When the prospect of artificial intelligence (AI) first came into mainstream popularity, fears that it would replace millions of workers dominated headlines. However, the tone has since shifted.
While it’s true that autonomous robots and smart software will take over some jobs, they’ll also create new ones. In fact, Gartner researchers found that job creation will outpace job losses by 2.3 million to 1.8 million. But AI isn’t just creating new career opportunities. It’s also helping leaders build better, more agile teams. Here are three areas in which AI is driving change in how companies recruit, train, and level-up their workforces.
Many businesses have expressed intentions to improve gender equality over the past several years, but there is only so much people can do on their own. People are prone to making biased decisions even when they consciously try to stay neutral. A hiring manager might think he or she is being objective when evaluating candidates’ résumés, but even a person’s name or address may carry certain connotations about their gender, ethnicity, or socioeconomic backgrounds. Try as they might, most people can’t help being swayed by those subtleties.
New AI-driven platforms help get more diverse candidates past the door by screening and qualifying candidates through a more objective lens. Unlike humans, software programs can be designed to ignore certain data points that could lead to biased recommendations, or to screen based on task performance instead of background. Although AI screening isn’t perfect (it is built by humans, after all, so it can be designed with unconscious biases), it can help ensure that a broader range of candidates is considered in the hiring process.
Joonko, a startup based in San Francisco and Tel Aviv, takes this one step further. They’ve created a virtual “diversity coach” that helps companies build inclusive cultures. As Joonko founder Ilit Raz says, it’s one thing to bring a diverse range of talent on board, but it’s quite another to retain it. “I think focusing on just one aspect of the employee life cycle is like trying to solve a piece of the puzzle,” she says. “Once they recruit them, they don’t know how to keep them.”
Using machine learning, a form of artificial intelligence, Joonko’s program connects with a client’s chosen software program, such as an email client or Slack. It then analyzes conversations and events to spot potentially exclusive language and behaviors.
Raz describes Joonko as being “like an anti-virus software” that operates beneath employees’ radars until a potential problem is spotted. Once the system catches a discriminatory pattern, it triggers an email to the appropriate leader with recommendations on how they might improve their processes.
One example is which team members are included in the interview panel when a potential hire comes to the office. If a woman of color is interviewed exclusively by white men, she might assume one of two things: either that there are no other people of color in the company (let alone other women of color), or that none of those women hold decision-making positions. Whichever it is, she may be reluctant to sign on as the first person to break the company’s apparent recruiting pattern.
In reality, there might be several people of color on the team, many of whom might hold leadership roles. However, because the hiring managers default to bringing in the same interviewers without considering the impression being made on potential candidates, they could end up losing out on talented individuals. If Joonko detects that the same people are included in repeated interviews, it can trigger a notification to the designated leader and recommend that they diversify the interview team.
Joonko also analyzes language usage, such as in Slack conversations. Perhaps someone jokingly uses a slur that could be hurtful or divisive to their colleagues. The platform will respond immediately, letting them know the comment could be off-color and recommending that they revise the message. By curbing insensitive behaviors and educating team members in real-time, companies are able to retain diverse talent and foster cultures of inclusiveness in their organizations.
Beyond the recruiting process, AI also can help with onboarding and ongoing employee training. This is critical, especially as the pace of technological change continues to accelerate. Tech touches every job, and employees will need to learn new skills throughout their careers if a company is to keep up. However, not everyone learns at the same rate or in the same ways. Day-long, lecture-style trainings are not nearly as effective—or as efficient—as using personalized training programs.
Imagine that instead of herding employees into a conference room to stare at a 30-slide PowerPoint, you ask each of them to log on to a smart e-learning platform that will adapt to their needs as they work through each training module. The interactive lessons will gather feedback on their performances, adjusting content as they go along to ensure they spend the appropriate amount of time improving their weakest areas.
Meanwhile, the management or leadership teams can access a dashboard to see how employees are doing. If someone is struggling in a particular area, they might reach out to that person to find out what’s going on. Perhaps they need additional support or mentorship to really start making progress.
By integrating e-learning into onboarding and training programs, team members are given the opportunity to excel. Rather than feeling too bored, embarrassed, or frustrated to interrupt a lecture with questions, they’re able to learn in more natural and tangible ways. This will help them integrate the information into their daily tasks, and ultimately lead to a better informed and more dynamic team.
Even with customized training programs, employee performance reviews still need to be conducted. Periodically discussing team members’ performances allows managers to see what everyone is doing at an individual level, and it helps managers better understand individuals’ challenges and motivations.
AI can augment traditional performance reviews by providing a wealth of new insights and metrics that will make for more productive conversations. By seeing the hard numbers associated with a team member’s performance—sales data, campaign ROI, or leads generated, for instance—positive and negative trends can be identified at a glance. But broader contextual data that connects an individual’s work with the company’s overarching goals also can be gathered.
“I see AI as allowing for easy data collection and generating dashboards that can be used to evaluate human performance,” says Dr. Sayeed Islam, a consultant with Talent Metrics and a professor at Farmingdale State College. “I think AI could be instrumental in taking that data and making it easily digestible, so feedback and coaching can focus on performance improvement.”
Of course, it’s important to dig into those numbers to understand exactly what’s happening on the ground level. That’s why Islam sees AI helping, but never replacing humans when it comes to conducting performance reviews.
Let’s take a coffee shop chain, for example. When evaluating a manager's performance, his boss might observe that this particular branch sees higher traffic than all others in the area. On the face of it, that’s the reason for commendation and perhaps even a raise, but it’s important to find out why those numbers are so high. Is he exceptionally good at marketing? Does he incentivize great customer service? Or are there less praiseworthy factors at play, such as deceptive promotions or sketchy reporting?
In a media or marketing company, there may be temptation to reward public-facing individuals for accruing significant social media followings. However, it’s important to look at the long-term impact of emphasizing social influence, Islam says. Does the drive for upping online traffic lead to more shallow or divisive content? How does that impact the brand?
“Whatever you measure and reward is the behavior that you're going to get as an organization,” Islam says. He points to the 2017 Wells Fargo scandal as a cautionary tale. “They rewarded employees for new accounts and didn't care where those accounts came from,” he says. Without clear parameters and careful monitoring, a negative brand culture internally and externally could be created.
So, when using AI-generated data in performance reviews, always consider the context. The power of AI is that it can reveal what matters to consumers, and those metrics can be used to shape how employees are evaluated. Nevertheless, it’s also critical to study the long-term effects of those practices.
Ultimately, Islam says AI’s greatest advantage in performance reviews is its immediacy. Instead of reactive quarterly reviews, leaders will be able to coach team members while projects are still underway, allowing them to impact outcomes on much shorter timelines.
When applied correctly, artificial intelligence can be a powerful tool for building exceptional teams. But the key to maximizing these strategies is always keeping humans in the mix. Looking at the numbers in context and continuously examining the results ensures that organizations can craft the teams they want without sacrificing company values.
We’ll keep you in the know about the latest US-Asia business news and trends.
Lo mantendremos informado sobre las últimas noticias y tendencias comerciales entre Estados Unidos y China.