Important points:
- AI agents have become a standard part of many organizations’ operations.
- Access to trusted internal knowledge remains the biggest barrier to AI effectiveness.
- Leading companies are strengthening governance and adopting multi-platform AI strategies.
Although AI agents have rapidly entered the enterprise mainstream, 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 realizing that effective governance, connected knowledge, and flexible infrastructure are the real determinants of success.
Cloud content management company Box surveyed 1,640 IT decision makers in the US, UK, France, and Japan. The study found that organizations are rapidly advancing in AI maturity, with more organizations now considering themselves advanced or cutting-edge users of the technology. Over the past year, AI maturity has increased significantly across the enterprise, but the number of organizations still in the early stages of adoption has declined sharply.
AI agents are already a standard part of most organizations’ operations, and more and more companies are using them to automate tasks, support decision-making, and streamline workflows. As adoption continues, many companies are reporting measurable and tangible business value, with the majority indicating that their AI investments have led to significant increases in productivity and overall performance.
Leading organizations use AI to drive business transformation
Many companies are now using AI to improve efficiency through automation and digital assistance, but the most advanced adopters are taking a broader approach. Leverage AI to scale your work beyond previous limits and enable entirely new business activities that were previously impractical or resource-intensive.
According to the study, many organizations expect to expand their workforces as AI becomes more deeply integrated into business operations. AI is driving demand for new roles focused on areas such as AI operations, governance, compliance, and workflow management.
AI is hampered by limited access to trusted corporate knowledge
Organizations widely recognize that AI agents can provide the most value when they can access and understand the organization’s internal knowledge, data, and content. However, many companies are still unable to connect their AI systems to trusted information sources at scale, limiting the ability of agents to provide context-aware insights.
As organizations accelerate AI adoption, data security and governance concerns are becoming increasingly important. Many companies have already experienced AI-related data breach incidents, prompting increased investment in governance frameworks and risk management practices. However, significant gaps remain in areas such as monitoring AI activity, maintaining visibility into tool usage, and establishing clear controls over how AI systems access and interact with sensitive corporate data.
Finally, businesses are becoming increasingly wary of relying too heavily on a single AI provider. In response, leading organizations are taking a multi-platform approach, deploying a variety of AI tools while building flexible, interoperable architectures that allow AI agents to seamlessly connect 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 additional tools. The report suggests that successful companies treat AI as a long-term business function by giving personnel access to trusted internal knowledge, establishing clear governance policies, and integrating AI into core business processes.
Additionally, companies need to provide AI systems with accurate and reliable company information to provide more relevant insights and support decision-making. Robust governance also helps reduce risks related to security, compliance, and data breaches.
Companies should also avoid relying on a single AI platform and instead adopt flexible technology architectures that can evolve as the market changes. Additionally, leading organizations are incorporating AI into complex workflows, experimenting with new use cases, and developing teams with expertise in AI management and monitoring.
