Business professionals constantly search for ways to help their companies gain a competitive advantage. Now, machine learning empowers these professionals to get the most out of data.
Indeed Recruitment Evangelist Bianca Rehmer offered tips to help business professionals partner with machines to drive talent acquisition during her presentation to Argyle’s CIO membership at the 2018 Chief Information Officer Leadership Forum in New York on June 19. In her presentation, “Humans vs. Machines,” Rehmer discussed the impact of machines on talent acquisition.
Machines provide business professionals with insights they may struggle to obtain elsewhere. Plus, machines help companies streamline their day-to-day operations, as well as eliminate human error. “At the end of the day, we’re only human, and we make mistakes,” Rehmer pointed out. “And we often bring those mistakes into our workplaces.”
In addition, machines enable business professionals to process massive amounts of structured and unstructured data, faster than ever before. They also are designed to perform consistently and deliver predictable performance. And with machines, businesses can eliminate biases, too. Biases sometimes affect business professionals as they evaluate potential job candidates and whether these individuals will fit within a company’s culture. Perhaps worst of all, biases may occur subconsciously and dictate how business professionals run their companies.
Many companies are devoting significant time and resources to integrate machines into their everyday operations. Yet machines alone are insufficient, particularly for businesses that want to find and retain top talent. Without the right set of guidelines in place, machines may struggle to deliver long-lasting value for companies. “Machines are very data-driven and optimized … and are targeted,” Rehmer said. “And machine learning ensures an algorithm follows a set of guidelines and learns.”
Furthermore, business professionals must learn about all aspects of machine learning. If business professionals understand the ins and outs of machine learning, they then can develop algorithms designed to help machines make data-driven decisions. That way, business professionals can integrate machines into their talent acquisition efforts, ensuring a company can find and retain top talent time and time again.
“If you’re not using the right keywords, your algorithm won’t work, and it won’t show the right job-seekers the right jobs,” Rehmer stated.
If business professionals incorporate emotional intelligence into their talent acquisition efforts, they can work in conjunction with machines to make informed hiring decisions. Best of all, business professionals can use data provided by machines to take an objective view of potential job candidates. At the same time, these professionals can use their emotional intelligence in combination with this information to make the best-possible hiring choices.
“[Machines] can’t always tell us why, and they don’t have emotional intelligence,” Rehmer indicated. “Humans still need to figure out the why.”
Going forward, the push for machine learning will continue to increase at companies of all sizes and across all industries. However, relying solely on machines for data-driven talent acquisition may be problematic. Business professionals can deploy machines and use the information that they provide as part of the hiring process. But these professionals must realize that machines lack the emotional intelligence of humans, and as such, are incapable of making the optimal hiring decisions on their own.
Machine learning may help business professionals analyze a broad assortment of data about potential job candidates, but these professionals must continue to use all available resources throughout the hiring process. If business professionals guide a company’s machine learning investments, they can speed up data collection and analysis. And as a result, these professionals can obtain a wealth of information that they can use to bolster their talent acquisition efforts.
“There are going to be more and more algorithms, and machines are going to get smarter,” Rehmer pointed out. “It is humans that are guiding machines … and it is up to us to know their capabilities.”