MIT Sloan Management Review Article on Achieving Return on AI Projects

Bringing the benefits of artificial intelligence into a company requires good working relationships between the data team and the business units — and a clear focus on tangible value.

Companies embarking on AI and data science initiatives in the current economy should strive for a level of economic return higher than those achieved by many companies in the early days of enterprise AI. Several surveys suggest a low level of returns thus far, in part because many AI systems were never deployed: A 2021 IBM survey, for instance, found that only 21% of 5,501 companies said they had “deployed AI across the business,” while the remainder said they are exploring AI, developing proofs of concept, or using pre-built AI applications. Similarly, a VentureBeat analysis suggests that 87% of AI models are never put into production. And a 2019 MIT Sloan Management Review/Boston Consulting Group survey found that 7 out of 10 companies reported no value from their AI investments. This makes sense: If there is no production deployment, there is no economic value.

About the Author

Thomas H. Davenport (@tdav) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting professor at Oxford’s Saïd Business School, and a fellow of the MIT Initiative on the Digital Economy. Ren Zhang is the chief data scientist for BMO Financial Group, a member of the Business of Data’s global advisory board, and a mentor to the Creative Destruction Lab.

Learn more about MIT SMR.