When automation first entered the human resources space, many otherwise well-meaning HR departments embraced it with bit too much enthusiasm. You’ve doubtless heard some stories from that dark time. The earliest days of recruitment automation, when no one really understood what the technology was or how it worked.
Hiring software that eliminated otherwise qualified candidates based on a few meaningless keywords. Questionnaires which arbitrarily and impersonally sorted applicants into narrow boxes. Poorly-coded algorithms which, rather than displaying impartiality, were every bit as biased as the hiring managers who deployed them.
A lot has changed over the past several decades.
Machine learning is now more advanced than ever, capable of optimizing every phase of the recruitment process, from awareness through to onboarding. Some particularly advanced algorithms are even capable of examining a business’s talent pool in order to predict future hiring needs. Unfortunately, even with these considerable advancements, many recruiters still fall into the same trap as 20 years ago.
Namely, they rely too much on automation and too little on their own judgment and instincts.
The core problem, I think, is that they don’t fully grasp what AI is capable of. Yes, it’s incredibly powerful and beneficial. Yes, when applied properly, it’s capable of drawing out incredible insights that might otherwise have been overlooked.
At the same time, it’s very easy to misapply and overuse AI. An algorithm, after all, is only as good as the data-sets it is fed. As such, without analytics expertise and human intelligence to serve as a foundation for its algorithms, AI-driven recruiting software may cause more harm than good.