T-Mobile’s unparalleled CEO John Legere is known as one of the most brash, foul-mouthed, and outspoken leaders of a major corporation today.  He’s also one of the most successful, doubling the number of customers and increasing the company’s stock price well over 200% in just the last four years.

Recently, Legere shared the leadership advice he likes to dole out.

John Legere quote"It’s kind of fun at my age to go back and talk to business-school people," Legere said. "I tell them, ‘I can summarize everything you need to know to lead a major corporation. Are you prepared to write this down?’ And then they get all ready. I tell them I can summarize how I succeed as a leader: Listen to your employees, listen to your customers, shut the f*** up, and do what they tell you."

His blunt, simple advice has merit.  "Listening is an overlooked leadership tool," wrote Melissa Daimler, SVP, Talent Acquisition & Development at WeWork, in Harvard Business Review.  And there’s a big reason why:  because management generally doesn’t have the time to do it.

This is where technology is coming to the rescue.  Most years I attend HR Tech, the main tradeshow exhibiting the latest in human capital technologies.  Each year, there seems to be a hot term or phrase on the lips of every attendee and vendor.  This year, there were two phrases that stood out to me being used – and in many cases misused – by many of the vendors I, and my team, talked with:

  1. Natural Language Processing (NLP)
  2. Machine Learning (ML)

While these terms weren’t always used correctly by vendors who were desperate to showcase why their product is unique, I think both are going to play significant roles in HR for years to come.

In case you aren’t familiar with NLP, very simply it is a way for computers to understand and derive meaning from human language.  NLP is used to analyze text, allowing machines to understand what human beings are saying and writing.  This is especially important for sentiment analysis, which companies are clearly interested in for both employees and customers. 

Machine learning provides computer applications with the ability to "learn" without being explicitly programmed. As a result, these applications can change when exposed to new data, allowing them to improve over time as more data is entered.

Combined, they have the ability to revolutionize what I think is one of the biggest dinosaurs in human capital:  the employee engagement survey.

Recently, I’ve been involved with a new platform using a combination of NLP and ML to provide better insights into employee engagement data that we’ve collected from several companies.  The results have been fascinating. 

The typical engagement survey has a 1-5 or 1-10 Likert scale for 95% of the questions asked, and often an area for open text at the end.  The magic is in that final 5%.  While one employee’s definition of a "4" is likely different than another’s, what’s written in free form text is often much more valuable.  In every single instance where we’ve entered the text collected in an engagement survey, we’ve been able to generate new, and more interesting, insights than the quantitative analysis was able to provide.

The reality is that most companies don’t take the time to read or summarize the free form text because it’s impossibly time consuming.  The most some companies will do is create a word cloud, which simply identifies the most used words.  However, that doesn’t help understand the context in which those words are used.  Thus, the whole concept of sentiment analysis is lost.

This is where NLP and ML, when combined, are going to provide a significant impact to engagement surveys, and many other types of surveys, worldwide.  This impact will be so profound that I can easily envision a day when we don’t even bother with Likert scales. 

Most importantly, it will allow busy executives and managers to do what they should have been doing all along:  shutting the f*** up and listening.

Read more 2017 talent predictions by other thought leaders.