Technology is disrupting the workplace. For better or for worse, it’s changing the way we work at an alarmingly fast pace that only seems to pick up speed by the second. Automation, artificial intelligence, machine learning – we’ve been tossing these terms around for a while. But what’s different about them today is the direct influence they have on the employee/manager experience. They are creating new ways of interacting at work.
How? Chatbots. A new collaboration with Microsoft brings a Workforce Dimensions-powered chatbot within Microsoft Teams to managers and employees, helping them easily navigate day-to-day concerns. For example, the chatbot provides immediate answers to everyday workforce management questions. Employees can ask it to request time off or check vacation hours in real-time while managers can use it to identify who is approaching overtime or who has not clocked in for the day. Read more about the announcement here.
From Eliza to Parry, Siri, and Alexa, a lot has changed over the years. And chatbots are only becoming a more attractive method of improving the user experience. In fact, Gartner recently predicted that 50% of companies will invest more on bots and chatbots per year than on mobile.
So, let’s back up for a moment. What is a chatbot? Is it artificial intelligence or machine learning? The simple answer is that it’s both. A chatbot is a form of artificial intelligence, and inside that artificial intelligence is the machine learning that helps the bot create the algorithms necessary to produce answers to our questions.
Dr. Thomas Walsh, director of data science at Kronos, describes the difference between artificial intelligence and machine learning in the HR Bartender article Everything #HR Needs to Know About Machine Learning.
“Machine learning (ML) and artificial intelligence (AI) can be tough fields to pin down,” Walsh says. “Artificial intelligence refers to a collection of subfields that solve complex problems associated with human intelligence and/or interacting with the world. These subfields include computer vision, natural language processing, robotics, and machine learning. Machine learning generally covers methods that build models of complex data.”
While there are different approaches to machine learning, Dr. Walsh notes that being data-driven is the commonality between them all. The more data that goes in, the more accurate outputs the machine learning algorithms will produce.
Is all of this positive or negative? The highly disputed question about advancements in technology prevails. The Workforce Institute at Kronos conducted research on the top concerns about artificial intelligence in the workplace in a recent study. Their results revealed that participants are primarily concerned with losing their jobs or being replaced by automated processes. On the opposite end of the spectrum, the fourth most common concern is that automated processes will create more work for people because they will have to be monitored and checked. What’s your take? Good or bad? Cast your vote in the ongoing Workforce Institute poll.