Cloudera's Data Readiness Index reveals a growing “AI readiness illusion,” where widespread adoption outpaces the data foundations required to deliver real business impact.
SAN JOSE, Calif., April 14, 2026 (GLOBE NEWSWIRE) -- Cloudera, the only company bringing AI to data anywhere, today 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 organizations still lack the data foundation needed for success. The findings highlight a striking paradox: while 96% of organizations report integrating AI into core business processes and 85% say they have a clear data strategy, nearly 4 out of 5 (~80%) admit their AI and data initiatives are still constrained by limited data access across environments.
This gap 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, Chief Technology Officer 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.”
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 cited several key challenges: data quality (22%), cost overruns (16%), and poor integration into existing workflows (15%). These barriers highlight the ongoing complexity of translating AI investments into measurable business outcomes.
Infrastructure limitations further compound the issue. Nearly three-quarters (73%) of respondents report that performance constraints have hindered operational initiatives, reflecting the difficulty of scaling AI across fragmented environments.
The Data Gap: Access, Governance, and Visibility
At the core of these challenges is a lack of complete data access and control.
84% of respondents felt 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.
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.
How Industries Compare on 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%).
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.
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.
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.
To read more about the barriers to enterprise AI and how to address the data readiness gap, read the full report here.
Methodology
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.
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.
To learn more, visit Cloudera.com and follow us on LinkedIn and X. ©2026 Cloudera and associated marks are trademarks or registered trademarks of Cloudera, Inc. All other company and product names may be trademarks of their respective owners.
Contact
Jess Hohn-Cabana
cloudera@v2comms.com
-
Nearly 80% of Enterprises Say AI Is Held Back by Data Access Challenges, New Cloudera Report FindsCloudera's Data Readiness Index reveals a growing “AI readiness illusion,” where widespread adoption outpaces the data foundations required to deliv2026-04-15
-
开启“芒”里抽闲新体验,养乐多芒果味新鲜上市上海2026年4月15日 美通社 -- 2026年4月15日起,养乐多家族迎来新成员养乐多活菌型乳酸菌乳饮品(芒果味)陆续于中国地区*各平台新鲜上市。新品延续每瓶100亿个副干酪乳2026-04-15
-
迈威生物孵化公司思努赛生物 α-syn PET示踪剂SST001获NMPA批准开展临床试验上海2026年4月15日 美通社 -- 迈威生物(688062.SH),一家全产业链布局的生物制药公司,宣布其投资孵化公司思努赛生物自主研发的靶向α-突触核蛋白(α-syn)PET示踪剂18 F-FD42026-04-15
-
沃特世推出业界首款适用于UHPLCUPLC的宽范围MALS分析系统,助力大分子快速表征新闻摘要: * 色谱运行时间可缩短至原来的四分之一,从而加速在发现、开发及质量控制工作流程中的决策1,3。 * 绝对摩尔质量测量精度和尺寸测定范围提升10倍,且不影2026-04-15
-
Aramco Stadium Company宣布高管任命沙特阿拉伯宰赫兰 2026年4月15日 美通社 -- Aramco的子公司Aramco Stadium Company今日宣布正式成立,并任命其首任董事会兼首席执行官。 这些任命标志着该公司启2026-04-15
-
AMD股价暴跌17%创近9年之最,苏姿丰紧急回应:AI增速远超想象
-
江苏省脑机接口产业联盟在宁成立,麦澜德分享前沿成果
-
艾芬达入选国家知识产权强国建设示范创建对象:二十载长期主义,兑现每一份用户价值
-
慧启赣疆 聚势共赢丨慧友酒店集团江西品鉴会书写区域文旅融合新篇
-
电影《一秒》定档:2026年,活在这一秒
-
西藏斜视患儿寒假进京手术成功,千里护航点亮视觉未来
-
年度盛典|卓兴半导体2025年度总结表彰暨 2026 年迎新晚会
-
科技赋能 生态协同,登途集团车辆资产管理运营模式助推行业提质增效
-
公元地暖构建“产品+施工+服务”全维保障网,兑现50年温暖承诺
-
VCI Global 投资组合公司 Reveillon Group 与 NOWWA Coffee 达成战略合作,共同开拓马来西亚市场
