Tomas Chamorro-Premuzic is a renowned organizational psychologist who frequently speaks and writes on topics at the intersection of talent, innovation and AI. Today, he is a Professor of Business Psychology at Columbia University and University College of London, and the Chief Talent Scientist and at ManpowerGroup, a staffing firm. Follow him on Twitter @drtcp.
It’s true that well-designed, structured interview processes are predictive of future behavior, including job performance—but they rarely happen. Most interviews are like a first date; the behavior in the first meeting doesn’t necessarily reflect what you see months or years later.
Between two organizations, the one that is more data driven will be better able to spot trends and talent, and they can be more meritocratic. Because of this, we can expect companies with data-driven hiring practices to maximize their talent ROI and outperform competitors.
There’s now more digital data available on candidates and employees than ever before, from social profiles, email, video interviews and more. With AI, companies can mine this data for predictive and meaningful insights on a person’s potential in the workplace. This opens up unprecedented opportunities to help organizations deploy people in the best possible ways.
Some people are put off by the idea of automation in talent decision-making and can feel creeped out by the thought of algorithms evaluating their resume or social media presence. To mitigate reluctance, Tomas believes companies should lead with transparency; it should be clear exactly what personal information the company captures and how automation will be used, and candidates should be allowed to opt in or out.
Not only will this benefit organizations by encouraging acceptance but it will also help to educate candidates on what their personal data actually says about them and where their skills could best fit within an organization.
Human bias has pervaded hiring throughout history. Moving forward, Tomas believes that we must decrease reliance on human observers and instead use technology, including AI, to identify the predictive signals of somebody’s talent or potential without weighing attributes like ethnicity, gender and age. Ultimately, the use of AI in talent practices will lead to more merit based decision making.