According to the report, content access, governance, and platform flexibility are emerging as key factors that distinguish AI leaders from laggards in the enterprise sector. A survey of 1,640 IT decision makers across the US, UK, France, and Japan found that the combined share of organizations describing themselves as advanced or leading edge soared from 8% to 64% over the past year.
The share of organizations calling themselves early stage or not yet started collapsed from 53% to just 9% during the same period. Olivia Nottebohm, COO of Box, says the swing is largely due to how enterprises are now organizing their AI use rather than to any single technical breakthrough.
Nottebohm explains that enterprises have moved from standalone experimentation that lived at the individual level into systematized, integrated agentic operations, agents that are in production and can be used in a repeatable manner. That’s where the impact is coming from.
Half of leading-edge companies reported AI-driven ROI above 25%, compared with just 11% of early-stage companies. Nottebohm says the real differentiator was not whether companies adopted AI, but how rigorously they integrated and managed it. What separates the leading edge is the operating muscle they’ve built: the right teams to deploy agents, formal governance to control them, and consistency in the content layer those agents work from.
Content access is the biggest barrier to enterprise AI ROI, with 96% of organizations saying agents need access to company-specific content, yet only 36% have connected agents to trusted content across many use cases. It’s an issue of trust rather than raw capability. Agents are only as good as the content they can reference, and only as safe as the security around it.
Getting the content layer right has a second benefit beyond safety, since it’s also what finally lets agents work across departments that previously operated in isolation from one another. Nottebohm notes that this is a key aspect of specialized architecture in AI systems.
Nearly half of all organizations say they have already experienced an AI-related data exposure incident, with 60% of leading-edge companies facing greater exposure from more agents and connected systems. However, those incidents function as a forcing mechanism rather than a setback, Nottebohm says. Better governance is actually what lets them move faster, she explains.
The share of organizations reporting established or advanced governance frameworks rose from 24% in 2025 to 73% this year, but real gaps remain in instrumentation. Only 39% have visibility across sanctioned and unsanctioned AI use, 34% have formal standards for how agents access company data, and 27% still describe their governance as ad hoc.
Nottebohm notes that enterprises need to make the transition from governance that’s retrofitted from human workflows to governance that’s built specifically for agents from the start. That means tracking what an agent has touched, whose permissions were applied, and which sources were used, and all of that is now shaping how governance gets applied.
Enterprises are avoiding lock-in to a single AI vendor, with 68% saying they’re concerned about depending on a single AI provider. The average number of officially adopted AI tools has climbed to 3.3, and 79% now consider it important or critical that agents operate headlessly, connecting directly to systems and APIs without a human interface in between.
A flexible architecture is built on platform interoperability, Nottebohm says. It runs on multiple models, operates headlessly, and keeps every part of the AI stack swappable, so organizations don’t have to bet on which individual tool wins.
They can also consider models like the MiniMax M3 which provides a cost-effective solution.
Over the next three years, businesses should prioritize organizing, classifying, and cleaning up unstructured content, actively hiring and building teams around emerging roles, and adopting a hybrid token compute budget model. And right now, it’s easy to get up to speed fast.
Nottebohm says, “You don’t have to start at early maturity and slowly work your way up. If you build in the governance, the content layer, and the multi-model system from the start, you can enter as a leading company and capture that same outsized impact.”
Furthermore, recent actions by companies like Anthropic demonstrate the importance of responsible AI development and deployment.
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