Summary. AI initiatives at many organizations are too small and too tentative. They never get to the only step that can add economic value—being deployed on a large scale. Testing the waters may deliver valuable insights, but it probably won’t be enough to achieve true…

If you ask someone to name a company that’s putting artificial intelligence at the center of its business, you’ll probably hear a predictable list of technology powerhouses: Alphabet (Google), Meta (Facebook), Amazon, Microsoft, Tencent, and Alibaba. But at legacy organizations in other industries many leaders feel that it’s beyond the capabilities of their companies to transform themselves using AI. Because this technology is relatively new, however, no company was powered by AI a decade ago, so all those that have been successful had to accomplish the same fundamental tasks: They put people in charge of creating the AI; they rounded up the required data, talent, and monetary investments; and they moved as aggressively as possible to build capabilities.

Easier said than done? Yes. At many organizations AI initiatives are too small and too tentative; they never get to the only step that can add economic value—deploying a model on a large scale. In a 2019 survey conducted by MIT Sloan Management Review and Boston Consulting Group, seven out of 10 companies reported that their AI efforts had had minimal or no impact. The same survey showed that among the 90% of companies that had made some investment in AI, fewer than 40% had achieved business gains over the previous three years. That’s not surprising: A pilot program or an experiment can take you only so far.