How next-gen automation is helping recruiters to identify qualified talent at scale

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With the Great Resignation showing no signs of letting up, recruiters are looking for all the help they can get to replenish their head counts with qualified talent. The human resource management (HRM) market – including talent acquisition software and services – is currently valued at nearly $20 billion.

It is expected to grow at a rate of over 12 per cent annually until 2028 on the back of continued digitisation and automation of recruiting and HR operations.

Across the world, enterprises are putting an emphasis on creating and retaining the best, brightest, and most diverse employee pool. 

Expectedly, advances in artificial intelligence (AI), machine learning (ML), and predictive modelling are giving enterprises – as well as small/medium-sized businesses – a never-before opportunity to automate their recruitment even as they deal with radical changes in workplace practices involving remote and hybrid work.

In fact, four out of every five recruiters surveyed in an Entelo study believe productivity would increase if they could automate candidate sourcing altogether. 

They were unanimously of the opinion that having more data would assist them in qualifying candidates, evaluating candidate pools, improving outreach, and perfecting hiring workflows. Despite this, 42 per cent didn’t have the data or the time to implement or dig into analytics, let alone turn the data into insights.

Enter recruiting automation solutions.

What is recruiting automation and how can it help?

Human resource or people management as a function begins with hiring. Every day an open role remains unfulfilled costs companies profit and productivity. Intelligent tools based on AI can gather relevant data on candidates, make it available to recruiters, and then process it accurately to speed up and streamline multiple sub-processes, including candidate sourcing, screening, diversity and inclusion, interviews, and applicant tracking.

“The days of physically sorting through hundreds of resumes and posting your job descriptions on each individual board are over,” notes Ilit Raz, CEO of Joonko, a talent feed solution for surfacing candidates from underrepresented backgrounds. “Without some form of automation or HR tech, you’re always going to be a step behind your competitors, especially when it comes to recruitment.”

Recruiting automation is a category of technology – delivered as software-as-a-service (SaaS) apps and increasingly powered by AI – that an organisation can use to manage all aspects of its workforce. Its central aims include:

  • automating recruiting tasks and workflows
  • reducing cost per hire
  • increasing productivity of HR personnel and recruiters
  • accelerating filling of vacant posts
  • bias-free hiring
  • improving the company’s overall talent profile.

How does a typical AI-based recruiting automation technology help you go about achieving these goals? Here are the different functions where it can play a key role:

Job ads: Recruiting software can automate purchase of ads on jobs platforms as well as other websites. It leverages programmatic advertising and branded content to place job postings on industry-specific sites that your target candidates frequent. It can also help you optimise your job advertising budget and reduce cost per applicant.

Application tracking system (ATS): An ATS is software that automates the complete hiring and recruitment cycle for an organisation. It provides a centralised location to manage job postings, sort through resumes, filter applications, and identify the most suitable candidates for open positions. This way, HR managers can stay organised and get easy access to details on the stage at which a candidate is in the hiring process.

Resume screening: Manually screening resumes is one of the most time-consuming parts of recruiting. AI-based software “learns and understands” the job requirements based on the listing and filters resumes based on keywords, terms and phrases used by candidates.

Pre-qualifying candidates: Intelligent algorithms can determine probable candidates by evaluating their skills, experience and other characteristics with those of previous hires and the published job role. They can also rank or grade these candidates as they move them forward in the hiring process.