Recruiting for technical roles can be challenging. There are often too many roles to fill, too many or too few candidates to interview and not enough time to get it all done and develop relationships with your key stakeholders: Hiring managers and the executive team.
Working with talent acquisition (TA) leaders and technical recruiters can help companies scalably, accurately and fairly assess potential candidates’ technical skills to fill high-value engineering roles. Technology also offers many advantages that help achieve TA objectives. But in my experience, many TA and HR leaders get frustrated when new tools fail to launch or deliver underwhelming results, because they aren’t adequately adopted, trusted or utilized by end users.
I find that hiring managers are more open-minded to “mechanical” or automated hiring tools if those tools aren’t evaluated on their own, but are evaluated relative to status quo hiring processes.
All of this leads to technical decision-makers and stakeholders developing a natural skepticism for mechanical or automated hiring tools. If your hiring managers seem doubtful about using tech for hiring, here are three strategies to help them embrace hiring tools.
Expect skepticism, it’s natural
Researchers studying how to make scientific hiring tools more effective have discovered an interesting phenomenon: Human beings are naturally skeptical of tools that outsource our decisions (Highhouse, 2008). Left to our own devices, we are hardwired to trust gut instinct over external data points, especially when developing and nurturing new relationships, including who we work with.
Scientists have offered up a few explanations for this preference of gut over data. Some people consider external, mechanical decision-making aids as less trustworthy because of a lack of familiarity with how they work, or because using them reflects poorly on the decision-maker’s value and worth as a leader or manager.
It could also be because there’s a fear of surrendering control and agency to a tool that doesn’t seem to consider or understand context clues. However, research has shown that people make better choices when using mechanical decision support tools than when either humans or mechanical tools make decisions alone.