AI for the Rest of Us

As an expert in AI, Joshua Gans spends a lot of time separating hype from reality. Currently a Professor at the University of Toronto’s Rotman School of Management, he teaches MBA students networking and digital marketing strategy—including how companies can use technology to compete through innovation. We sat down with Joshua to discuss the new […]

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There’s a lot of talk in today’s market around the possibilities of AI. But are there concrete examples of AI’s benefits today in today’s business world?

I’ll admit it: AI is seeing a lot of hype right now. In the book, we take a different approach to what AI developments over the last 10 years have been all about. We’re not talking about general intelligence—replacing humans and all their cognitive abilities—but just one facet that hasn’t been exploited previously, which is our ability to predict.

Normally, we think about prediction in the context of forecasting. With weather, we gather historical data about wind and precipitation and other factors, and we produce a prediction about what the weather’s going to be tomorrow, and next week. But prediction isn’t always about the future. Computer vision is one example: when you give a machine an image and ask what it sees, what it’s really coming back with is a prediction. The computer is asking “What would a person think is in this picture?”

Prediction is all about making better decisions. With weather predictions, you can determine what you should wear. When you have a prediction regarding what’s in an image—take an MRI, for example—you can then make a decision on the right course of treatment. Seen in that regard, AI is kind of boring. It’s just a better statistical technique. But it is such a huge advance, that prediction itself is going to become better, faster, and cheaper. That’s going to open up a whole lot of uses for prediction that weren’t there previously.

So how does AI move from hype to real value for businesses?

One of the concerns in the back of our minds as we wrote this book was recalling what happened with the computer revolution and the internet revolution. With those, there was a lot of hype, and a lot of companies spent millions of dollars on things that weren’t really thought out. We don’t want to repeat that mistake. What we instead encourage services to do is to say, “If prediction is going to help better decision making, let’s take workflows from our organization and break them down into all the decisions that have to be made in order to go from an input to an output, and in that process, identify where the sources of uncertainty are.” It’s there that you’ll start to understand where AI might be useful in reducing that uncertainty and making better decisions.

This is a process that happened previously with computers. People broke down the workflows and tasks, and worked out where computers were going to be useful. It led to this movement, 20-25 years ago, called reengineering. What we’re suggesting is that there’s an opportunity to do that again.

In the book, you write that everyone has a lightbulb moment with AI—a moment where it clicks for them. Will every industry need to see their own lightbulb to embrace the technology?

Some people are ahead of the curve, asking, “Can AI help our business?” In other situations, it does need to be more tangible.

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