Hyland has announced new research from Harvard Business Review Analytic Services, Bridging the Readiness Gap to the Agentic Enterprise, showing that enterprise AI ambition is advancing faster than enterprise readiness. While organizations increasingly recognize that AI success depends on connected data, content, and workflows, most have not yet built the operational foundation required to scale it.
The gap is especially visible in how organizations are managing enterprise information. While nearly all (94%) of respondents say well-connected data, processes, and applications are highly important to successful AI adoption, less than a third (27%) say those elements are well connected in their organization today. And although 65% say their structured data is somewhat or fully prepared for AI use, only 39% say the same about their unstructured data, including emails, PDFs, images, video, and other document-based content that make up much of the information businesses rely on every day.
Untapped Opportunity in Unstructured Data
For many organizations, the issue is not a lack of data. It is that much of the most operationally important information remains trapped in unstructured data spread across repositories, applications, and workflows. The report suggests that closing this gap will require more than deploying new AI tools. It will depend on building a stronger foundation for governance, access, and workflow execution, especially as organizations move toward more agentic forms of AI.
“As organizations move into the next phase of AI, the challenge is no longer just access to models, but whether the business is ready to operationalize AI in a way that is governed, contextual, and trusted,” said Jitesh S. Ghai, CEO of Hyland. “The agentic enterprise takes shape when AI is embedded into real operational workflows, grounded in the content, data, and controls the business already depends on. For many organizations, unstructured data is both the most overlooked asset and the biggest obstacle to scaling AI effectively.”
The research identifies several barriers that continue to limit organizations’ ability to adopt and scale AI. The top-cited data challenges are data silos (54%), data security and privacy issues (48%), data format issues (46%), insufficient data management and governance (46%), and insufficient or unclear data strategy (45%). Just 10% cite a lack of data as a primary issue, reinforcing that the central problem is not volume, but readiness, access, and trust.
A Practical Path to Enterprise AI Readiness
The findings also suggest that many organizations still have not embedded AI into day-to-day operations. Among respondents at organizations actively using, piloting, or exploring AI, 39% say most AI-enabled workflows still rely on separate, standalone tools, while only 12% say AI is embedded directly within the flow of work. Fewer than half, 45%, say their AI projects are delivering the outcomes they expected.
“As companies move toward advanced and agentic forms of AI, the bar is being raised; not just for technology, but for how information flows, decisions are governed, and value is measured,” said Amy Machado, senior research manager at IDC. “The organizations that invest in modernizing their content foundations and embedding intelligence into real workflows will be best positioned to turn AI ambition into sustained impact.”
The report outlines several priorities for organizations seeking to close the readiness gap:
Prioritize data readiness, especially for unstructured data that remains underprepared for AI use
Modernize content platforms to reduce fragmentation and improve access, governance, and reuse
Embed AI into workflows rather than relying on disconnected, standalone tools
Align leadership and IT around shared governance, definitions, and operational accountability
Measure success through adoption, quality, and business outcomes, not speed alone
