The article explores how artificial intelligence (AI) is improving the recruitment process by identifying a job seeker’s skills and abilities required for a job and simplifying the process of candidate matching.
Using artificial intelligence (AI) in the recruitment process is far from science fiction. Applicant tracking systemsOpens a new window are already here, and they’re making the job of human resources professionals a whole lot easier.
Undercover Recruiter statsOpens a new window suggest that AI applications will replace 16 percent of HR jobs in the coming 10 years. The general belief is that AI will enhance the recruitment process by simplifying onboarding, carrying out relevant new employee training, keeping track of applicants and employee development, as well as assisting applicants who may have questions.
Applicant tracking through machine learning is of particularly high interest. While AI simplifies the challenging process of sifting through resumes, recruiters can focus on more strategic aspects of the process. How do such systems work, are they reliable, and do they bring a sufficiently high return on investment? Let’s attempt to answer these key questions right now.
How Does AI Applicant Tracking Work?
Resume screening and the invitation of the best candidates to a job interview is considered one of the most tedious parts of the process by many HR professionals. Depending on the specifics of the position and the requirements, a company may receive from dozens to thousands of resumes.
Finding the right talent is critical for businesses, but currently, the process is rather ineffective and costly. The cost of onboarding one new employee can reach 240,000 dollarsOpens a new window , the Society for Human Resource Management reports. At the same time, the risk of hiring the wrong person is high because HR professionals will often rush through the process. In a survey, 43% of recruiters said that they made a bad hire because they felt pressed to fill a position fast.
Through the use of artificial intelligence solutions, this cumbersome process can be automated.Â By using post-hire information, functionalities can be added to the applicant tracking system (ATS) that the company utilizes. As a result, better recommendations and choices can be made when new candidates are examined for a certain position.
Insights about the current employee experience play an important role in the automation of the process. Also, the system will take qualifications and skill sets into consideration. Based on all of this data, artificial intelligence can rate new candidates to determine whether they’re a good match for the job being offered.
The Google Hire technology is one example of a recruitment-focused machine learning solution. Its launch was announced in April 2018, and the solution became available in July. Through the use of the application, recruiters can put together a smart list of former candidates who may be a good match to current positions being offered by the company. By implementing additional candidate discovery, Google Hire can determine how profiles match a job description or the location of the company looking for new team members.
While Google Hire is currently aimed at either passive or pre-determined candidates, the same premise could apply to a completely new set of job seekers. Machine learning makes it possible to identify the skill and capabilities required for a certain open position. Adding location to the set of criteria enables the app to make even more relevant candidate suggestions.
On top of sorting through candidates, artificial intelligence applicant tracking solutions can also simplify the process of candidate matching. Based on the job requirements, the app will select the strongest matches after analyzing each candidate’s skills, education, location, salary preferences, etc.
This capability doesn’t have to be restricted to the pool of individuals who have applied for a specific job. The search could be extended to individuals who have previously submitted their resume or who are regularly looking for new positions within the organization. The process is another highly challenging one for the HR team, and machine learning can help here as well.
Adoption and Effectiveness of AI Applicant Tracking Solutions
Machine learning software requires a lot of data to learn how to make the right decisions. This is why the adoption of such solutions is still in its early stages. Many small and medium-sized companies may feel discouraged from giving them a try due to the information that has to be fed to the app to increase the effectiveness of the applicant tracking and sorting process.
There’s another challenge to the recruitment process, and it stems from the old-school reliance on resumes to identify the right candidate. Standardized resumes are potentially biased and inaccurate. In order to screen applicants, machines have to be provided with accurate information. This reliance has contributed to HR professionals making notoriously bad decisions for the selection of good candidates. **AI solutions can rely on a much wider pool of data, and when such information is available, a comprehensive and unbiased profile can be created.**
Recent reports suggest that only one percent of Fortune 500 companies are relying on artificial intelligence tools for enhanced recruitment processes. However, such data may be misleading. AI recruitment and candidate selection Opens a new window may be featured as a capability within a more comprehensive software solution that the business relies on. The fact that companies aren’t opting for AI candidate tracking solutions specifically does not necessarily mean such tools are not a part of the HR process.
The chances are that adoption will become much more widespread in the years to come. The ROI guaranteed by AI applicant tracking solutions is one of the primary reasons why.
For a start, machine learning can eliminate the number of bad hires â€“ a problem that we’ve already highlighted as rather costly. A bad hire can cost businesses a lot due to the specifics of the onboarding processes. Fewer mistakes will diminish the loss of important resources.