The cost of a bad hire can be severely damaging for budget-conscious small businesses, but is artificial intelligence improving the accuracy of recruiters when identifying and onboarding the best candidates for roles? artificial intelligence Using data from the US Department of Labor, the cost of a bad hire is equivalent to at least 30% of the employee’s first-year earnings. However, for mid to senior-level roles, these expenses can spiral, with industry experts suggesting that the cost of a bad hire can climb to between $100,000 and $240,000 per employee when taking lost productivity and recruitment expenses into account. equivalent to at least 30% equivalent to at least 30% Factors that increase the cost of bad hires can involve lost productivity as colleagues spend more of their own time fixing errors or addressing project delays, training and onboarding expenses, negative impacts on team cohesion and morale, and the loss of customers or clients due to poor service. However, artificial intelligence use cases suggest that help may be on the way for recruitment teams seeking stronger accuracy in the hiring process. AI adoption in HR is growing at such a rate that businesses are scrambling to provide adequate training to team members. Today, as many as 82% of HR professionals are now actively using artificial intelligence at work, but just 30% have received sufficient job-specific training. 82% of HR professionals 82% of HR professionals As the rate of training improves, we’re set to see the qualities of AI tools in the recruitment landscape shine through, with emerging use cases providing a glimpse into the enhanced accuracy that can be gained in working with artificial intelligence to discover the most suitable hire for roles. Data-Driven Job Descriptions Bad hires can be caused by many different factors, and the most preventable is inaccurate job descriptions. Bad hires Data shows that an estimated 61% of employees leave their role due to a lack of clarity on what’s expected of them at work, which can come with costly ramifications for employers. 61% of employees 61% of employees Artificial intelligence eliminates this risk by analyzing market trends and company workforce needs to generate accurate job descriptions that are automatically posted to relevant recruitment platforms for the best chance of finding the most suitable candidate. generate accurate job descriptions generate accurate job descriptions These job descriptions can anticipate the profiles of successful candidates who have already been onboarded into the workforce, helping to gain a better understanding of the soft skills and other intangible qualities that have historically led to high-quality hires. Automated Candidate Screening One of the best advantages AI has provided recruiters is the ability to apply equal time to each prospective hire when sifting through applications. Recruiters can be prone to different biases when it comes to screening candidates, and it’s possible that their judgment can be affected by the time of day or the scale of prospects that they need to review. Modern AI recruitment software has the power to analyze thousands of applications in real-time, identifying the most qualified candidates based on a series of complex criteria that surpasses keyword matching. For instance, Unilever has developed an AI system that processes nearly 2 million job applications each year, reducing the hiring process from four months to just four weeks, saving over 100,000 hours of recruitment time in the process. processes nearly 2 million job applications processes nearly 2 million job applications Despite its far quicker pace, AI can look beyond simple experience and qualification matching, identifying key indicators and patterns within the profiles of candidates that may be challenging for human recruiters to spot. Predictive Analytics in Hiring Artificial intelligence tools like predictive analytics and machine learning (ML) can also drive far more accuracy when it comes to making a successful hire. Using predictive analytics, it’s possible to anticipate organizational hiring needs and the possibility of future recruitment requirements by analyzing turnover rates, company growth forecasts, prospective skills gaps, and employee performance. anticipate organizational hiring needs anticipate organizational hiring needs This can eliminate the risk of making a bad hiring decision by recruiting for a role that doesn’t need to exist at this moment in time for the company. Predictive analytics and machine learning can also unite to predict the success of prospective hires based on taking the profiles of other successful employees into account. However, it’s important that organizations ensure that their datasets are rich enough to incorporate insights from varied demographics, such as different genders, ethnicities, ages, and professional backgrounds, to ensure that AI systems aren’t building their own biases by focusing only on a single demographic through their available historical data. varied demographics, varied demographics Banishing Bad Hires Bad hires can be extremely costly, particularly for small businesses, but artificial intelligence is rapidly evolving to support recruiters in finding the best candidates based on many different critical factors. small businesses As a result, businesses can enrich their company culture and protect their operational efficiency by onboarding the best workers for their vacancies, all with a far lower risk attached to getting a key hiring decision wrong. Although AI is only as accurate as the data it’s trained on, with the right level of human oversight, it’s possible to create a wholly fair recruitment process for businesses of all shapes and sizes without the costly risk of high employee turnover or critical errors in the quality of the new hire.

Why Unconscious Bias is Ruining the UK Workforce and How AI is Helping to Counter HR Inefficiencies

The Cost of a Bad Hire is Growing: How AI is Delivering Enhanced Accuracy in Recruitment

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

$4 Trillion Appears Inevitable as Nvidia Remains the Star of the Generative AI Boom

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

The Noonification: White Man (11/26/2022)

The Noonification: The Metaverse is a Sh*tshow (11/2/2022)

100 Days of AI Day 1: From Newsletter to Podcast, Leveraging AI for Audio Transformation

$4 Trillion Appears Inevitable as Nvidia Remains the Star of the Generative AI Boom

The Noonification: How Often Do NFTs Pass The Howey Test? (1/13/2023)

Darwin's Hybrid Intelligence to Align AI & Human Goals for Startups & VCs

The Noonification: White Man (11/26/2022)

The Noonification: The Metaverse is a Sh*tshow (11/2/2022)

100 Days of AI Day 1: From Newsletter to Podcast, Leveraging AI for Audio Transformation

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps