When Computers Learn About Humans

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Being a child of the 1960s, I was enamored of Star Trek. Among the many future wonders to behold? Computers that could conduct an informed conversation with humans.

Fast forward to the present day, and we’re getting accustomed to intelligent assistants in our homes and on our phones. Better yet, intelligent assistants are now poised to show their value in the workplace.

And by teaching computers how to converse with people—instead of the other way around—we’re on the verge of huge gains in efficiency and productivity.

Eliminating the Productivity Tax

For decades and decades, the equation has been the same: Computers could make us more productive, but we had to spend time learning how to use them. Yes, that’s gotten easier over time, but it’s still on us to learn about them, rather than the other way around.

As anyone who’s had to navigate a customer service phone system knows, automation doesn’t always result in the best customer experience. You’re looking for a specific answer, but you have to make choice after choice to get to your goal. Or lose your cool and yell “Operator!” repeatedly.

Just let me talk to a real person, please?

Human conversation, whether via voice or text, is the natural, intuitive way that we communicate. We don’t have to learn how to do it. And thanks to huge advances in compute power and machine learning, we’ll soon see intelligent digital assistants springing up everywhere.

Let’s say you’re an employee at a large company where most tasks are self-service. You’d like to order a laptop, or maybe book a larger conference room, but you’re not sure how to do it. After all, it’s not something you do every day.

So you go looking for someone to give you a hint or two. Compare that experience with simply asking an intelligent digital assistant: “I’d like to order a new laptop.”

Or maybe you’re responsible for some aspect of the business, and you’re getting fed mountains of raw data on how various aspects are performing. Your eyes glaze over. Advanced analytics were never your strong suit, so you go looking for someone with more expertise.

How nice it would be to have your intelligent assistant spot patterns and correlations in the data, and give you a hint on where to dig deeper.

The more significant impact will be on how we interact with our customers. We’d all like to be greeted by a friendly voice or text message along the lines of “How can I help you today?” Intelligent assistants can navigate the customer interaction like a trained human specialist, asking questions and redirecting the conversation toward a positive outcome as quickly as possible.

And if for some reason, the intelligent assistant couldn’t progress the conversation quickly enough, there’d be a warm handoff to a real person, with all the context provided.

Behind the Scenes

Companies can put the new crop of intelligent assistants to work quickly, and with surprising ease. There’s usually no need to change your existing applications. It’s a layer over what you have—or might have in the future.

Better yet, intelligent assistants are adept at making connections across unconnected systems. Marketing, sales, and support. Finance, HR, and supply chain. Much like skilled people will hop from one departmental system to another to get the full picture.

The only challenging part is diagramming all of the different ways you’d like the conversation to flow. Fortunately, embedded analytics will tell you if you’ve done a good job or where you might need to improve. Thanks to machine learning, the intelligent assistant can also improve on its own over time, usually by making better recommendations.

Stepping back, the equation seems fairly simple to me: Make it easier to get things done, and more will get done in less time. That’s the promise of intelligent assistants in the workplace.

Now, if we could just figure out how to build a transporter beam….