Brianna White

Staff member
Mar 25, 2020
Since the pandemic, more companies have been incorporating automated artificial intelligence and machine learning-based platforms to assist with the screening and hiring of employees. According to a recent study from The Sage Group, 24% of businesses have started using AI for acquiring talent, with 56% of managers planning to adopt these technologies over the next year. AI can augment the recruiting process by automating time-consuming recruiting and hiring tasks to more accurately identify the right candidates for a position, ensuring diverse and fair selections and reducing bias in the workplace—a common goal businesses are looking to achieve.
Hiring teams face a great deal of pressure these days to hire for business growth while simultaneously keeping up with rising turnover rates, which can have a costly impact on the business. A broad estimate shows the average cost per hire is just over $4,000 per employee and the cost of turnover is even higher at approximately one-fifth of an employee’s annual salary). With the ability to efficiently analyze large volumes of data instantaneously, AI significantly reduces the time spent finding and recruiting great potential candidates. Intelligent hiring platforms integrate AI, automation and predictive analytics with pre-hire assessments and a range of interviewing tools to identify qualified candidates with more precision and increase hiring efficiency and quality of hire. Using AI in candidate assessments can help identify those who are likely to be a turnover risk.
How does this work? AI is fueled by data, and the more information an AI-based platform has, the better it will be at delivering results based on that data. Due to its machine learning capabilities, AI looks for patterns in data, and makes assumptions based on its findings. Therefore, in recruiting and hiring, AI can be trained to look for a job candidate’s skills and competencies to determine that person’s potential for a particular role. It can find statistical correlations between the applicant’s characteristics and the various skills required of the open position to determine the applicant’s success rate, as opposed to focusing on past experience or “culture fit” (i.e., similar characteristics, qualities and interests)—traditional methods that may lead to bias.
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