Automation helps HR to identify talents at scale
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Automation helps HR to identify talents at scale

With the Great Resignation still in full swing, employers are searching for all possible assistance to recruit qualified professionals back into their ranks. The human resources management (HRM) industry – including talent acquisition software and services – is presently worth approximately $20 billion. On the heels of continuing digitization and automation of recruiting and HR operations, demand is expected to rise at a rate of more than 12% each year until 2028.

Around the world, organizations are putting a higher value on developing and maintaining a top, brightest, and most diversified workforce. As a result, enterprises – as well as small/medium-sized businesses – will gain access to a new level of automation due to advances in artificial intelligence (AI), machine learning (ML), and predictive modeling, allowing them to automate their recruitment even while they deal with significant changes in workplace practices involving remote and hybrid work.

Furthermore, four out of five recruiters interviewed in an Entelo survey said automating candidate sourcing entirely would boost productivity. They were unanimous in their conviction that having additional information would help them qualify prospects, assess applicant pools, improve outreach, and fine-tune hiring processes. Despite this, 42% of respondents didn't have the data or time to implement analytics, much alone generate insights from it. And that's when automation enters the equation.

What is recruiting automation?

The first step in human resource or personnel management is to hire individuals. Every day an unfulfilled open position costs businesses profit and productivity. AI-powered software platforms can gather pertinent information on applicants, give recruiters access to it, and then accurately process it to speed up and automate numerous sub-processes, including applicant sourcing, screening, diversity and inclusion, interviews, and applicant tracking.

Recruitment automation is a type of technology, often provided as software-as-a-service (SaaS) and increasingly driven by AI, that an organization may use to manage all parts of its workforce. Its primary goals are automating recruiting workflows, reducing hiring costs, increasing HR productivity, filling vacant posts, creating bias-free hiring processes, and improving the company's overall talent profile.

Benefits of AI-based recruiting automation 

Recruiting software can automate the purchase of job advertisements on various platforms and other websites. AI solutions use programmatic advertisements and branded content to display job possibilities on sites frequented by your target audience. It can also help you save money by optimizing your employment advertising budget and lowering costs per applicant.

An applicant tracking system (ATS) is software that automates a company's entire recruitment and hiring process. It allows you to search and sort through resumes, filter applications, and identify the best prospects for open positions all in one place.

Manual screening of resumes is one of the most time-consuming stages of hiring. AI-powered software "learns and understands" the requirements for the position based on the job description, then filters candidates according to keywords, phrases, and terms used in their applications, thanks to the natural language processing (NLP) technology.

Intelligent algorithms can evaluate potential candidates' skills, experience, and other characteristics against prior hires and the advertised job role to determine prospective candidates. They can also rate or grade these individuals as they proceed through the hiring procedure. AI-based chatbots can learn more about job seekers by starting conversations with them. The algorithms can also look through their LinkedIn, Twitter, Facebook, and other social media accounts and industry-specific platforms where they participate (such as Stack Overflow for programmers) for a better picture of their personality, skills, abilities, and interest.

How to make the most out of AI-based recruiting automation?

Data-driven recruiting teams are already outperforming their peers, according to the said Entelo study. Furthermore, 84 percent of recruiters feel competent in applying AI and machine learning in their daily operations. However, how can AI algorithms be used in recruiting without introducing (and magnifying) human prejudice into the equation? The answer is setting company-specific performance standards, identifying key metrics that can be observed objectively, and analyzing recruitment efforts using talent analytics.

Algorithms designed to perform a certain task often succeed because the most comprehensive and biggest data sets are accessible to them. It is necessary to acquire these measurement points and feed them into the talent pipeline or recruiting automation software. The process is reversed on implementation: It's always a good idea to test the algorithm on a small (but diverse) pool of prospects and manually check its output before adopting it as your organization's standard hiring solution.