Nearly half of enterprises report failed AI projects due to poor data readiness

Fivetran has 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 centralisation. 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 percent of organisations rate their data centralisation strategy as highly effective, nearly the same proportion say that over half of their AI projects fail to deliver. The disconnect is data that is not fully centralised, 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 percent of enterprises say more than half of their AI projects have been delayed, underperformed, or failed due to data readiness issues

68 percent of organisations with less than half of their data centralised report lost revenue tied to failed or delayed AI projects

59 percent 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 percent 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 modernise 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 percent 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 operationalise their data. The data revealed several key pain points:

67 percent of highly centralised enterprises still spend over 80 percent of their data engineering resources maintaining pipelines, leaving little time for AI innovation

41 percent of organisations report the lack of real-time data access prevents AI models from delivering timely insights

29 percent of enterprises say data silos are blocking AI success

Until these challenges are addressed, organisations 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 US at 8.2. The UK trails with a score of 6.0 due to weak integration strategies and fragmented infrastructure.