AI projects continue to scale in ambition, but many stall before deployment due to gaps in enterprise data quality and low AI data readiness, directly limiting overall AI readiness. The impact of poor data quality is reflected in rising AI data preparation effort, fragmented enterprise data architecture, and weak enterprise AI governance. In many cases, excessive data cleaning in AI creates diminishing returns without improving outcomes. This report examines how data readiness for AI, stronger data governance in AI, and ongoing quality monitoring are essential to achieving AI readiness and building AI-ready enterprises that deliver measurable results.Â
Key Highlights:Â
- Up to 80Â % of AI project time and 59% of the AI budget is spent on AI data preparation and data remediation.
- Data quality and integration challenges are the most common causes of AI project failure and underperformance.Â
- Only 29Â %Â of enterprises report having an enterprise data architecture that fully supports scalable AI deployment.Â
- Excessive data cleaning in AI can weaken model robustness by eliminating meaningful real-world variance.Â
- Enterprise data governance, accessibility, and infrastructure are prerequisites for AI data readiness and responsible AI adoption.Â
- DataOps practices and continuous quality monitoring are essential for sustaining AI systems in production.Â