Artificial intelligence seems to be on the brink of a boom. It’s now guiding decisions on everything from crop harvests to bank loans, and uses like totally automated customer service are on the horizon. This HBR article explores the organization practices many leading companies have adopted to deploy AI successfully..
Often leaders simply ask, “What organizational model works best?” and then, after hearing what succeeded at other companies, do one of three things: consolidate the majority of AI and analytics capabilities within a central “hub”; decentralize them and embed them mostly in the business units (“the spokes”); or distribute them across both, using a hybrid (“hub-and-spoke”) model.
Key tasks—setting the direction for AI projects, analyzing the problems they’ll solve, building the algorithms, designing the tools, testing them with end users, managing the change, and creating the supporting IT infrastructure—can be owned by either the hub or the spoke, shared by both, or shared with IT.
When a company is early in its AI journey, it often makes sense for analytics executives, data scientists, data engineers, user interface designers, visualization specialists who graphically interpret analytics findings, and the like to sit within a hub and be deployed as needed to the spokes. When they need to innovate rapidly, some companies put more gray-area strategy and capability building in the hub, so they can monitor industry and technology changes better and quickly deploy AI resources to head off competitive challenges.
By concentrating its data scientists, engineers, and many other gray-area experts within the hub, the company ensured that all business units and functions could rapidly access essential know-how when needed.
