Cloudera 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 more than 300 IT leaders in the EMEA region, including strong insights from Saudi Arabia, the report finds that while AI adoption is growing, most organisations still lack the data foundation needed for success.
The findings highlight a sharp contrast in how effectively organisations track their data. Nearly 9 in 10 EMEA IT leaders claim complete visibility into where all their data resides, compared to just 32% of respondents in Saudi Arabia. Furthermore, 62% of Saudi respondents cite data access restrictions as a major roadblock to effective data use.
This gap 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, Chief Technology Officer 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.”
AI adoption is high, but ROI remains elusive
While AI is now deeply embedded across the enterprise, achieving consistent returns on investment remains difficult due to a sharp geographical divide in implementation hurdles. Across EMEA, the struggle is largely centred on the inputs, with data quality issues (18%) and cost overruns (16%) cited as the primary causes of lackluster ROI. However, Saudi Arabia presents a different challenge focused on execution. In the Kingdom, weak integration into workflows is the overwhelming barrier at 29%, nearly doubling the concern over data quality, which sits at 15%.
These regional nuances are further tangled by significant infrastructure limitations. Around 65% of respondents in KSA report that performance constraints have hindered operational initiatives, highlighting the immense difficulty of scaling AI across fragmented environments.
Bridging the data gap
At the core of these challenges is a significant disconnect between data optimism and operational reality.
The report highlights that 95% of KSA respondents are highly confident in their data, but only 32% of that data is currently fully governed. While this outpaces the broader EMEA region, where only 26% of data is governed despite 91% confidence, it highlights a critical execution gap that organisations are now racing to fill.
The Kingdom is uniquely positioned to bridge this divide with 100% of Saudi respondents ready to adopt new governance frameworks, and 79% being extremely willing to transform their operations. This regional commitment suggests that Saudi Arabia’s proactive approach will likely outpace its peers in the race toward AI and digital maturity.
Strategic alignment and the accountability gap
While leadership in both the EMEA and KSA regions understands the necessity of data infrastructure, the execution and accountability frameworks are worlds apart. More than 90% of EMEA respondents report a well-defined data strategy tied directly to business objectives, while only over half (53%) of Saudi Arabian respondents feel the same level of alignment.
Accountability and internal culture further widen this divide. In EMEA, 69% of leaders hold the CIO or CTO chiefly responsible for data readiness, whereas in Saudi Arabia, only 35% place ultimate responsibility on this role, indicating a more emerging ownership structure.
Beyond accountability and alignment, respondents in Saudi Arabia face a unique internal hurdle: 50% struggle with insufficient data literacy, while nearly a third (32%) cite a lack of executive sponsorship.
Data readiness will define the 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.
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 willing to adapt existing frameworks to support true data readiness.
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. Those that close this gap will be best positioned to drive lasting impact and lead the next era of intelligent business.
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