Enterprise AI ambitions outpace readiness, survey finds

Hyland has published research showing a wide gap between enterprise AI ambitions and operational readiness. The study was produced by Harvard Business Review Analytic Services.

Most organisations see connected data, content and workflows as essential to successful AI adoption, yet few have those foundations in place. While 94% of respondents said well-connected data, processes and applications were highly important to AI success, only 27% said those elements were well connected in their organisation.

Unstructured data emerged as a particular weakness. While 65% of respondents said their structured data was somewhat or fully prepared for AI use, only 39% said the same of unstructured data such as emails, PDFs, images, video and other document-based content.

That gap matters because unstructured information underpins many day-to-day business processes. Much of it remains spread across repositories, applications and workflows, limiting efforts to apply AI at scale.

Readiness gap

The survey also found that AI projects often remain detached from routine operations. Among organisations actively using, piloting or exploring AI, 39% said most AI-enabled workflows still rely on separate, standalone tools, while 12% said AI is embedded directly into the flow of work.

Fewer than half of respondents, 45%, said their AI projects were delivering the outcomes they expected. This suggests businesses are still struggling to turn interest in AI into measurable operational results.

Respondents repeatedly cited several obstacles. Data silos were named by 54% as a leading challenge, followed by data security and privacy issues at 48%, data format issues at 46%, insufficient data management and governance at 46%, and an insufficient or unclear data strategy at 45%.

Only 10% identified a lack of data as a primary issue. For many organisations, the problem is not access to information but whether data can be governed, connected and used in a trusted way.

The results suggest that AI readiness depends as much on operational discipline as on software investment. The report outlines a series of priorities, including improving data readiness, reducing fragmentation in content systems, embedding AI within workflows, aligning leadership and IT on governance, and measuring outcomes through adoption, quality and business results.

The study was based on a survey of 325 members of the Harvard Business Review audience across North America, Europe, Asia Pacific and other regions. Respondents came from organisations ranging from small and medium-sized businesses to companies with more than 10,000 employees, across sectors including manufacturing, technology, education, financial services and government.

All respondents were involved in AI decisions within their organisations, giving the survey a view from executives and managers responsible for adoption rather than from technical teams alone.

Workflow focus

Jitesh S. Ghai, Chief Executive Officer of Hyland, linked the findings to a broader shift in how companies are thinking about AI deployment.

"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 Ghai.

He added that the next stage of adoption depends on integrating AI into routine business activity rather than leaving it as an isolated layer of tooling.

"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," Ghai said.

Amy Machado, senior research manager at IDC, said the threshold for success is rising as organisations pursue more advanced uses of AI.

"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," Machado said.

"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," she added.