring, and the possibilities for every stage.

1.1. Candidate Recommendation

Machine learning can not only help you screen resumes for keywords but meaning. You can benchmark current resumes against resumes that have been successful in the past. You can then sort candidates based on different parameters like values, behavior, and reputation in your database. Most importantly, by using ML algorithms you can remove bias from the hiring process.

1.2. Candidate Interviewing

Video interviewing using ML algorithms not only helps save time but also reflects on patterns that are hard to catch even by the most experienced recruiters. By using algorithms to analyze voice tone, body language, context, keywords, and words choice, these algorithms can segment candidates based on their proficiency, culture fit, and many more parameters.

Using data from previous studies ML algorithms are now also being used to develop competency tests that assess candidates on several traits like proficiency on skillset, communication, data interpretation, etc.

1.3. Reviews and Follow-up

The Auto Review system can provide candidates instant responses on the status of their application, therefore, the candidates are involved in all the stages of the Hiring Process.

By using AI chatbots in recruitment companies can answer any questions a candidate might have 24/7. These AI chatbots come with programmed responses to a set of frequently asked questions and learn to incorporate new responses based on chat history. By self-learning based on interactions, they can reduce recruiter man-hours considerably.

2. Candidate’s Experience

Below are the use cases for AI and Machine Learning divided into the two different stages of hiring for good candidate experience.

2.1. Resume Parser

A resume parser helps in analyzing a CV and turns it into structured data. This data is much more convenient to manipulate, handle and store, not only speeding up the entire hiring process but also helps in providing a stellar candidate experience. A Resume parsing module will scan a candidate’s CV and extract important information such as name, contact information, address, relevant work experience, skills, certifications, and so on and store them in a database for further intervention. Utilizing CV parsing can save time for candidates from typing information.

2.2. Job Recommendation

Finding relevant jobs from a very big overload of vacancies is challenging. That’s where recommender systems show up in order to smartly support users in their job hunting journey. Typical recommender systems use information about a user to recommend relevant jobs. A typical recommendation engine is a type of data filtering tool using machine learning algorithms to recommend the most relevant jobs to a particular candidate. It operates on the principle of finding patterns in candidate behavior data, which can be collected implicitly or explicitly.

Conclusion

There is no doubt that by using machine learning algorithms in talent acquisition, companies can make better hiring decisions whether it be sourcing, screening, or onboarding. Technology has the potential to impact every part of the recruitment cycle and can considerably reduce the cost-to-hire. Data-driven, consistent, and free from any bias, machine learning applications in recruitment might revolutionize the entire process.