Box Survey Reveals Enterprise AI Leaders Outperform Peers Through Content Access and Governance

Box survey: Why enterprise AI leaders are outperforming their peers

Content access, governance, and platform flexibility are emerging as the dividing lines between AI leaders and laggards, according to the new State of AI in the enterprise report from Box, which surveyed 1,640 IT decision makers across the US, UK, France, and Japan. One of the report's major findings is the speed of the shift: the combined share of organizations describing themselves as advanced or leading edge soared from 8% to 64% just over the past year, while the share calling themselves early stage or not yet started collapsed from 53% to just 9%. Eighty percent of organizations reported a notable return on their AI investment, defined in the survey as an improvement of at least 10%, and more than half saw measurable business impact within six months of getting a project approved. The swing is largely due to how enterprises are now organizing their AI use rather than to any single technical breakthrough, says Olivia Nottebohm, COO of Box. "We've 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," Nottebohm says. "That's where the impact is coming from." Why AI leaders get higher ROI than early-stage companies The divide between tiers is a matter of execution. Significantly, half of leading-edge companies reported AI-driven ROI above 25%, compared with just 11% of early-stage companies, with the advanced (33%) and developing (16%) tiers falling steadily in between. But 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," she explains. "Earlier stage companies are approaching it in a much more ad hoc, experimental way, letting people play around with it without the same intent or structured design." Content access is the biggest barrier to enterprise AI ROI Content, rather than model quality, is the defining bottleneck of 2026. Ninety-six percent of organizations say 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. "We started this journey assuming enterprise AI was about access to the latest model," Nottebohm says. "But the question now is whether agents have access to the right content, and whether that content is protected, because those agents are only as good as the content they can reference, and only as safe as the security around it." Getting that 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. And while roughly a quarter of organizations point to data fragmented across systems, 24% cite difficulty integrating AI into existing systems, 21% say they lack adequate permissions and access controls, and 18% describe their content as too unorganized to make accessible at all. Among the most mature organizations, 63% now treat unstructured documents, contracts, and reports as a competitive advantage rather than dead weight sitting in a digital filing cabinet. Reducing common AI data exposure incidents Nearly half of all organizations say they have already experienced an AI-related data exposure incident. That figure rises to 60% among leading-edge companies, which may face greater exposure from more agents and connected systems -- but may also be better equipped to detect it. 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 comprehensive 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. But those incidents function as a forcing mechanism rather than a setback, Nottebohm says. "Governance used to be seen as something that slowed people down, but 93% of respondents told us better governance is actually what let them move faster," she explains. "It makes scaling AI survivable. Once content is secured and highly permissioned, you can run multiple agents across multiple processes and get a real multiplier effect." One practical consequence of that shift is that permission structures built for human employees are now being revisited with agents in mind, a process most enterprises are only partway through. "The permissions enterprises set up two years ago need to be reviewed," she explains. "Until fairly recently, people weren't setting permissions on a document with how an agent might use it in mind, but now they're much more deliberate about that. It leaves them with a whole corpus of unstructured data to go back through and either clean up or repermission." That's part of a broader move away from governance designed for people and toward governance designed for agents from the start. "Enterprises need to make the transition from governance that's retrofitted from human workflows to governance that's built specifically for agents," Nottebohm says. "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 need to avoid lock-in to a single AI vendor "The days of token-maxing are already gone," Nottebohm says. "It's now about the responsibility of delivering efficient AI. Organizations want to use the cheapest model that meets the quality bar they need, not necessarily the most expensive one, because different model families keep leapfrogging each other and companies want to preserve that choice." That means enterprises are avoiding lock-in more than ever. Sixty-eight percent say 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. It's a trend similar to the shift toward multi-cloud infrastructure, and driven by a similar reluctance to hand any one vendor outsized negotiating power. "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, and that's part of the broader shift away from defaulting to the biggest, most expensive model available." The next steps to AI success 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, where IT owns the core infrastructure and token budget while business units own the application-level spend. And right now, it's easy to get up to speed fast. "You don't have to start at early maturity and slowly work your way up," Nottebohm says. "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." 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Box CEO says frontier and specialized AI models will keep growing as enterprise adoption scales for years ahead

Most Artificial Intelligence (AI) commentary I read falls into either of two camps: everything is at peak hype, and a correction is coming, or the AI boom is just beginning, and spending will compound for decades. Box CEO Aaron Levie posted something on X (formerly Twitter) this week that I found more useful than either extreme. His argument is structural, specific, and grounded in how enterprise software actually gets adopted. And coming from someone who has spent years watching how large organizations absorb new technology, it carries weight that pure market commentary does not. The spend and token volume for both approaches will continue to go up for years to come as we're still early on both. Levie said the statement above, referring to frontier AI models and specialized lower-cost systems. "This is why the spend and token volume for both approaches will continue to go up for years to come." Box (BOX) is not a semiconductor company or a hyperscaler. It is the enterprise content management platform at the center of how companies actually deploy AI against their real business workflows. Levie sees this market from the inside. Also Read: AI CEO just made a wild prediction about AI agents What Box CEO actually said and why I think the "both will grow" thesis matters The debate playing out in technology investing right now is whether cheaper, specialized AI models eventually cannibalize spending on frontier systems from Anthropic, OpenAI, and Google. Levie answers that the framing of the debate is wrong. I also think Frontier models will always dominate brand-new use cases and complex orchestration workflows. As those use cases mature and become predictable, enterprises can then peel off workloads to cheaper open or closed models, or models specifically trained for the task. But critically, just as Levie argued in his post, doing that too early does not work. "Doing this too early in the adoption curve of any new use-case doesn't make sense as you don't know what you're optimizing for," Levie wrote. Now, my read of that framing is that it explains why enterprise AI spending does not follow the typical technology commoditization curve as quickly as bears expect. Every new AI capability that becomes commercially viable starts at the frontier model layer. That layer keeps expanding in capability, which means the frontier spend floor never actually falls. It just gets accompanied by a growing specialized spend tier underneath it. "This process can essentially run on forever as there is no end for both the benefits of frontier intelligence or tuned models." Levie wrote. Box is massively positioning itself at the center of enterprise AI workflows Levie is not just a commentator here. Box is actively building the applied AI layer he described. According to a blog from Box, the company launched Box Agent on April 2, 2026. Box Agent is a unified AI engine that leverages advanced reasoning models to search, analyze, synthesize, and generate content across enterprise files while maintaining Box's enterprise-grade security and governance controls, according to Box's Q1 FY2027 earnings. release. Box Automate, also launched this quarter, is an agentic workflow orchestration solution that dynamically routes work across people, Box Agents, and enterprise systems. These products are the commercial expression of what Levie described on X (formerly Twitter). Box is building the "applied AI layer" that evaluates enterprise workflows, selects the right model mix for each task, and eventually enables companies to train specialized models for their specific purposes. Enterprise Advanced, Box's premium AI tier launched a year ago, now accounts for 10% of total company revenue, with customers paying a 30%-40% price uplift per seat over Enterprise Plus, according to its Q4 2026 Earnings Call Transcript. That metric arriving within a year of launch is the clearest evidence that the adoption curve Levie described is already running inside Box's own customer base. Michael Short/Bloomberg via Getty Images Box's Q1 FY2027 results show the business accelerating alongside the AI argument The Q1 FY2027 results, reported May 26, provided concrete validation for the enterprise AI adoption thesis Levie outlined. * Record revenue of $305.9 million came in up 11% year over year, or 10% on a constant currency basis * Remaining performance obligations reached $1.6 billion, up 12%, with long-term RPO growing 16%, signaling customers are making multi-year AI commitments. * Non-GAAP operating margin expanded to 27.7% from 25.3% * Free cash flow of $127.7 million was up 8% * Non-GAAP EPS of $0.37 beat the prior year's $0.30 Source: Box First Quarter Fiscal 2027 Financial Results "Customers are adopting Enterprise Advanced to manage and connect their organization's unique content to AI agents, allowing them to securely build intelligent workflows, automate work, and accelerate decision-making at scale," Levie said in the earnings release. For Q2 FY2027, Box guided for revenue of approximately $319 million, up 9% year over year. Full-year FY2027 guidance calls for revenue of approximately $1.28 billion, up 9%, with a non-GAAP operating margin of approximately 28% and non-GAAP diluted EPS of $1.56. The broader implication for investors watching AI spending Box CEO Aaron Levie argues that AI spending will expand across both frontier and specialized models rather than shift from one to the other, implying a much larger AI market than many investors expect. Box is positioning itself at the enterprise AI layer, backed by its recognition as a Leader in the 2026 Gartner Magic Quadrant for Document Management and the integration of Box Agent for Gemini Enterprise with Google Cloud AI orchestration. What I find most useful about Levie's post is that he is not selling optimism. He is describing a mechanism. Levie sees enterprise AI as a continuous process of evaluating, selecting, and training models for specific workflows, with no clear end in sight. The Arena Media Brands, LLC THESTREET is a registered trademark of TheStreet, Inc. This story was originally published July 8, 2026 at 7:08 AM.