Hiring in tech has always been specialized, with specific knowledge, skills and languages needed for certain roles. So, it’s unsurprising that talent acquisition professionals have leveraged AI to help them with this difficult task.

Today, strict and unforgiving AI algorithms filter candidates to ensure only the most qualified (or optimized) resumes get through. Plus, candidates must hope that the AI algorithm analyzing their information isn’t trained on biased data, another significant obstacle in hiring

However, tech professionals can leverage AI to help overcome these hiring hurdles while also focusing on establishing and building their personal brands to help them stand out.

Here's a guide to how businesses use AI in tech hiring and how professionals can still come out on top.

Is AI in Recruitment a Friend or Foe?

Hiring in the tech industry requires specialized knowledge and experience, adding to its difficulty. But AI-powered applicant tracking systems (ATS) can assist HR managers by analyzing mountains of resumes and looking for keywords like “JavaScript” or “10 years of experience.” The AI algorithm then grades each resume out of 100, for example, and the ATS will separate successful candidates who scored, in many cases, above 85 percent.

This helps talent acquisition professionals quickly and efficiently filter through resumes and personally review a much smaller amount of applications. But this isn't the only way AI can expedite the hiring process in tech.  

The development of AI has also helped steer the recent shift toward skill-based hiring, particularly for technology jobs. Employers can use AI testing platforms, like Testify, for instance. These platforms allow employers to use pre-made and custom tests with topics that cover everything from coding to communication skills. Talent acquisition professionals can then ensure applicants can successfully decode a bug or understand JavaScript, for example.

AI platforms can also use computer adaptive testing, which adjusts the difficulty of each question based on the applicant's previous answers. Thus, employers can get a precise understanding of an applicant's knowledge. For talent acquisition professionals, this technology can save them precious time, as research has found that adaptive tests reduce test length from anywhere between 50-to-90 percent.

Businesses are eager to adopt this approach since employees chosen for their skills, rather than just their education, are five times more likely to perform well in their jobs. A skill-based approach also expands access to candidates. For instance, a newly qualified software engineer may lack work experience but can prove their skills through test results.

While AI may seem like hiring’s magic bullet, this new tech doesn’t come without its challenges. The main issue lies in how AI system parameters are set, as a highly skilled, self-taught programmer might be automatically rejected for lacking a computer science degree.

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