AI development in an enterprise setup is challenging primarily because of the fact that data volumes are never sufficient for the data hungry Deep Learning model training. Data collection & model training goes hand in hand and is an iterative process which is usually ever evolving. Particularly in case of Autonomous driving the scale of data is enormous (in Petabytes/week). Incremental data collection and dealing with all aspects of data management can be very complex without a winning strategy. Giant corporates like “Bosch” intend to leverage these huge data lakes for AI enablement of numerous use cases across multiple domains. And hence it requires a good data strategy. I would talk about a “data loop” strategy, a generalized overview of how AI based tools & processes enable the data loop strategy for large scale product development with a synopsis of autonomous driving domain.