Is AI bad news for Talent Acquisition?

If you work in HR or Talent Acquisition I doubt a day goes by without somebody trying to sell you a new AI based technology that will ‘revolutionise the way you recruit’. AI based TA Tech is heralded as the ultimate solution for talent acquisition, promising to streamline high-volume screening and cut down time-to-hire etc. However, in reality, the technology around Talent Acquisition has been a bit of a mixed bag in recent years, with some HR departments finding it hard to equate value with the extra expense involved. Even its fiercest critics would find it hard to deny AI has delivered on some of its promises and administrative efficiency. But that same technology is now creating a new set of challenges that make the critical task of processing and assessing good applicants more difficult than ever for human recruiters.

The most significant challenge stems from the accessibility of generative AI tools like ChatGPT for job seekers. This has created a technological arms race where the lines between a truly qualified candidate and an AI-optimised application are blurring.

CVs are starting to look the same. Job seekers can now instantly generate highly polished résumés and cover letters, perfectly keyword-matched to the job description. This ability to "game" Applicant Tracking Systems (ATS) means a recruiter's shortlist is increasingly populated with applications that are technically perfect but may lack the genuine experience or nuanced skills of the person behind them.

The CV volumes are going up and up. By lowering the barrier to entry, AI encourages a "scattergun" application approach to role applications, and by the way, that scattergun is running fully automatically. Recruiters are now inundated with an even higher volume of applications, making the manual review process—the one step intended to verify the AI's results—overwhelming and prone to fatigue. Paradoxically, the solution meant to reduce administrative work has led to an increase in the complexity of the remaining human-led work.

Using technical assessment processes is also a challenge. AI can be used to solve common or simple coding problems. A candidate may copy the assessment question and paste it into a model to receive a functional, optimised block of code, which they then paste back into the test environment. This bypasses the need for the candidate to demonstrate problem-solving, debugging, or coding knowledge. While less effective for complex, novel, or timed problems, AI is often fast enough to help a candidate with knowledge-based or common multiple-choice questions. They can use a secondary device or quickly tab out of the test environment to feed the question into the AI and get an answer, especially when time limits are generous.

Conclusion

In essence, while AI has reduced much of the administrative hassles, it has simultaneously introduced new layers of complexity, ethical risk, and time-wasting, making the recruiter's job harder than ever. So, what about the future? It’s clear that the role of a recruiter has fundamentally changed. The recruiter's primary focus has shifted away from manual screening toward acting as an auditor of the AI's results. They must now be adept at both strategic talent marketing and understanding algorithmic fairness and data governance. But at its heart I would argue that human interaction is more important than ever.

One final thought, as AI struggles to quantify traits like creativity, cultural fit, resilience, and communication, the recruiter is left with the immense pressure of validating these crucial soft skills in the later stages of the interview process. The stakes for the recruiter are thus even higher, as the AI has already failed to filter out candidates who look perfect on paper but will be a poor cultural fit.

So, has AI technology made the role of the recruiter easier or harder? Time will tell.