The field of artificial intelligence (AI) is finally yielding valuable smart devices and applications that do more than win games against human champions. According to a report from the Frederick S. Pardee Center for International Futures at the University of Denver, the products of AI are changing the competitive landscape in several industry sectors and are poised to upend operations in many business functions.
So, what do managers need to know about AI?
and the Boston Consulting Group have joined forces to find out. Our new research initiative, Artificial Intelligence & Business Strategy, explores the most important business opportunities and challenges from AI.
Artificial intelligence covers a diverse set of abilities, much like Howard Gardner’s breakdown of human intelligence into multiple intelligences. These abilities include deep learning, reinforcement learning, robotics, computer vision, and natural language processing. The first report from Stanford’s One Hundred Year Study on Artificial Intelligence — “Artificial Intelligence and Life in 2030” — lists 11 applications of AI altogether. Each of these represents narrow AI, which is defined as a machine-based system designed to address a specific problem (such as playing Go or chess) by refining its own solution using rules and approaches not specifically programmed by its maker.
More general AI refers to a system that can solve many types of problems on its own and is self-aware. No such general AI system currently exists (at least none have made it into public view). Two reports — the White House Office of Science and Technology Policy’s 2016 report called “Preparing for the Future of Artificial Intelligence” and the Stanford paper mentioned earlier — offer more detail on the various types of narrow AI.
In a forthcoming article, slated to be published in February 2017, Ajay Agrawal, Joshua Gans, and Avi Goldfarb provide a managerial perspective of AI and argue that the business value of AI consists of its ability to lower the cost of prediction, just as computers lowered the cost of arithmetic. They note that when looking to assess the impact of radical technological change, one should determine which task’s cost it is reducing. For AI, they argue, that task is prediction, or the ability to take the information you have and generate information you don’t have.
From this perspective, the current wave of AI technologies is enhancing managers’ ability to make predictions (such as identifying which job candidates will be most successful), and that the most valuable worker skills will continue to involve judgment (such as mentoring, providing emotional support, and taking ethical positions). Humans have more advanced judgment skills than computers, a state of affairs that will continue into the near future. One implication: Managerial skill sets will need to adjust as prediction tasks are given over to computers. More generally, the implications of this trend will have an impact far beyond individual skill sets.
AI is already having an effect on the composition and deployment of workforces in a variety of industries. In their forthcoming article, Agrawal and colleagues point out that at the start of the 21st century, the set of recognized prediction problems were classic statistical questions, such as inventory management and demand forecasting, but over the last 10 years, researchers have learned that image recognition, driving, and translation may also be framed as prediction problems.