February 03, 2021
February 03, 2021
Contributor: Laurence Goasduff
Few organizations successfully move their AI model prototypes into production, and failing to manage data dependencies is a major gap in the process.
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.
Read more: 5 Habits of Organizations With Successful AI
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.
Address AI operationalization by developing three operations management competencies: ModelOps, DataOps and DevOps practices.
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Recommended resources for Gartner clients*:
Operational AI Requires Data Engineering, DataOps and Data-AI Role Alignment
*Note that some documents may not be available to all Gartner clients.