We’ve published a number of articles recently on the challenges field service organizations are facing with recruitment and hiring. In this article with Robb Origer of DISH and in this article with Roy Dockery of Swisslog Healthcare, we discussed how it is necessary for field service organizations to move away from hiring based on experience and begin hiring based on skills and abilities. However, despite this recognition, the field service industry is still struggling to diversify. You can read here the firsthand perspective of a female field technician on how the industry needs to up the efforts to become more inclusive.
With these articles in mind, when I came across this piece in Bloomberg, I was intrigued. The article begins by discussing some of the challenges that exist with recruiting, across all industries. They certainly ring true for field service – the lack of racial and gender diversity, how the issue is fed when the majority of hires come from employee referrals, the biases of recruitment and hiring managers. As one of the quotes in the article points out, “Identifying high-potential candidates is very subjective,” said Alan Todd, CEO of CorpU, a technology platform for leadership development. “People pick who they like based on unconscious biases.”
The article goes on to discuss the role AI could play in removing some of the bias in hiring to level the playing field for employees to be selected more squarely based on skills and abilities. The article states, “Instead of relying on people’s feelings to make hiring decisions, companies such as Entelo and Stella.ai use machine learning to detect the skills needed for certain jobs. The AI then matches candidates who have those skills with open positions. The companies claim not only to find better candidates, but also to pinpoint those who may have previously gone unrecognized in the traditional process.” However, it goes on to point out that incorporating AI isn’t foolproof since there’s a certain degree of bias that can be built in to how the solution selects candidates by the person that programs them.
Entelo, one of the companies mentioned in the article, has recently launched a tool called Unbiased Sourcing Mode to further anonymize hiring. The article explains that “the software allows recruiters to hide names, photos, school, employment gaps and markers of someone’s age, as well as to replace gender-specific pronouns—all in the service of reducing various forms of discrimination.”
The article goes on to discuss some of the potential challenges of using AI for hiring, but in summary states that while there are certain constrictions using AI is still thought to be a superior option to human bias. I can see how for field service organizations this could be especially valuable. The role of a field technician, the aspirations of field service organizations, and the development of talent are all continually-transforming moving parts. However, oftentimes, the recruiting efforts field service organizations have in place haven’t changed in a decade – or even longer. This creates disparity between what the field service function needs and how the recruiters and hiring managers are used to doing things. Perhaps leveraging AI would help organizations get closer to that skills-based hiring that Origer and Dockery discuss while also working to remove unconscious bias to promote greater diversity.