How to Actually Drive AI Adoption Across a Large Enterprise

AI adoption in large enterprises faces challenges in moving from pilot projects to widespread production, often resulting in unstructured data and high costs without clear returns on investment. The traditional method of deploying forward-deployed engineers (FDEs) can falter as these engineers often leave without imparting sustainable knowledge. Instead, a balanced approach involving internal champions who understand organizational context and are motivated to share insights has proven effective. This model integrates people, process, and product to create a reinforcing cycle of adoption. Ona's strategy involves large-scale presentations to generate interest, followed by targeted workshops to foster power users who become key in spreading AI adoption. The platform's features, like projects that simplify setup and automations that save time, help scale adoption efficiently. Security measures ensure safe scaling, essential for industries with stringent regulations. Successful adoption is characterized by exponential user growth and increased engagement, driven by a partnership approach that adapts to evolving needs.