Artificial intelligence (AI) is all the rage these days, as advocates hail the technology for its potential to make jobs easier across virtually every industry. For talent acquisition professionals, the primary benefit of AI seems to be in automating the early stages of the recruitment process.
AI models’ superior data-processing capabilities have made them such an in-demand tool across so many global industries. An AI model can take large sets of data and process them nearly instantaneously, in a fraction of the time it would have taken a human.
For recruiters, this type of superior data processing is paramount. They are often dealing with mountains of applicants looking to fill their vacant positions, and sorting through each one would require more time than they have on any given day.
As a result, even industries once seen as fundamentally “human” have found that AI could be a valuable tool for their profession.
What Recruitment Tasks Can AI Automate?
AI can streamline a wide spectrum of time-consuming recruitment tasks, from conducting market research to sourcing a diverse pool of job applicants.
- Market research. Recruiters can better understand the conditions in which they will be hiring, such as the number of job seekers in the market, their salary expectations, the competition for talent and more. Therefore, recruiters can approach their talent acquisition strategies being much better informed.
- Early stages of recruiting. AI models can be used to screen applicants’ resumes before the recruiter even sees them, cutting back on the time recruiters waste on unqualified candidates. By training AI to look for specific skills or experiences on a resume, recruiters can be provided with a list of qualified candidates to take to the next stage of the recruiting process.
- Candidate matching. AI can scan applicant profiles and job descriptions to connect candidates with employers who are a good fit for each other. AI technology can help ensure the pool of candidates that recruiters sort through will be more consistently qualified for the roles to be filled.
- Interview scheduling. If a recruiter and candidate both give the AI access to their respective calendars, the model can find a time slot that works best for both parties. This cuts down on the back-and-forth that can happen when scheduling job interviews by playing phone or email tag to try and find a convenient time for both the recruiter and candidate.
- Creation of job descriptions. An AI model can take data from historically successful job descriptions that have matched open positions with skilled candidates, as well as information from the recruiter on the specifics of the job posting, and then create a job description that will attract the ideal applicants. Of course, the description generated by the AI is unlikely to be perfect. It will need the human touch of a recruiter but will nevertheless significantly cut down on the time recruiters spend on these tasks.
- Expansion of the candidate pool. By automating tasks such as candidate sourcing, recruiters no longer have to invest extreme amounts of time in posting their job listings on different forums and media. Instead, listings can be posted across a wider array of digital platforms in less time, thereby increasing the number of potential applicants reached.
- Predictive analytics. AI models can be used to evaluate applicants with a high potential for success. By analyzing historical data alongside the applicant’s profile, an AI model can predict whether that candidate will be successful if offered the position.
The Limitations of AI in Recruiting
However, it’s crucial to note that no current AI program yields infallible results. For example, if an AI is programmed to look for a certain indicator — such as a set number of years of experience with a specific job title — it could cause the model to overlook other candidates who may be ideal but don’t meet that indicator.
Say an applicant has the same years of experience in a similar role, but with a different title. If an AI model is trained to look only for candidates with a given job title, everyone without that title would likely be filtered out regardless of their other qualifications.
AI tends to retain the same biases with which it has been trained. After all, the output of an AI can only be as good as the quality of the command it is given. If a recruiter instructs the model to operate under a strict set of conditions and biases, the results will reflect them. Thus, it is essential to establish a system of checks and balances when implementing AI technology into the talent acquisition process to help ensure it is being used fairly and responsibly.
Recruiters must also maintain high levels of data integrity. When dealing with the recruiting process, recruiters are using a great deal of sensitive data, including names, addresses and Social Security numbers. As such, it is recruiters’ responsibility to ensure that applicants’ data does not get into the wrong hands.
It seems that no industry will be untouched by the spread of AI, making it all the more important to embrace this new paradigm before it takes over. For recruiters, this means using AI models to automate time-intensive tasks such as market research, resume screening and writing job descriptions. In doing so, not only will recruiters be able to save their valuable time, but they will also be met with a pool of candidates whose qualifications fit and are backed by data.
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