Nearly 80% of enterprises say AI is held back by data access challenges: Cloudera

- Cloudera’s The Data Readiness Index has revealed a growing “AI readiness illusion”. In essence, widespread AI adoption is outpacing the data foundations required to deliver real business impact.

- In the Asia-Pacific region (APAC), 85% of organisations claim to have complete visibility over where their data resides, but 38% struggle to use their data effectively due to complicated access requirements and processes.

 

Cloudera, the company bringing AI to data anywhere, has released its latest global survey, The Data Readiness Index: Understanding the Foundations for Successful AI, examining how prepared enterprises are to support AI at scale. Surveying nearly 1,300 global IT leaders, the report finds that while AI adoption is growing, most organisations still lack the data foundation needed for success.

Source: Cloudera. Cover for the Data Readiness
Index 2026 report. 

The global findings highlight a paradox: while 96% of organisations report integrating AI into core business processes and 85% say they have a clear data strategy, nearly eight out of 10 (~80%) admit their AI and data initiatives are still constrained by limited data access across environments. 

In APAC, organisations appear to be making stronger progress, with only 38% reporting the same constraint. 

However, gaps remain. While 82% of organisations say they have a clear data strategy, just 27% report that their data sources are fully integrated.

This highlights an emerging “AI readiness illusion”: the belief that organisations are prepared to scale AI even as critical data challenges remain unresolved.

“Enterprises aren’t struggling to adopt AI, they’re struggling to operationalise it beyond experiments,” said Sergio Gago, CTO at Cloudera. 

“AI is only as effective as the data that fuels it. Without seamless access to all their data, organisations limit the accuracy, trust, and business value that AI can deliver. You can’t do AI without data.”

“Asia Pacific organisations are not standing still on AI. Many already have clear strategies in place and are moving quickly to put them into action,” said Remus Lim, Senior VP, Asia Pacific and Japan, Cloudera.

“But in the next phase of AI, organisations need to connect, govern, and operationalise their data across environments. That is what turns AI from isolated progress into repeatable business value.”

According to Cloudera, AI is now embedded across the enterprise, but achieving consistent returns on investment remains difficult. When asked why AI initiatives fall short, respondents globally cited several key challenges: data quality (22%), cost overruns (16%), and poor integration into existing workflows (15%). 

The leading barriers in APAC were data quality issues (19%) and weak integration into workflows (19%), showing that even in markets making progress on AI adoption, foundational data and operational challenges continue to limit impact.

Infrastructure limitations further compound the issue, Cloudera found. Nearly three-quarters (73%) of respondents globally reported that performance constraints hindered operational initiatives, reflecting the difficulty of scaling AI across fragmented environments. 

In contrast, 28% of APAC respondents said operational initiatives were often held back by infrastructure performance issues, while another 38% said they were sometimes hindered, showing that infrastructure remains a meaningful obstacle to consistent execution.

Cloudera said that a lack of complete data access and control lies at the heart of these challenges.

Over eight in 10 (84%) respondents felt confident in the accuracy, completeness, and alignment of their organisation’s data. However, this optimism often masked deeper issues, including persistent silos, inconsistent data quality, and limited accessibility. Data that appears reliable in isolation frequently breaks down when used across teams, systems, or AI applications, exposing gaps in governance and consistency across the organisation, Cloudera elaborated.

Under than one in five (18%) respondents said their data was fully governed, highlighting the gap between perceived confidence and reality. While 71% said most of their data is governed, true data-backed initiatives require a consistent, organisation-wide source of truth. In APAC, governance maturity appeared even less consistent, with just 10% of respondents saying all of their data was fully governed.

Without comprehensive governance to unify data and enforce clear standards, organisations risk missed opportunities, poor decision-making, and outputs that fall short of their full potential, Cloudera cautioned.

The landscape of data readiness varied across industries. For example, 54% of telecommunications respondents said it is “extremely true” that they have full visibility into where their data resides. In comparison, only 30% of financial services respondents and 31% of public sector respondents reported the same. Regarding access, 51% of telecommunications respondents said they can access all their data at any time, compared to just 24% in financial services and 16% in the public sector.

Despite the advantage of strong data readiness, operational success is not guaranteed. Six in 10 telecommunications respondents said infrastructure performance consistently hinders operational initiatives, the highest among all industries surveyed.

Barriers to AI ROI also differed by industry. While survey respondents most often cited data quality, cost overruns were most prominent in energy and utilities (25%). In contrast, poor integration into workflows was highlighted by respondents in healthcare, manufacturing, and financial services (20%).

As enterprise AI shifts from experimentation to execution, data readiness is emerging as the defining factor separating leaders from laggards, Cloudera said.

Organisations able to fully access and govern all their data, wherever it resides, are far better equipped to deliver trusted, scalable AI. Notably, every respondent in the report indicated their organisation is at least somewhat willing to adapt existing frameworks to support true data readiness. 

In APAC, that willingness appears especially strong: 94% of respondents said their organisation was very willing to adopt new governance frameworks to improve data readiness, while 92% said senior leadership understands and prioritises the data infrastructure needed to enable AI at scale. Drilling deeper, 69% said CIOs and CTOs are chiefly accountable for delivering data readiness for AI.

Cloudera concluded that unlocking AI’s full value will require more than ambition; it will demand genuine data readiness. 

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*The survey, commissioned by Cloudera and fielded by Researchscape, examines the views of 1,270 IT leaders based across the AMER, EMEA, and APAC regions who work at companies with more than 1,000 employees. The survey was fielded from January 22, 2026, to March 3, 2026. The results of this survey have been weighted to be representative of the overall GDP of surveyed countries.