Data and analytics leaders know that data and platform capabilities and the correct application of data and AI skills deliver successful AI applications. However, the majority of organizations miss the critical collaboration required across data management and AI disciplines when organizing these roles. Only 1 in 10 organizations are able to get 75% or more of their AI model prototypes into production, according to the Gartner AI in Organizations Survey. The survey also revealed that several barriers prevent organizations from successfully moving AI applications beyond prototypes. The survey revealed data dependency as a high barrier for operational AI. To mitigate this key dependency, data and analytics leaders must establish interdisciplinary practices across data management and AI.

Data and analytics leaders know that data and platform capabilities and the correct application of data and AI skills deliver successful AI applications. However, the majority of organizations miss the critical collaboration required across data management and AI disciplines when organizing these roles. Only 1 in 10 organizations are able to get 75% or more of their AI model prototypes into production, according to the Gartner AI in Organizations Survey. The survey also revealed that several barriers prevent organizations from successfully moving AI applications beyond prototypes. The survey revealed data dependency as a high barrier for operational AI. To mitigate this key dependency, data and analytics leaders must establish interdisciplinary practices across data management and AI.