August 30, 2022
As we continuously evolve to understand the world through the lens of data, more and more industries enjoy drastic changes to their landscape. Human Resources, hiring to be specific, has evolved to encompass contemporary technologies to take full advantage of inventive opportunities.
AI has become integral to this development, making way for recruitment automation and advanced candidate sourcing capabilities through understanding the data candidates make available to us.
We’ve already covered some aspects of using AI in HR in the first article, but today we’ll focus closely on hiring as it has been a huge issue for many companies.
“The global talent crisis is making it difficult for businesses to recruit qualified staff to fill open positions. HR leaders must design more effective talent evaluation tools to reduce the effects of the shortage and win the raging talent war. Skills tests and remote interviewing techniques may be used as part of this task, as these methods may help to speed up the hiring process.” - People Hum
Let’s see how AI can help deal with the biggest recruitment issues and what impact it has on the whole future hiring.
Table of contents:
The beauty of data analysis lies in the ability to solve problems with limited available information. For recruitment, this means being able to profile candidates based on limited information extracted from the application and sourcing processes, and also being able to create hiring processes that make it possible to collect more data from candidates. Think of the average candidate's journey, how much information is the candidate relaying to the employer?
With career pages, it becomes possible to collect applicant data before they even apply for your vacancy. An important piece of information that can be extracted from candidates is if they are truly showing keen interest in your open job position, an indicator for which is the time spent on your job listing.
This can reveal if applicants are carefully considering the job requirements and the job details before applying or not. Without this, employers can really distinguish candidates based on their interest in the interviewing stage (which might be enough in many cases) but now recruiters can factor this in way sooner in the hiring process.
Around 90% of applicants don’t even read the job description before applying, with the average candidate spending no more than 14 seconds on a job posting before applying.
Not only does this mean that employers must optimize their job descriptions to spark as much interest as possible from applicants in those 14 seconds, but also that there is an intrinsic value in finding those applicants who took more time and carefully read through the job description before applying.
Recruiting software makes this data available through powerful reporting and analytics which make it seamless for businesses to integrate relevant data into their decision-making process. One of the tools that enable this type of screening is Hirebee, their reporting and analytics make it easy for businesses to collect all relevant data from their candidates’ journey.
After a candidate has applied, you’ll receive two additional pieces of information, upon which you should be able to base your candidate screening process. These are the resume and the application form responses. Most employers identify candidate screening as the most time-consuming process in recruitment. Not only is this the most time-consuming, but also where most biases and guesswork take place.
This is where AI steps in. AI can read or parse these resumes and application responses for you and give a more accurate estimate of the candidates most fit for the job. With deep learning algorithms and constant development, recruitment AI has come a long way to be able to accurately guide recruiters to the most relevant resumes and applicants, and this sort of technology can no doubt get better to eliminate discrepancies.
This is a common feature included in most recruiting software and applicant tracking systems. The algorithm works by comparing the submitted resumes with the job description of the vacant job position. It then matches the most relevant resumes by taking into account the job requirements of the vacant position with the skills, experience and other relevant factors included in the applicants’ resumes.
Even when employers perform recruitment automation through leveraging automated candidate screening, there’s still plenty of space for the AI to hasten and improve recruitment in other hiring processes, such as candidate sourcing and interviewing.
Implementing AI for video interviews can help interviewers with their evaluation of the interview and gain the additional data needed from the interview to move forward into the next hiring stage. For example, businesses are implementing AI in the interviewing process by delegating it to perform data analysis through interviews’ several typical functions, such as:
However, as good psychologists can already perform this job with efficiency, AI for interviews is only typically used for companies with extremely high hiring volumes, such as a lot of companies operating in China. On the other hand, candidate sourcing with AI is much more revolutionary for employers.
Candidate sourcing is like searching the web for a B2B product. You access a sophisticated database, search for what you are looking for, review the results that fit you best, and finally negotiate on the money.
However, results for a B2B product seem to be much more accurate. This is because you can access much more data on the web for the products you are looking for than you can with any candidate database. The last piece of the puzzle is being able to search and filter for cultural fits, to make searching through candidate databases as effective as searching the web for products, helping recruiters find exactly what they are looking for.
The potency of such technology would present a major paradigm shift in HR, and to be honest, all the signs were there. More and more companies have been pouring money into human resource development, essentially investing in their employees to learn valuable skills which are needed by the business. Partially, human resource development has seen this rise because they have found it profitable to teach their employees life and on-the-job skills that schools, families, and communities have failed to provide. This defeats the idea that companies are in the job market to pay for labor and skills, and instead reinforces the idea that companies are in the job market to invest in people.
What all this means is that these developing hiring techniques and capabilities complement the increasing trend in human resource development, and the idea behind it — which is to invest in people. This technology can empower businesses to pursue hiring strategies that may be profitable for them — which is hiring for loyalty, enthusiasm, easy learners and people prone to integrating well into the team, to make their investments into human resource development more profitable by increasing employee retention rates and productivity, rather than hiring for only for qualifications and skills.
Although recruitment has already been through massive changes which have sparked new thought leadership and a new outlook on hiring processes, we have every reason to believe that more is yet to come. Comparing recruitment tech with other such industries such as marketing tech, it becomes clear that the HR Tech revolution is still in its infancy, and has more to offer to hundreds of millions of employers and billions of job-seekers globally.
Looking for more HR tech resources? Check our our blog: