AI Agents Go Mainstream, But Access to Trusted Company Knowledge Remains a Challenge

AI agents have rapidly entered the enterprise mainstream, but the systems that provide them with trusted business context and content have not evolved at the same pace. As organizations race to deploy AI at scale, many are discovering that effective governance, connected knowledge, and flexible infrastructure are the real factors determining success.

Cloud content management company Box conducted a survey of 1,640 IT decision-makers across the U.S., U.K., France, and Japan. This study found that organizations are advancing rapidly in AI maturity, with a growing number now considering themselves advanced or leading-edge users of the technology. Over the past year, AI maturity has increased significantly across enterprises, but the number of organizations still in the early stages of adoption has declined sharply.

AI agents have already become a standard part of operations for most organizations, with businesses increasingly using them to automate tasks, support decision-making, and streamline workflows. As adoption grows, many companies are reporting measurable tangible business value, with the majority indicating that their AI investments have led to noticeable gains in productivity and overall performance.

Leading organizations use AI to drive business transformation

While many companies currently use AI to improve efficiency through automation and digital assistance, the most advanced adopters are taking a broader approach. They leverage AI to scale work beyond previous limits and enable entirely new business activities that were previously impractical or too resource-intensive.

According to the study, many organizations anticipate expanding their workforce as AI becomes more deeply embedded in business operations. AI is driving demand for new roles focused on areas such as AI operations, governance, compliance, and workflow management.

Limited access to trusted enterprise knowledge holds AI back

Organizations widely recognize that AI agents deliver the greatest value when they can access and understand an organization’s internal knowledge, data, and content. However, many businesses have yet to connect their AI systems to trusted information sources at scale, which limits the agents’ ability to provide context-aware insights.

As organizations accelerate AI adoption, concerns around data security and governance are becoming increasingly important. Many companies have already encountered AI-related data exposure incidents, which prompt increased investment in governance frameworks and risk management practices. However, significant gaps remain in areas such as monitoring AI activity, maintaining visibility into how tools are being used, and establishing clear controls over how AI systems access and interact with sensitive corporate data.

Lastly, businesses are becoming increasingly wary of relying too heavily on a single AI provider. In response, leading organizations are embracing a multi-platform approach, deploying a range of AI tools while building flexible, interoperable architectures that enable AI agents to connect seamlessly with enterprise systems, data repositories, and APIs.

Best practices for scaling AI agents across the enterprise

Organizations should focus on building a strong foundation for AI rather than deploying more tools. This report suggests that successful companies treat AI as a long-term business capability by ensuring agents have access to reliable internal knowledge, establishing clear governance policies, and integrating AI into core business processes.

Additionally, enterprises should provide AI systems with accurate, trusted company information that enables them to deliver more relevant insights and support decision-making. Robust governance also helps to reduce risks related to security, compliance, and data exposure.

Companies should also avoid becoming dependent on any single AI platform and instead adopt flexible technology architectures that can evolve as the market changes. Moreover, leading organizations are embedding AI into complex workflows, experimenting with new use cases, and developing teams with expertise in AI management and oversight.