  In popular usage, Data Gravity refers to the ability of a body of data to
attract applications, services and other data. In this work we introduce a
broader concept, "Digital Gravity" which includes not just data, but other
elements of the AI/ML workflow. This concept is born out of our recent
experiences in developing and deploying an AI-based decision support platform
intended for use in a public health context. In addition to data, examples of
additional considerations are compute (infrastructure and software), DevSecOps
(personnel and practices), algorithms/programs, control planes, middleware
(considered separately from programs), and even companies/service providers. We
discuss the impact of Digital Gravity on the pathway to adoption and suggest
preliminary approaches to conceptualize and mitigate the friction caused by it.
