Fivetran Report Finds Nearly Half of Enterprise AI Projects Fail Due to Poor Data Readiness

New global survey reveals rising business costs from failed AI projects, as integration complexity and pipeline maintenance drain engineering resources

Fivetran, the global leader in data movement, today released new research showing that nearly half of enterprises report delayed, underperforming, or failed AI projects despite bold strategies and major investment in AI and data centralization. The survey, conducted by Redpoint Content, highlights poor data readiness as the leading roadblock to AI execution, driving increased costs, stalled innovation, and lost revenue.

Even though 57% of organizations rate their data centralization strategy as highly effective, nearly the same proportion say that over half their AI projects fail to deliver. The disconnect is data that is not fully centralized, governed, or made available in real time for AI models. From integration bottlenecks to pipeline maintenance burdens, enterprises are stuck managing infrastructure instead of delivering business value through AI.

Key findings from the Fivetran AI and Data Readiness Survey

• 42% of enterprises say more than half of their AI projects have been delayed, underperformed, or failed due to data readiness issues
• 68% of organizations with less than half of their data centralized report lost revenue tied to failed or delayed AI projects
• 67% of centralized enterprises allocate over 80 percent of their engineering resources to maintaining data pipelines
• 59% of enterprises say regulatory compliance is their top challenge in managing data for AI

AI ambition without execution is costing businesses

AI underperformance is not just a technical problem. It is a business risk. The research found that:

• 38% of enterprises report increased operational costs due to AI project failures
• Reduced customer satisfaction and retention was the most common consequence of failed AI projects

Automation and integration unlock AI success

The report calls on enterprises to modernize their data infrastructure with automated integration tools that reduce pipeline complexity and free up engineering resources. Among the top investment priorities cited by respondents:

• 65% plan to invest in data integration tools as their primary strategy to enable AI
• Nearly three-quarters of enterprises manage or plan to manage more than 500 data sources, amplifying the need for scalable, automated solutions

What’s really blocking AI success

The survey found that many enterprises are struggling to move beyond pilot AI projects because they cannot efficiently prepare, integrate, or operationalize their data. The data revealed several key pain points:

• 74% of enterprises manage or plan to manage more than 500 data sources, creating significant integration complexity
• 67% of highly centralized enterprises still spend over 80 percent of their data engineering resources maintaining pipelines, leaving little time for AI innovation
• 41% of organizations report the lack of real-time data access prevents AI models from delivering timely insights
• 29% of enterprises say data silos are blocking AI success

Until these challenges are addressed, organizations will continue to struggle with AI performance and fail to unlock the full value of their investments.

Regional and industry differences in AI readiness

These issues are not limited to any one sector. Industries like healthcare and retail are leading in AI readiness due to stronger automation and data integration strategies. Sectors such as finance and manufacturing continue to struggle with legacy systems and integration constraints.

Regional differences are also significant. The Asia-Pacific region leads all others with an AI readiness score of 8.8 out of 10, followed by the United States at 8.2. The United Kingdom trails with a score of 6.0 due to weak integration strategies and fragmented infrastructure.

The survey was conducted in the first quarter of 2025 by Redpoint Content. It gathered responses from 401 data leaders and professionals across the United States, United Kingdom, Europe, the Middle East, and Africa and the Asia-Pacific region. Respondents represented enterprises in technology, finance, healthcare, retail, and manufacturing, ranging from 500 to more than 5,000 employees. The full report is available at https://www.fivetran.com/resources/reports/ai-data-readiness.

About Fivetran

Fivetran, the global leader in data movement, is trusted by companies like OpenAI, LVMH, Pfizer, Verizon, and Spotify to centralize data from SaaS applications, databases, files, and other sources into cloud destinations, including data lakes. With high-performance pipelines, seamless interoperability, and enterprise-grade security, Fivetran empowers organizations to modernize their data infrastructure, power analytics and AI, ensure compliance, and achieve transformative business outcomes. Learn more at Fivetran.com.

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