Employee surveys have dominated employee listening for so long now that we have almost learned to overlook how unsatisfactory they really are.
If you’ve run into questions such as “On a scale of 1 to 5, do you feel that your contributions are appreciated?” where the real answer is a complex “It depends” then you know what I mean.
AI tools, however, offer the opportunity to finally change the way we do employee listening.
Let’s examine what they might do:
AI superpower 1: Access to far more employee listening data
AI can potentially review emails, instant messaging, and even recordings of phone calls and meetings. Privacy issues aside for the moment, AI employee listening tools could gain insight from a vastly better dataset than we get from surveys.
AI superpower 2: Real-time analysis
The superpower of AI listening is that it can take huge streams of text and rapidly make sense of them. This means you can get insights on sentiment or issues in real-time. If there is a problem managers can act as it emerges – not months or years later.
AI superpower 3: Follow-up questions
AI is smart enough to ask relevant follow-up questions. If the employee listening AI asks an employee “Do you have the tools you need?” and the person says “No,” it can dig more deeply into what tools are missing and why.
Of course, nothing new comes along in HR without generating its own set of problems.
If we develop excellent AI-based employee listening tools, we may run into the following problems:
More than anything else, employee listening programs (AI or otherwise), will be most effective if they are developed with a clear idea of why the data is being collected and how it will be acted upon.
For example, AI-based employee listening tools could be used to:
In fact, almost any issue you are concerned about could potentially be monitored by a sufficiently advanced AI tool – if it’s given access to the right data.
Let’s imagine that instead of employee surveys, you were given a huge team of psychologists who could go around the organization listening to employees and reporting back any significant findings.
In a sense, AI can do just that.
Perhaps we will one day look back on the employee listening techniques we have now and shake our heads in wonderment at how little value they provided.
The whole world of employee listening may evolve into something quite different, not concerned that the average satisfaction of a group of 100 people had gone from 3.2 to 3.4, but instead picking up on very specific problems with small groups or even individuals and helping to address them.
That new world of employee listening is almost upon us.
