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Artificial intelligence (AI) efforts can fail to move out of the lab if organizations don't carefully manage access to data throughout the development and production life cycle. Advanced analytics and AI promise to generate insights that will help organizations stay competitive. Their ability to do that is heavily dependent on the availability of good data, but sometimes organizations just don't have the data to make AI work. Here, how organizations move their AI initiatives from R&D, lablike settings into production and the problems they encounter in doing so are examined. The research is based on interviews with key AI leaders and informants in six North American companies of different sizes and operating in different industries.