It’s no secret that the adoption of AI in the hiring process has created an arms race between talent acquisition teams using the technology to sift through increasingly large mountains of applications and job seekers who have been deploying AI to submit to hundreds of positions to gain a leg up on their competition. This wave of escalation, driven in part by a fixation on metrics like time-to-hire, cost-per-hire, and most problematically, quality of hire, has led to this crossroads, where recruiters feel compelled to measure every action and candidates suffer the consequences.

The search for a solution to the quality of hire formula stretches back decades, and despite a growing industry built around the problem, no one has quite cracked the code—though not for lack of trying.

WHAT TO KNOW ABOUT QUALITY

Considering all this, I think it is time to abandon the search for a few reasons. The primary issue is that quality of hire is just not calculable. It represents a more complex problem than cost or time, with too many variables to boil down to a single, neat data point that can be presented to the C-suite.

Expanding on that, there is the subjective nature of quality when it comes to the workforce. The value an employee brings to their job on day 60 may radically differ from day 120 or day 720. As lives change, personal and professional, so do goals, aspirations, and even base needs, making it nearly impossible to pin the quality of one’s hire to a set point in time. Even as the metric has evolved, an attempt to make it quantifiable still fails to account for what makes recruiting so challenging in the first place—the unpredictable human element.

Even Dr. Sullivan, one of the earliest and most ardent proponents of quality of hire, has said, “Selecting HR metrics is unfortunately not a scientific process.”

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