How automation technology can help recruiters identify qualified talent at scale

AI-based automation tools can collect and process applicant data to speed up and streamline candidate sourcing, screening, diversity and other HR functions.

With the wave of resignations showing no signs of letting up, recruiters are looking for all the help they can get to replenish their workforces with qualified talent. The human resources management market (including talent acquisition software and services) is currently worth nearly $20 billion.

Against the backdrop of continued digitization and automation of recruitment and HR operations, it is expected to grow at an annual rate of over 12% through 2028.

Across the world, companies are placing an emphasis on creating and retaining the best, brightest, and most diverse employees. It can be expected that advances in artificial intelligence, machine learning and predictive modeling are providing enterprises, as well as small and medium-sized businesses, with unprecedented opportunities to automate recruitment, even as they deal with fundamental changes in the industry. Workplace practices involving remote and hybrid working.

In fact, four out of five recruiters surveyed in one study believed they would be more productive if the process of recruiting candidates could be fully automated. They agreed that having more data would help them qualify candidates, assess their candidate pool, improve outreach and refine their recruiting workflows. Despite this, 42% of recruiters don’t have the data or time to implement or drill down into analytics, let alone turn data into insights.

What is recruitment automation and how can it help?

Human resources or people management as a function begins with recruitment. Every day, an unfilled job vacancy costs a company's bottom line and productivity. Smart AI-based tools can collect relevant data about candidates, provide it to recruiters, and then accurately process it to speed up and streamline multiple sub-processes, including candidate sourcing, screening, diversity and inclusion, interviewing and applicants track.

Ilit Raz, CEO of Joonko, a talent feed solutions provider, noted that “the days of sorting through hundreds of resumes and posting job descriptions for every board member are over.” , used to showcase candidates from underrepresented backgrounds. “If you don’t have some form of automation or HR technology, you’re always going to be a step behind your competitors, especially in recruiting,” he said.

Recruitment Automation is a technology that is available as software as a service (SaaS) application delivery and increasingly powered by artificial intelligence, which businesses can use to manage every aspect of their workforce. Its core goals include:

• Automate recruiting tasks and workflows
• Reduce the cost per hire.
• Improve the productivity of HR staff and recruiters.
• Expedite filling of open positions.
• Bias-free recruitment.
• Improve the overall talent situation of the enterprise.

How does typical AI-based recruitment automation technology help recruiters achieve these goals? Here are the different capabilities it can play a key role in:

• Recruitment advertisements: Recruitment software can automatically purchase advertisements on recruitment platforms and other websites. It leverages programmatic advertising and branded content to post job postings on industry-specific websites frequented by targeted candidates. It also helps recruiters optimize job advertising budgets and reduce recruitment costs.
• Application Tracking System (ATS): ATS is a software that automates a business’s complete recruiting and hiring cycle. It provides a centralized location to manage job postings, sort resumes, filter applications, and identify the best candidates for open positions. This way, HR managers can stay organized and have easy access to details about where candidates are in the hiring process.
• Resume Screening: Manually screening resumes is one of the most time-consuming parts of recruiting. The AI-based software "learns and understands" job requirements based on the list and filters resumes based on keywords, terms and phrases used by candidates.
• Pre-Qualified Applicants: Intelligent algorithms can identify possible candidates by evaluating their skills, experience and other characteristics against those of previous recruiters and advertised job roles. They can also rank or score these candidates as they move them forward in the hiring process. AI-based chatbots can collect basic information and learn more about candidates by initiating conversations with them. These algorithms can also scan their LinkedIn, Twitter, Facebook and other social profiles as well as industry-specific platforms on which they are active (such as StackOverflow for developers) to better understand their personality, knowledge, abilities and qualifications.

When does recruitment automation go wrong?

Despite the advancements in recruiting automation software, it is not a panacea for recruiting challenges. No technology can handle the cumbersome recruitment process. Data overload is a critical issue. Today, recruiters have so much data (about candidates and job roles) that they have neither the time nor the skills to analyze it and make the right decisions. Many times, the cost and complexity of accessing and validating this data is prohibitive.

Another long-standing problem is bias. While the hiring process itself is often biased (in large part due to businesses’ tendency to rely on employee referrals), the use of AI and automation in recruiting can sometimes compound the problem.

Jelena Kovačević, IEEE Fellow and Dean of the NYU Tandon School of Engineering, said: “If you don’t have a representative data set that describes any number of characteristics that recruiters decide on, then of course it’s impossible to properly find and evaluate candidates. In one example, Amazon developed an AI-based recruiting tool that analyzed patterns in resumes it received over a decade and ultimately led to discrimination against female applicants. Amazon eventually abandoned the tool.

The biggest issue facing data and AI is how to maintain diversity, equity, and inclusion (DEI). Some diversity-related mistakes in recruiting that are exacerbated by automation and machine learning are:

Insensitive, elitist, or less-inclusive language in job postings (forcing diverse candidates to abandon their applications).

• Limited sourcing and limited applicant pool (excluding applicants from other areas or those who did not attend certain schools).
• No Remote Work Policy (Keeping Out Candidates with Disabilities and Transportation Challenges)
• An interesting approach to DEI that aims to meet minimum regulatory or industry standards.
• Lack of automation.
• AI can cause problems, analytics is the cure

While artificial intelligence is certainly not a magic bullet for recruiting, it has made a lot of progress since the recruitment program developed by Amazon failed. Big progress. Research finds that data-driven recruiting teams already outperform their peers. Additionally, 84% of recruiters have high confidence in their ability to use artificial intelligence and machine learning in their daily workflow.

The question is: How can recruitment automation technology use artificial intelligence algorithms in the recruiting process without adding (and amplifying) human bias?

The answer lies in establishing enterprise-specific performance benchmarks to determine objective measures of recruitment key indicators of candidate competency and use talent analytics to measure the success and efficiency of recruiting efforts.

Algorithms that achieve the purpose for which they were built often do so because they can work with the largest and most extensive data sets. It’s the business’s responsibility to collect these data points and feed them into the business’s talent pipeline or recruitment automation software. The process is reversed when implemented, which is always a good idea to test the algorithm on a small (but diverse) set of candidates and manually review its output before using it as an actual recruiting solution for the business.

The above is the detailed content of How automation technology can help recruiters identify qualified talent at scale. For more information, please follow other related articles on the PHP Chinese website!