By Ridhika Basnet · AIM Media House
Enterprise adoption of AI agents is exposing challenges that go well beyond model performance, according to Box Co-founder and CEO Aaron Levie. As organisations expand the use of AI across internal operations, they are grappling with workflow redesign, data access, security, and uneven AI spending.
In a post on X last week, Levie said one of the biggest barriers to deploying AI agents is change management. Most business processes, he said, were not designed to work with autonomous agents and require significant changes across technology, data and human workflows.
He added that enterprises are prioritising efforts to make both structured and unstructured data accessible to AI agents, recognising that data quality and accessibility remain critical to successful deployments.
Levie said many organisations are also embedding engineers directly within business teams to accelerate AI adoption. The approach, similar to having an internal forward-deployed engineer, helps companies identify workflow bottlenecks early and avoid lengthy, unsuccessful AI experiments.
As AI agents begin working across multiple departments, enterprises are encountering new identity and permissions challenges. Levie said agents require their own roles and access privileges because they often need information spanning multiple business functions.
Managing those permissions securely is difficult, he noted, particularly because agents cannot independently secure or govern themselves.
He also said Anthropic's recently introduced Mythos models are exposing more sophisticated security risks by enabling attackers to chain together multiple vulnerabilities, prompting organisations to build larger patch backlogs.
AI spending varies widely across enterprises Levie highlighted significant differences in enterprise AI budgets.
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