AMBITION AND EXECUTION
There are also large gaps between leaders and laggards. Among leaders, three-quarters have identified business cases for AI. About 80% say that senior leaders are onboard. The biggest hurdles at these companies are hiring and developing talent and establishing priorities for AI investments; they are also starting to worry about security issues related to AI. Laggards, on the other hand, have not identified business cases. More than 50% report that their senior leaders are generally not involved in AI, and most have yet to encounter the difficulties of sourcing AI talent.
The differences in adoption can be striking, particularly within the same industry. Ping An Insurance Company of China, one of that country’s largest insurers, employs 110 data scientists and has launched about 30 CEO-sponsored AI initiatives that support, in part, its vision “that technology will be the key driver to deliver top-line growth for the company in the years to come,” says the company’s chief innovation officer, Jonathan Larsen. Elsewhere in the insurance industry, AI initiatives are at the other end of the spectrum, limited to such efforts as “experimenting with chatbots,” as a senior executive at a large Western insurer described his company’s AI program.
DATA, TRAINING, AND ALGORITHMS
Thus, even if the organization owns the data it needs, fragmentation across multiple systems can hinder the process of training AI algorithms. Agus Sudjianto, executive vice president of corporate model risk at Wells Fargo, put it this way: “A big component of what we do is dealing with unstructured data, such as text mining, and analyzing enormous quantities of transaction data, looking at patterns. We work on continuously improving our customer experience as well as decision making in terms of customer prospecting, credit approval, and financial crime detection. In all these fields, there are significant opportunities to apply AI, but in a very large organization, data is often fragmented. This is the core issue of the large corporation—dealing with data strategically.” Less than half of our survey respondents said that their organization understands the data needs of algorithms or the processes required to train algorithms.
MAKE VERSUS BUY
The CIO of a large pharma company described the products and services that AI vendors provide as “very young children.” The AI tech suppliers “require us to give them tons of information to allow them to learn,” he said. “The amount of effort it takes to get the AI-based service to age 17 or 18 or 21 does not appear worth it yet. We believe the juice is not worth the squeeze.” Using AI for competitive advantage requires companies to build up their internal skills.
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.)
AI AND JOBS
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