Today, artificial intelligence technologies (AI) add significant complexities to organizational learning, performance, and change, and these technologies are proliferating across all industries at rapidly increasing rates (West, 2018). However, most organizations do not understand how to make sense of AI (Brynjolfsson & McAfee, 2017), and the scholarship best suited to guide organizations' learning with AI is at a nascent stage (von Krogh, 2018). To address the practical challenge and scholarly gap, this case study examined one biomedical research funding organization's learning regarding AI. The study used Weick's (1979) enactment theory (ET) to unpack the core dynamics of the organization's actions and reflections in learning about AI--as posited in Schwandt and Marquardt's (2000) organizational learning systems model (OLSM). The study was guided by the following research question: How does an organization focused on funding biomedical research use action and reflection processes while learning about AI? This study demonstrated that ET and the OLSM are useful for guiding research and practice regarding AI. Six primary conclusions emerged: (1) dialogue is the nucleus of organizational learning; (2) dialogue links the levels of learning in a collective system; (3) organizational learning includes both orderly and dynamic processes; (4) collaboration increases organizational learning efficacy; (5) reflections must be probed/ stimulated for organizational learning to occur; and (6) organizational learning produces outcomes at multiple levels of analysis and at different time intervals. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]