Among organizations worldwide, 96% report integrating AI into core business processes and 85% say they have a clear data strategy, but nearly four out of five (about 80%) admit their AI and data initiatives are still constrained by limited data access across environments.
This is according to a report from Cloudera, which is based on a survey fielded by Researchscape. It covered 1,270 IT leaders based across the Americas; Europe, the Middle East and Africa; and the Asia-Pacific regions who work at companies with more than 1,000 employees. The survey was done from January 22 to March 3 in 2026.
In APAC, organizations appear to be making stronger progress, with only 38% reporting the same constraint.
However, gaps remain. While 82% of organizations 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 organizations are prepared to scale AI even as critical data challenges remain unresolved.
“Enterprises aren’t struggling to adopt AI, they’re struggling to operationalize 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, organizations limit the accuracy, trust, and business value that AI can deliver.You can’t do AI without data,” said Gago.
Remus Lim, Cloudera’s SVP in APAC and Japan, said APAC firms are not standing still on AI. Many already have clear strategies in place and are moving quickly to put them into action.
“But in the next phase of AI, organizations need to connect, govern, and operationalize their data across environments. That is what turns AI from isolated progress into repeatable business value,” said Lim.
AI adoption is high, but ROI remains elusive
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. Nearly three-quarters (73%) of respondents globally report that performance constraints have hindered operational initiatives, reflecting the difficulty of scaling AI across fragmented environments.
In contrast, 28% of APAC respondents said operational initiatives were often hindered by infrastructure performance issues, while another 38% said they were sometimes hindered, showing that infrastructure remains a meaningful obstacle to consistent execution.
The data gap
At the core of these challenges is a lack of complete data access and control, with 84% of respondents feeling confident in the accuracy, completeness, and alignment of their organization’s data.
However, this optimism often masks 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 organization.
Less than one in five (18%) respondents said their data was fully governed, highlighting the gap between perceived confidence and reality. While 71% say most of their data is governed, true data-backed initiatives depend on a consistent, organization-wide source of truth.
In APAC, governance maturity appears 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, organizations risk missed opportunities, poor decision-making, and outputs that fall short of their full potential.
Comparing data readiness
The landscape of data readiness looks very different 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 this strong data readiness, the advantage has not fully translated into operational success. Three out of five (60%) telecommunications respondents said infrastructure performance consistently hinders operational initiatives, the highest among all industries surveyed.
These challenges extend to AI initiatives as well. Barriers to AI ROI differ by industry. While survey respondents most often cited data quality, cost overruns were most prominent in energy and utilities (25%).
By contrast, poor integration into workflows was highlighted by respondents in healthcare, manufacturing, and financial services (20%).
Next phase of enterprise AI
As enterprise AI shifts from experimentation to execution, data readiness is emerging as the defining factor separating leaders from laggards.
Organizations 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 organization 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 organization was very willing to adopt new governance frameworks to improve data readiness, while 92% said senior leadership understands and prioritizes the data infrastructure needed to enable AI at scale. Reflecting this, 69% said CIOs and CTOs are chiefly accountable for delivering data readiness for AI.
As enterprises confront the limits of the AI readiness illusion, the path forward is clear: unlocking AI’s full value will require more than ambition; it will demand genuine data readiness.
