AI requires more than data mastery. Companies also face many managerial challenges common to other technology-driven transformations. These include vision and leadership, openness and the ability to change, long-term thinking, close alignment between business and technology strategy, and effective collaboration. Companies also face AI-specific challenges.
Most basic—and most important—is developing an intuitive understanding of AI. J.D. Elliott, director of enterprise data management at TIAA, a Fortune 100 financial services organization with nearly $1 trillion in assets under management, said, “I don’t think that every frontline manager needs to understand the difference between deep and shallow learning within a neural network. But I think a basic understanding that—through the use of analytics and by leveraging data—we do have techniques that will produce better and more accurate results and decisions than gut instinct is important.”
A second challenge is organizing for AI. Adopting AI broadly will likely place a premium on soft skills and organizational flexibility. There are different models—such as centralized, distributed, and hybrid—but ultimately, a hybrid model emphasizing cross-functional collaboration may make the most sense. “We have to bring in people from different disciplines. And then, of course, we need the machine learning and AI people,” said Wells Fargo’s Sudjianto. “Somebody who can lead that type of team holistically is very important.” Organizational flexibility is a centerpiece of all the AI models. For large companies, the culture change required to implement AI will be daunting, according to several of these executives.
A third challenge is figuring out how humans and computers can build off each other’s strengths. This is not easy. Amy Hoe, chief technology and operations officer of insurer FWD Group, says that companies need privileged access to data (which, according to our findings, many don’t have), they need to put in place flexible organizational structures, and they must learn how to make people and machines work effectively together. All of which means tough cultural changes for both company and employee.
Managers also need to realize that employing AI goes beyond improving upon the status quo. The real hard work is to understand the potential shift of entire value pools—as is expected in the health care industry, for example—and how to build sustainable competitive advantage in a changing environment. (See “Competing in the Age of Artificial Intelligence,” BCG article, January 2017, and “Putting Artificial Intelligence to Work ,” BCG article, forthcoming.)