7 years ago
The Business of Artificial Intelligence—What It Can and Cannot Do for Your Organization

Originally published in Harvard Business Review

For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine. Each one catalyzed waves of complementary innovations and opportunities. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models.

The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) — that is, the machine’s ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it’s given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own.

Why is this such a big deal? Two reasons. First, we humans know more than we can tell: We can’t explain exactly how we’re able to do a lot of things — from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn’t automate many tasks. Now we can.

Second, ML systems are often excellent learners. They can achieve superhuman performance in a wide range of activities, including detecting fraud and diagnosing disease. Excellent digital learners are being deployed across the economy, and their impact will be profound.

In the sphere of business, AI is poised have a transformational impact, on the scale of earlier general-purpose technologies. Although it is already in use in thousands of companies around the world, most big opportunities have not yet been tapped. The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck now is in management, implementation, and business imagination.

Like so many other new technologies, however, AI has generated lots of unrealistic expectations. We see business plans liberally sprinkled with references to machine learning, neural nets, and other forms of the technology, with little connection to its real capabilities. Simply calling a dating site “AI-powered,” for example, doesn’t make it any more effective, but it might help with fundraising. This article will cut through the noise to describe the real potential of AI, its practical implications, and the barriers to its adoption.

Click here to continue reading this article at Harvard Business Review.

ABOUT THE AUTHORS

Erik Brynjolfsson (@erikbryn) is the director of MIT’s Initiative on the Digital Economy, the Schussel Family Professor of Management Science at the MIT Sloan School of Management, and a research associate at NBER. His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets. At MIT he teaches courses on the economics of information and the Analytics Lab.

Brynjolfsson was among the first researchers to measure IT’s productivity contributions and the complementary role of organizational capital and other intangibles. His research provided the first quantification of online product variety value, known as the “long tail,” and developed pricing and bundling models for information goods. He earned his AB and his SM in applied mathematics and decision sciences at Harvard and his PhD in managerial economics at the Sloan School.

Brynjolfsson is the author of several books, including, with Andrew McAfee, Machine, Platform, Crowd: Harnessing Our Digital Future (2017) and the New York Times best seller The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (2014). You can find his papers here.

Andrew McAfee (@amcafee), a principal research scientist at MIT, studies how digital technologies are changing business, the economy, and society. With Erik Brynjolfsson he coauthored Machine, Platform, Crowd: Harnessing Our Digital Future (2017) and The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies(2014), which was a New York Times best seller and was shortlisted for the Financial Times/McKinsey Business Book of the Year Award. McAfee writes academic papers, a blogfor the Financial Times, and articles for publications including Harvard Business Review, the Economist, the Wall Street Journal, and the New York Times. He has talked about his work on Charlie Rose and 60 Minutes; at TED, Davos, and the Aspen Ideas Festival; and in front of many other audiences.

McAfee was educated at Harvard and MIT, where he is a cofounder of the institute’s Initiative on the Digital Economy.