John McCarthy, widely acclaimed as one of the godfathers of Artificial Intelligence (AI), defined AI as the science of creating intelligent machines which can perform ‘human-like’ cognitive functions. These functions can help organizations in removing bias, increasing accuracy and reducing the turnaround time of various business processes.
Machine Learning is a subset of AI and provides smart devices with the ability to learn dynamically from their environment. Together, Artificial Intelligence and Machine Learning are redefining the organizational landscape across a range of domains - recruitment being one of them.
Recruitment: Areas where AI and ML can make an Impact
The recruitment process is a complex activity with various steps in it, recruiters have been struggling to find out the right candidate from all the resumes that they receive using a plethora of sources all around them. Success lies in sourcing high quality candidates, mapping their fitment with organizational needs, scheduling interviews with them, conducting the interview process with transparency, and finally onboarding them with the right training and resources.
AI and ML are evolving as a strategy implemented by employers to efficiently handle recruitment related challenges. In this article, we delve into the main areas of recruitment where AI and ML have already started to make an impact. Below are the few ways in which AI and ML are enabling recruiters and their organizations –
Talent sourcing and mapping
Traditionally, recruiters had to go through a highly tedious job of sifting through scores of resumes to find the right candidate. AI has enabled organizations to get rid of this manual process by bringing in virtual assistants that can do that job more efficiently. For example, Canadian startup Ideal leverages AI to screen resumes based on the client’s requirements. Its virtual assistant integrates itself with the client’s applicant tracking system (ATS) and based on the client’s past hiring decisions, the assistant adapts itself to identify the desirable elements in a resume using pattern recognition methodology.
Chatbot led initial screening and scheduling
Post the resume screening, the recruiters go for an initial round of questions with the candidate. This task can now be automated thanks to the AI based chatbots like Helena which can speed up the screening process for organizations. These chatbots can also talk to the candidates and collect relevant information from them as well as schedule interviews. International brands like Microsoft and Uber have already started deploying chatbots to handle the task.
Improve candidate’s experience
A 2017 report by Glassdoor suggested that 66% millennials anticipate quitting their current jobs by 2020. With attrition being a significant challenge for human resource managers, AI can bring in improved candidate and employee experience by the use of predictive analytics. It can help companies in predicting the likelihood of a candidate fitting well into a company’s work culture. The better the prediction, the more the chances of an employee staying back.
Some potential pitfalls
The benefits of AI and ML integration into recruitment are many, however, there are some potential pitfalls as well. Companies will have to decide how much they want to rely upon a machine’s judgement regarding the suitability of a candidate. Also, adoption of AI and ML will have to be approached in a phased manner to ensure that the algorithms are not botched in the first place. It will be prudent to choose the right source to integrate the machines. For example, relying solely on the LinkedIn profiles of candidates to shortlist them might lead to poor matches. As HR managers and companies still dabble with AI and ML, there might be unsavoury surprises in store too.
Like any other new technology in its nascent stage, AI and ML will also bring in both rewards and challenges to the recruitment scene. A strategic approach while adopting them will help companies identify problems and resolve them without any major dent in the recruitment process.
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