AI projects continue to grow in ambition and scale, but many stall before reaching deployment due to foundational data issues. This report explores the hidden cost of poor data quality, the overcommitment of AI budgets to data preparation, and the paradox of over-cleaning. It presents industry-backed strategies and frameworks for improving data governance, architecture, and culture. The goal is to help organizations move from fragmented systems to AI-ready foundations that deliver measurable outcomes.Â
Key Highlights:Â
- Up to 80% of AI project time and 59% of the budget is allocated to data preparation.Â
- Data quality and integration challenges are the most significant causes of AI project underperformance.Â
- Only 29% of firms report having a data architecture that supports full-scale AI deployment.Â
- Excessive data cleaning can reduce model resilience by eliminating important real-world variance.Â
- Foundational capabilities like governance, accessibility, and infrastructure are prerequisites for scalable AI.Â
- Ongoing quality monitoring and DataOps practices are critical to sustaining AI performance over time.Â