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Hiring is one of the riskiest aspects of any business. Organizations incur a great cost in hiring and training its workforce. Rehiring or re-training levies further cost to company. But the one expense that is irrecoverable for an organization and can have several more ramifications is - bad hire. Talent acquisition teams have been striving to eliminate bad hires due to their quantitative (efficiency) as well as the qualitative (culture) effect on a business. According to Global HR Research, 39% of businesses report a decrease in productivity due to bad hire.
Recruiters are turning to Artificial Intelligence (AI) and Machine Learning (ML) intervention to avoid such hiring hazards. According to the Deloitte Human Capital Trends report, 38% of companies use AI, and 62% intend to do so by the end of 2018. From the operational burden of screening and interviewing to making the right person-job matches, companies are now incorporating AI to make the processes more efficient and accurate. The growing use of AI and ML have led to ‘Predictive Hiring’, a new recruiting trend, which is less instinct and more data-driven.
What is predictive hiring?
Predictive hiring is a concept that helps organizations forecast their skill requirement to achieve specific business goals. Talent acquisition teams use data and assessment science to devise a pattern of skills and attributes based on the performance of their existing high potential employees. This data enables the recruiters to take a more analytical approach while filtering the best-fit candidates for the specified roles.
Who is using predictive hiring and why?
Recruiters are gradually doing away with traditional hiring to move to more data and algorithm-centric hiring. The use of data and analytics is not only limited to finding the right candidates, but it rather extends to other human resource goals such as fostering, training, and retaining the best talent. HR departments have to go an extra mile to carry out these processes and interactions with the millennial workforce in order to maximize their potential.
Google is one of the best known examples for using ‘people analytics’ in their workforce management. This tech giant collected and used the data of its current employees to build an in-house algorithm which was further used for measuring productivity, making decisions about benefits, etc.
Reliance Jio is another instance of a forward looking organization that experimented with learning analysis to create their own prototype of a ‘successful profile’. Job engagement and performance were identified as the pivotal factors while creating this potential profile.
Large organizations such as HCL Technologies are implementing natural language processing and semantics analysis to analyze the database of more than five million candidates and internal employees. The organization is able to meet its manifold HR goals through the insights revealed by the data. While the tool has provided predictive intelligence to the hiring managers to enable them to find the best-fit talent for the organization, it has also given critical insights on the current skill gap. Their HR is using the latter to devise the necessary talent development strategy for their employees. The company has reported 17% demand fulfillment through reskilling which was planned basis the findings of predictive intelligence used by the hiring and training managers.
Will predictive hiring be the driver of future recruitment?
As industry leaders pave the way for futuristic hiring using AI and ML, even smaller businesses and startups are geared up to ride the wave of predictive hiring. Startups are already using human analytics tools such as Predictive Index (PI) in a big way to meet the challenges of effective recruitment. Organizations are turning to this format of hiring to avoid any talent risk by forecasting it. Undoubtedly, there is enough evidence to support the fact that predictive hiring will be adopted across businesses as the more efficient and accurate tool for hiring.