Traditional Hiring is Inherently Biased

While diversity and inclusion has been proven to increase revenue, most companies still face the challenge of translating good intentions into effective action. Often, the same recruiting and hiring policies and practices that don’t reflect today’s reality are still in place. Job descriptions and the job postings typically are based on what business leaders and HR professionals THINK is needed, not what they KNOW is needed.

Good Intentions Alone Aren’t Enough

If traditional talent acquisition processes were bias-free, this would be a very different discussion.  Unfortunately, that’s not the case. A 2018 survey of small businesses found that 93% of them actively try to hire “employees with different attributes, backgrounds, and life experiences”— specifically in terms of race, ethnicity, gender, age, immigration status, and sexual orientation.[1] But good intentions don’t necessarily translate into a diverse workforce.

Perhaps the most telling finding of the survey is the link between the racial and ethnic background of a small company’s founder and the extent of minority hiring.  Nearly half of nonwhite employers reported that the majority of their employees were racial or ethnic minorities, but only 13% of white owners could make the same claim.

This is not a problem exclusive to small businesses. Many larger corporations with strong diversity and inclusion initiatives and experienced HR professionals are showing less than stellar D&I results.

AI Tools Eliminate Assumptions and Bias

Through systematic analysis of success in a role based on objective performance data, AI tools:

  • provide an objective perspective on the qualities and characteristics that lead to success in a given role;
  • make it possible to hire against proven job-specific success factors instead of traditional, often unchallenged, and often rushed, ideas of what is needed;
  • bring consistency and a systematic approach to hiring that takes assumptions and bias out of the equation.

AI Tools Help Ensure the Right Fit

Imagine sitting across the desk from a job applicant and knowing exactly how well that person fits the success profile for the position you’re hiring for. AI tools make it possible to assess a candidate’s “fit” early in the interview process, saving time and money that could otherwise be wasted on continuing the hiring conversation with someone who doesn’t match the role’s success profile.

Fit-to-Role Hiring Increases Diversity and Inclusion

Not relying solely on resumes as the key predictor of future performance and shifting to using AI-derived success profiles to identify candidates who have what it takes to succeed in a given role opens up opportunities for job-seekers and companies alike:

  • Job postings reflect data-derived success profiles;
  • A broader pool of external and internal candidates emerges;
  • Hiring managers are able to look past formal credentials and hire for “fit”;
  • Data-driven hiring decisions are less likely to be influenced by conscious or unconscious bias, resulting in greater organizational diversity.

Increasing Diversity and Inclusion is the Real Benefit of Using AI Tools for Hiring

Many vendors of AI tools emphasize the benefits of reducing time to hire by automating tasks like reviewing resumes and tracking candidates, and the associated cost savings can be substantial. The greatest benefits of using AI tools in the hiring process, however, come from the enrichment of an organization through the acquisition or internal development of diverse talent that might otherwise go undiscovered.

Hiring diverse team members who fit the success profile for the role can stimulate creativity, support better decision-making, increase team productivity, and improve company performance. [2] Sounds like a pretty good ROI, doesn’t it?

[1] “How the Traits of Small Business Founders Impact Diversity, Benefits, and Employee Satisfaction.” Gusto Report, May 2018. https://drive.google.com/file/d/1ddoBiEYnitsrsPwjo8RMk7qX2h23hIdb/view

[2] https://www.sciencedirect.com/science/article/pii/S2352250X16300744?via%3Dihub