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Why You Shouldn’t Blame Artificial Intelligence for Poor Recruiting Processes

Nastel Technologies®
February 18, 2022

A few stories have emerged lately about how companies are having difficulty hiring enough people to fill key positions. These articles allege that automated talent acquisition (TA) or Applicant Tracking Systems (ATS) reject millions of candidates solely due to minor flaws in their applications such as gaps in their work histories, failing to meet a long list of exacting job requirements, or failing to use the language of the job description on their resume.


Many people believe that these systems are weeding out strong candidates at a time when they are desperately needed. Some also blame technology for perpetuating bias in hiring. The result is frustrations for businesses and job seekers alike.


As The Great Resignation grinds on, businesses are still losing star workers with specific skills. However, as they try to replace those workers, many organizations may be inadvertently screening out great candidates to fill those critical roles. Also, job seekers are becoming more sure of themselves and pickier about what they want out of their careers– 88 percent of people say their definition of success has changed since the pandemic started, according to the Oracle 2021 AI@Work Study.


Workers are now more likely to leave jobs that aren’t giving them what they want to seek out better options, but they need recruiting teams to see and review their applications to find a new job.


Now, I won’t argue that the artificial intelligence (AI) used in these systems is perfect– by its very nature, it is continuously evolving as more data becomes available to improve the underlying models. But I do want to point out that, in most cases, it is not the tech that most impacts how well hiring systems work. Humans play a key role in driving the decisions that guide the technology. Therefore, in a time when organizations are finding themselves understaffed, it’s important for organizations to reconsider how they are setting up their hiring processes in order to find the right candidate at the right time.


The Nagging Resume Gap Problem

Many of the criteria that are causing so many resumes to be rejected are holdovers from before the pandemic. Let’s look at the work history gap issue for example. Traditionally, employers winnowed out candidates with unexplained gaps in their work histories. That used to make sense from the hiring perspective, especially if there were many qualified candidates for a small number of jobs: Why pursue someone with a patchy record when there were ample qualified people with no such gap?


But the pandemic–and the resulting “Great Resignation”–have made these practices outdated and are forcing companies to be more broadminded about application requirements. For example, it seems not just ill-advised but irresponsible to rule out a skilled candidate who took time off to care for children or elderly family members in the midst of a global health crisis.


The difficulty these people have had in finding jobs is a problem, but let me be clear: TA systems do not automatically reject anyone for having a red flag in their application such as a work history gap or missing a few items in the job requirements–at least they should not, and certainly none of the solutions I oversee do this. The people using the system need to decide what criteria they are going to use to vet candidates and configure the system to recognize them accordingly. From there the system will highlight how each application holds up to those criteria, flagging concerns so the recruiter or the employer can follow up on it. Basically, the technology kicks the decision upstairs for the human experts to deal with, such as by bringing it up during the interview.


It is also time for companies to reconsider standard job requirements such as a college degree or years of experience.


Recruiters may be missing workers who have critical skills even if they didn’t get it from a college education. While these may have been a relatively useful shorthand for skilled workers in the past, people today have access to a wide range of ways to gain skills including bootcamps, online certifications and even on-the-job training.


Recruiting teams need to tweak their own mindsets and processes so that candidates can get a fair shot at presenting their capabilities and so organizations can access a wider pool of workers to fill critical roles. This will ultimately help businesses too as it gives them a wider pool of candidates to find the critical skills they need.


What Good Technology Can Do

Once recruiting decision-makers fine-tune their requirements, technology like AI that is used in the recruiting process can improve both sides of the hiring process. For example, a strong TA system powered by AI can help recruiters focus on skills rather than experience. One of the challenges recruiters face is that there is no common language for skills. Across different industries, companies, and roles, the same skill may go by many different names. For example, someone with competencies working with HTML, CSS and JavaScript may just call that entire skillset “Web Design” on their resume instead of breaking out each individual skill. AI is getting better at analyzing a worker’s job history including resumes and certifications to understand the skills the candidate has and match them to the skills the job requires.


AI can also help guide job seekers towards roles that are a good fit for their career goals by analyzing their resume, certifications, and LinkedIn profile for potential skills they may not even have known they had. Then the system can show them options for open roles that will put those skills to use.


Another way advanced TAs can improve the recruitment process is by helping recruiters overcome some of their biases. For example, many orchestras hold blind auditions, where a musician plays behind a screen so the only thing the manager can judge the candidate on is performance quality. What could be fairer or more meritocratic than that?


AI can replicate some of the “blind” hiring practices of the type some orchestras use to diversify their musical staffs. AI-powered recruitment platforms can scrub identifying characteristics of applicants, including race, gender, age, even mentions of hometowns or universities. What remains for the hiring manager are listings of the candidate’s skills, degrees, training certificates, and experience.


As the technology used in recruiting continues to evolve, refining the role of technology will be a never-ending, iterative process. But technology is just one piece of the puzzle. For the overall process to work best, humans need to make the right decisions in the deployment of that technology and the hiring professionals need to act judiciously on the input they receive. AI has some major advantages over people in its ability to analyze huge amounts of data and make recommendations, but humans need to provide their own oversight to make sure the process actually helps candidates and recruiters alike in the hiring process and making fair judgement.


As much as I believe in the power of AI, the final employment decisions will always come down to the humans in charge.


This article originally appeared on, to read the full article, click here.

Nastel Technologies is the global leader in Integration Infrastructure Management (i2M). It helps companies achieve flawless delivery of digital services powered by integration infrastructure by delivering tools for Middleware Management, Monitoring, Tracking, and Analytics to detect anomalies, accelerate decisions, and enable customers to constantly innovate, to answer business-centric questions, and provide actionable guidance for decision-makers. It is particularly focused on IBM MQ, Apache Kafka, Solace, TIBCO EMS, ACE/IIB and also supports RabbitMQ, ActiveMQ, Blockchain, IOT, DataPower, MFT, IBM Cloud Pak for Integration and many more.


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