Most of us are exposed to artificial intelligence (ai) through the many digital assistants available for our personal use. Siri helps us send hands-free text messages while we drive. Cortana reads our emails and tracks our location. Alexa plays our favourite music while we make dinner. From smartphones to Fitbits and kids’ toys to Kindles, the technology tract continues to evolve—and tracks us in turn as it does.
A Future Freed for Focus
These digital assistants are tangible examples of AI; using them for simple tasks frees up our time to focus on more important things. As we use these assistants, some have the capacity to learn our behaviours and preferences, resulting in more accurate suggestions for music and restaurants we might like. How all of this rolls over into the world of work is a more emergent story, but the technology in question has already walked through the door.
We are on the tipping point of AI revolutionizing many industries, with investors pouring millions of dollars into research and development; Google and Mark Zuckerberg’s investment fund announced last year that they are backing a $150 million AI institute in Toronto. While its ultimate impact on the workplace is as yet unknown, human resources professionals are already grappling with this technological revolution with recruitment and team building being impacted dramatically.
Recruitment: Use Your Human “Beane”
When we look at recruitment, the realm of professional sport gives us a good overview of the big picture. It is difficult to dispute that hiring decisions for sports teams carry perhaps the greatest consequence for success or failure. Signing a first round draft pick who fails to launch carries significant salary and overhead costs with no return on investment. As a result, and perhaps unsurprisingly, a great deal of energy and resources are committed to picking the proverbial “best,” but what constitutes the best?
Oakland Athletics’ general manager Billy Beane, played by Brad Pitt in the movie Moneyball, went against the grain by using non-traditional player statistics to predict overall team success. While other teams used stolen bases, runs batted in and batting average in their recruitment of players, Beane’s statistical analysis suggested that on-base percentage and slugging percentage were better indicators.
With other teams using more traditional recruitment strategies, there were many players who did not meet these “popular” requirements available at a lower cost. Utilizing this strategy, Billy Beane and the Oakland Athletics were able to build a high-performing baseball team with a much lower salary total, taking them to the finals two years in a row.
“Partnering” With Technology
Beane’s brilliance was in his ability to recognize non-traditional baseball player characteristics that were correlated with success. Recent advancements with AI have resulted in improved matching of candidates to job requirements, and there are many applicant tracking systems (ATS) from which to choose.
An ATS will manage terabytes of data generated from thousands of applications and present candidates who match criteria outlined in a job posting. However, the major shortcoming of these systems is that an ATS will only select candidates who present a fit between the role and a set of pre-defined criteria. If we leave it up to the ATS and simply screen for traditional competencies correlated with job success—years of experience, relevant certifications—we may not find the best fit.
Instead, there must be a human element woven into the screening process to allow for non-traditional competencies, like the ability to get on base or we risk hiring a tribe of expensive, homogenous automatons. Moreover, consider that Steve Jobs, Marissa Mayer, Elon Musk and other great minds would have surely all been screened out using such methods of application assessment.
Teambuilding: OTB Needs TBD
The same principles apply when assembling special purpose teams from an existing group of employees. One example of an AI platform built to increase special team performance is IBM’s Opportunity Team Builder (OTB). OTB breaks the teambuilding process into a series of components which include identifying roles needed to win sales and creates a list of recommended experts based on skills, social network, and expertise.
More importantly, OTB uses AI to project the impact of adding potential team members to the overall success of the sales team by predicting how individuals will work and perform together. The same principles can be applied to other teambuilding situations, whether it be building a multi-disciplinary advisory board or a cross-functional recruitment team.
As with Billy Beane, IBM recognized that there are other attributes that can positively contribute to success and the inclusion of factors such as social capital represents a step in the right direction. Harnessing the power of AI to quickly and easily pull together a highly-rated team is an excellent example of how AI can work for us.
However, despite looking great on paper, we’ve all been part of a “dream team” that has performed poorly because of other, more difficult to measure member traits. Without the human element intimately involved in team design and selection, there is a high risk of team failure.
Crafting a Symbiotic Success
Harnessing the power of AI to either recruit or build special teams represents an exciting, time-saving service, which dwarfs Siri’s handy “skip this song” function. Yet unless there is human intelligence behind artificial intelligence and a symbiotic relationship between person and machine, the risks of losing top talent or fielding a sub-par sales team far outweigh the time-savings.
Indeed, with the advancement of AI into ever greater realms of our personal and professional lives, more than ever the opportunity— and onus—is upon us as individuals to more deeply explore our human potential.
Published in: PeopleTalk Summer 2018