Only 7% of Enterprises Say Their Data Is Completely Ready for AI, According to New Report from Cloudera and Harvard Business Review Analytic Services
AI Adoption Accelerates, but 73% say their Organization Struggles with AI Data Preparation, Highlighting Need for Unified, Governed Data Across Hybrid Environments
Dubai, UAE – March 25th, 2026: Cloudera, the only company bringing AI to data anywhere, announced findings from a new global study conducted by Harvard Business Review Analytic Services with Cloudera, revealing that while enterprises recognize AI’s transformative potential, most remain unprepared to operationalize it due to persistent data readiness challenges.
Ahmad Shakora, Group Vice President South, Emerging Markets (META), Cloudera
In the Middle East, where governments and businesses are aggressively driving AI adoption as part of digital transformation agendas, the gap between ambition and readiness is particularly pronounced. Despite strong investments in AI technologies, many organizations are still grappling with fragmented data systems, governance challenges, and skill shortages, which slow down the translation of AI potential into operational impact.
The report, Taming the Complexity of AI Data Readiness, surveyed more than 230 members of the Harvard Business Review audience, all involved in their organization’s AI data decisions, in October 2025, and revealed a clear imbalance in enterprise AI progress. While organizations are accelerating AI initiatives, their underlying data foundations are not keeping pace. Only 7% say their organization’s data is completely ready for AI adoption, and more than one-quarter (27%) report their data is not very or not at all ready, highlighting a growing gap between AI ambition and operational readiness.
Data Quality Perception Issues
Despite widespread AI experimentation, foundational data issues remain. Seventy-three percent of respondents say their organization should prioritize AI data quality more than it currently does, and an equal percentage report that their organization has found the processing and preparing of data for AI to be challenging.
The top obstacles when it comes to preparing data for AI include:
- Siloed data/difficulty integrating data sources (56%)
- Lack of a clear data strategy (44%)
- Data quality/bias issues (41%)
- Regulatory constraints on data use (34%)
Leadership Wants a Data Strategy
Enterprise leaders increasingly recognize that AI is no longer a future initiative; it is a present operational mandate. Yet most organizations are still formalizing the data foundations required to scale it responsibly and effectively.
- While only 23% say their organization has an established data strategy for AI adoption, more than half (53%) are actively developing one.
- Protecting sensitive data and privacy (59%), data quality (46%), and data governance (41%) rank as the most critical components of those strategies.
Innovation may capture headlines, but durable competitive advantage depends on modern, governed data architectures capable of operating seamlessly across multi-cloud, data center, edge, and hybrid environments.
At the same time, expectations for agentic AI are accelerating, reflecting a shift from experimentation to operational reinvention.
- Nearly two-thirds (65%) of respondents expect many of their organization’s business processes will be augmented or replaced by agentic AI in the next two years.
- 47% say their organization believes agentic AI can solve its data quality issues.
As organizations shift from applications to intelligent agents, scalable data pipelines, consistent governance, and cloud-like experiences across environments are becoming increasingly essential.
“Across the Middle East, organizations are accelerating AI adoption as part of broader national digital transformation agendas. However, the challenge many enterprises face today lies in ensuring their data environments are ready to support AI at scale. Fragmented systems, evolving governance frameworks, and the complexity of operating across hybrid and multi-cloud environments continue to slow progress. As AI capabilities advance, particularly with the emergence of agentic systems, organizations will need to prioritize trusted data, strong governance, and modern data architectures to translate AI ambition into measurable business impact.” Ahmad Shakora, Group Vice President, South- META, Cloudera Middle East.
“AI is only as powerful as the data behind it,” said Sergio Gago, Chief Technology Officer at Cloudera. “To move from pilots to production, organizations need secure access to 100% of their data, anywhere it resides. Bringing AI to data instead of moving data to your AI is what separates experimentation from enterprise-scale impact.”
Today’s enterprises operate in complex, distributed data estates spanning clouds, data centers and edge environments, yet mission-critical data often remains in on-prem environments due to sovereignty, security, cost, and compliance requirements. Bridging this divide requires architectures that can securely operationalize AI across hybrid environments without forcing data movement or compromising control.
Cloudera addresses this challenge by converging public cloud and enterprise data centers to deliver a unified, cloud experience in hybrid environments across the entire data estate. Built on an open-source foundation, it powers AI across more than 25 exabytes of enterprise data worldwide.
The full report can be found on Cloudera’s website here.
About Cloudera
Cloudera is the only hybrid data and AI platform company that large organizations trust to bring AI to their data anywhere it lives. Unlike other providers, Cloudera delivers a consistent cloud experience that converges public clouds, on-prem data centers, and the edge, leveraging a proven open-source foundation. As the pioneer in big data, Cloudera empowers businesses to apply AI and assert control over 100% of their data, in all forms, improving security, governance, and real-time and predictive insights. The world’s largest brands across all industries rely on Cloudera to transform decision-making and ultimately boost bottom lines, safeguard against threats, and save lives.
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