Recruiters around the world struggle with finding the right candidate from a heap of resumes that pour in from job portals, social media, and their official career page. Grouping, analyzing and presenting the data in a unified form takes so much of time that it takes at least 16 days for an interview process can be completed. This could have serious downturns in the operational smoothness of an organization. Especially in today’s job-hopping millennial-worker generation where employees would have switched 4 jobs by the time they hit 32 years of age (LinkedIn Economic graph).
AI puts forth few remedies that will help reduce the time taken to hire new candidates and achieve a higher level of post-hiring engagement that ensures lower attrition rates. Perfomatix, being a leading machine learning development company, we explore the use cases that make sense and implement the transformative technologies into the requirements of our clients, to deliver value to their needs through our services.
Some of the benefits that AI can bring to recruiting are briefed as below.
Talent Sourcing
Unlike the 90s when resumes were sourced primarily from newspaper classifieds, today, recruiters receive resumes through their career page, job portals, social media portals like LinkedIn or through referrals. This digital means of obtaining candidate information provides diverse datasets that can be used for talent sourcing.
Machine Learning and Natural Language Process can help recruiters determine from their resumes and previous job trends whether a candidate is a good fit for the long-term. Skill gaps can be identified and measures can be taken to improve the suitability of the candidate for the position.
General Electric is already using “collaborative filtering” to recommend job matches to employees based on how their peers have moved within the company. Salesforce, on the other hand, is analyzing the text of annual reviews submitted by employees. The analytic results are further used to recommend jobs that could better use their skills or interests of the employees. This helps in internal mobility of resources as well as better employee engagement. (Source: Human Resources Professional Association)
Chatbot-led Initial Screening
The initial screening process for most candidates follows a set of traditional questions that even recruiters have become wary of asking. Nevertheless, these questions are the gateway to knowing whether the candidate fulfills the basic criteria that makes him/her eligible for a F2F interview. A wrong initial screening results in a waste of time for interviewing the wrong candidate and prolonged delay in filling the position.
Intelligent AI systems and chatbots like Helena which is dubbed as a Siri for recruiters would enable recruiters to speed up their initial screening process. Mya by Firstjob is another AI & NLP based chatbot that automates more than 75% of the recruiting process. Global brands like Microsoft, Lyft, Uber, WeWork, etc. have already started using these chatbot systems to take care of their initial candidate screening process.
Talent Mapping – Right Man For The Right Job
According to Glassdoor, each corporate job attracts at least 250 resumes. Every recruiter is under immense pressure to decide whether the resume has all the necessary characteristics that the ideal profile requires. Given that most recruiters form a first impression about the candidate under six seconds, there is a large probability that good fit candidates are often missed out due to the wrong judgment.
Above all, each resume comes in a different layout with essential details spread across multiple pages which makes it further difficult for recruiters to decide whether the candidate is a good fit or not.
All the resumes collected by an organization while sourcing talent for a specific position is left discarded in its ATS (Applicant Tracking System) or HR portal of a CRM system. This creates a vicious cycle of looking for new candidates while the profile of an ideal candidate could already be existing in the database.
AI & ML can help sift through the existing database of applicants systematically to pick out profiles that could match the current position perfectly. The ideal candidate fit can be measured at preset points for skills/qualifications required and those as provided in the resume. The result would be enormous time saved in repeating the hiring process and also in zeroing in on the right man for the right job with the help of the AI system.
Unbiased interview process
67% of active and passive job seekers confirm a diverse workforce of an employer to be a decisive factor in evaluating a job offer (Glassdoor survey). But, recruiters often fail to set up a diverse workforce due to conscious and unconscious bias.
All recruiters undergo some unconscious bias based on age, gender, location, and other demographic backgrounds that discourage them from considering a candidate as an ideal profile. AI and ML in recruitment can weed out a major obstacle that is preventing recruiters from reaching out to the best talent for a position.
Machine Learning systems can be taught to ignore such biases and focus on matching the profile requirements to available talent. They also help overcome personal attachments or sentiments that often get in the way of unbiased recruiting. As Bob Schultz, GM of IBM Talent Management Solutions puts it, “Too often, it’s the squeaky wheel or the person pushing the hardest that gets his or her requisition addressed first,”
ML-equipped recruiting systems like Watson help recruiters bypass such sentiments, look at existing vacancies, compare them with historical fill rates and prioritize positions that need immediate attention.
Final Thoughts
The World Economic Forum estimates that technology and other factors could cause disruptions for 1.4 Million US jobs. On the other hand, they will also create a range of new jobs that did not exist a decade ago. Going further, AI and ML would also have a significant impact on the recruitment industry.
ML in recruiting would enable recruiters to take more ‘humane’ decisions based on accurate data predictions. The result would be reduced hiring costs, reduced administrative burden, long-term retaining of top talent among many others.
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