Why Enterprise AI Adoption Is Slower Than You Think — Aaron Levie (Box) + Harrison Chase
Enterprise AI adoption lags because knowledge work lacks the verifiability, technical users, and open data access that make AI coding agents succeed.
“The big question is when and how do we get these full, agentic workflows deployed across the rest of knowledge work?”— Harrison Chase
Box CEO Aaron Levie explains to LangChain's Harrison Chase why enterprise AI adoption trails AI coding: coding agents benefit from verifiable output, hyper-technical users, and open codebase data access, while knowledge work in sales, marketing, and finance lacks all three. The core obstacle is fragmented data permissions and non-technical users who can't safely supervise agents, making the leap from chat to deployed agentic workflows the central challenge for enterprises.