Box CEO and tech thought leader Aaron Levie says he recently met with 20 enterprise AI and IT leaders and came away with insights into what everyone, especially the stock market, wants to know: how—and how fast—large US companies are adopting AI for core business functions. In a post on X, he outlined the main themes he heard.
Here’s a closer look at those key themes.
Agents move from hype to production
“Agents are clearly the big thing,” Levie wrote. “Enterprises (are) moving from talking about chatbots to agents, though we’re still very early. Coding is still the dominant agentic use-case being adopted thus far, with other categories . . . across knowledge work starting to emerge. Lots of agentic work moving from pilots and PoCs into production, and some enterprises had lots of active live use-cases.”
Recent models from Anthropic and OpenAI, including Claude Opus 4.5 and 4.6 and GPT-5.2 and 5.3, have pushed AI coding agents beyond simple code generation toward something closer to operating like junior software engineers. As trust in these tools grows among developers, enterprise decision-makers appear increasingly eager to deploy them within software teams.
From coding copilots to company-wide agents
“Agentic use-cases span every part of a business, from back office operations to client facing experiences from sales to customer onboarding workflows,” Levie wrote on
AI companies have long argued that the capabilities powering coding tools—planning, reasoning, and tool use—can extend across knowledge work. Based on Levie’s conversations, enterprise leaders are starting to act on that idea. What works in software engineering may translate to marketing, finance, and HR. That raises the specter of job displacement, but Levie suggests companies are prioritizing improved customer experience over head-count reduction.
Governance becomes the bottleneck
“Data and AI governance still remain core challenges,” Levie added. “Getting data and content into a spot that agents can securely and easily operate on remains a huge task for more organizations. Years of data management fragmentation that wasn’t a problem now is an issue for enterprises looking to adopt agents. And governing what agents can do with data in a workflow (is) still a major topic.”
