Here is an excerpt from an article written by Ajay Agrawal, Joshua Gans, and Avi Goldfarb for Harvard Business Review and the HBR Blog Network. To read the complete article, check out the wealth of free resources, obtain subscription information, and receive HBR email alerts, please click here.
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There is no shortage of hot takes regarding the significant impact that artificial intelligence (AI) is going to have on business in the near future. Much less has been written about how, exactly, companies should get started with it. In our research and in our book, we begin by distilling AI down to its very simplest economics, and we offer one approach to taking that first step.
We start with a simple insight: Recent developments in AI are about lowering the cost of prediction. AI makes prediction better, faster, and cheaper. Not only can you more easily predict the future (What’s the weather going to be like next week?), but you can also predict the present (what is the English translation of this Spanish website?). Prediction is about using information you have to generate information you don’t have. Anywhere you have lots of information (data) and want to filter, squeeze, or sort it into insights that will facilitate decision making, prediction will help get that done. And now machines can do it.
Better predictions matter when you make decisions in the face of uncertainty, as every business does, constantly. But how do you think through what it would take to incorporate a prediction machine into your decision-making process?
In teaching this subject to MBA graduates at the University of Toronto’s Rotman School of Management, we have introduced a simple decision-making tool: the AI Canvas. Each space on the canvas contains one of the requirements for machine-assisted decision making, beginning with a prediction.
To explain how the AI Canvas works, we’ll use an example crafted during one of our AI strategy workshops by Craig Campbell, CEO of Peloton Innovations, a venture tackling the security industry with AI. (It’s a real example, based on a product that Peloton is commercializing, called RSPNDR.ai.)
Over 97% of the time that a home security alarm goes off, it’s a false alarm. That is, something other than an unknown intruder (threat) triggered it. This requires security companies to make a decision as to what to do: Dispatch police or a guard? Phone the homeowner? Ignore it? If the security company decides to take action, more than 90 out of 100 times, it will turn out that the action was wasted. However, always taking an action in response to an alarm signal means that when a threat is indeed present, the security company responds.
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Ajay Agrawal is the Peter Munk Professor of Entrepreneurship at the University of Toronto’s Rotman School of Management and Research Associate at the National Bureau of Economic Research in Cambridge, MA. He is founder of the Creative Destruction Lab, co-founder of The Next AI, and co-founder of Kindred. He is the co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business School Press, April 2018).
Joshua Gans is the Jeffrey S. Skoll Chair of Technical Innovation and Entrepreneurship at the University of Toronto’s Rotman School of Management and serves as chief economist in the Creative Destruction Lab. He is the co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business School Press, April 2018). His book, The Disruption Dilemma, is published by MIT Press.
Avi Goldfarb is the Ellison Professor of Marketing at the Rotman School of Management, University of Toronto. He is also a Research Associate at the National Bureau of Economic Research, Chief Data Scientist at the Creative Destruction Lab, and Senior Editor at Marketing Science. He is the co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business School Press, April 2018).