You've seen the video by now. Yes, the one of Serena Williams arguing with chair umpire at the U.S. Open Finals.

If you know tennis at all, you know she’s a great player. She’s done a ton for the sport. She’s done a ton for women. She’s a mom. She’s a successful businesswoman and far from stupid. She’s a role model to many.

But, when the chair umpire catches her coach sending her a sign from the box, he (rightfully so) issues her a violation. Regardless of if it was a “thumbs up” like she claimed, any form of input from a coach is illegal in tennis. Serena could –and should—have acknowledged that a signal was made and moved on. A small foul; let’s continue playing. Instead, she let her emotions spill over.

From the moment she first argues, she was in the wrong. And she continued to let her emotions spiral, now-famously saying that she “doesn’t cheat to win; I’d rather lose.” She goes on to break her racket, and the situation escalates further, claiming the umpire is a thief for stealing points from her. The result? “Code violation. Verbal abuse.”

The reality is simple: Serena never trusted the chair umpire’s judgment of the initial call. She didn’t trust the data she’s received. Her human judgment of “well that can’t be right” interfered and impacted her performance. And she paid dearly for it.

AI & MACHINE LEARNING

While I may not be a world-class tennis player, I have been in Serena’s position before. I’ve been handed data and too thought, “Well, there’s no way that’s right.”

I, like most employees, have two choices:

• Completely ignore the information, claiming that it’s false or

• Investigate, re-run the numbers, and see what my team can do.

Either way, I’m at a pivotal moment many of us encounter in the workplace:

DO I TRUST THE DATA AND ACT—OR NOT?

Humans, in my opinion, have always been a bit skeptical about technology. Somewhere, a computer and AI generated these numbers, but that computer doesn’t know the countless hours my team put in for this project. It doesn’t know I missed my son’s soccer game to get it over the finish line. Now, I’m staring at data that tells me we went in the wrong direction and have to start over? That’s a hard pill to swallow.

Data, like what the chair umpire presents to Serena, doesn’t consider our human emotions when it presents its findings. Instead, it shows us the truth of the situation, whether we want to admit it to ourselves or not.

USING THE DATA EFFECTIVELY

A lot of athletes have a hard time admitting when their performance is slipping. They’ll argue it’s the coach’s, umpire’s, or even the media and their “tough questions” fault. But there are some already absorbing the data they receive and turning to machine learning and AI for feedback on where and how they might improve.

Red Sox player, J.D. Martinez, is one example of someone who is ready to receive this type of feedback. As a recent Sports Illustrated article outlines, Martinez’s production was declining, and he was on the brink of losing his baseball career. In 2013, a hitting coach spat out data and warned him if he didn’t change the way he swung the bat, he might not see another season in the majors. The message stuck with him: his numbers weren’t good enough (i.e. an OPS of .700 or higher) to stay in the big leagues.

During the weeks (and years) that followed that conversation, Martinez dedicated himself to self-improvement by watching video clips of other sluggers (Ryan Braun, Albert Pujols, etc.) and studying their swings. Rather than recoil and reject the information, he used the data to adjust everything – from his stance to where his elbow ended after each motion in the batter’s box. Over the next four seasons, Martinez built his average OPS to .936 and now, in 2018, could be the first MLB Triple Crown Winner since 2012.

J.D. could have argued, but instead, he adjusted. If companies want to out-perform their competition, they will need to do the same.

ARE YOU MENTALLY READY TO RECEIVE FEEDBACK?

If you look at past tennis matches, you’ll see that Serena has been in this position before. She has past data (i.e. losses) on what happens if she doesn’t keep her emotions in check.

Serena had the opportunity to adjust during the match, but she couldn’t see the data the chair umpire presented. He continued to collect data that highlighted “wrongs” and gave the outcomes. She wasn’t ready to act on what the data was telling her. In other words, she wasn’t mentally ready to receive the feedback.  

Despite this being a tennis match, how she reacts is ultimately a business decision: Serena lost 2 million dollars. For most companies, this is not an insignificant event.

If companies let emotions take over and dictate strategy, they will miss out on opportunities. Leaders need their organizations and employees to be more agile now than ever before. When people are out of their comfort zone (the data tells them that they are going in the wrong direction), it’s hard to adjust to it. And if organizations aren’t ready to “hear” what the data is telling them, it’s useless. Keep in mind, emotions can affect more than just an ability to see data. There are countless stories of companies under-performing because employees are wary to change. Employees resist upgrading software and technology despite the data saying it will help them perform their jobs better.

A true leader’s job is to lead the company in the right direction, to take all data from machine learning and AI, and make business-forward decisions based on it. Like the best athletes, our decisions –and our reactions – will determine whether we are the hero or not.