AI Data Readiness Crisis 2026
60% of AI projects will be abandoned by 2026 due to lack of AI-ready data. This isn't an algorithm problem - it's a data infrastructure crisis. Learn why traditional data quality isn't enough, apply Gartner's 5-step framework, and build your remediation roadmap.
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The AI Data Readiness Crisis
Why 60% of AI projects will be abandoned by 2026 - and it's not about the algorithms
Organizations are rushing to implement AI, but the foundational data infrastructure is failing. Gartner predicts 60% of AI projects will be abandoned by 2026 due to lack of AI-ready data. The gap between AI ambition and data reality is widening, with S&P Global reporting scrapped initiatives jumped from 17% to 42% in a single year.
- 60% of AI projects projected to be abandoned by 2026 (Gartner)
- 42% of initiatives scrapped in 2025, up from 17% in 2024 (S&P Global)
- 63% of leaders admit their data practices aren't sufficient for AI
- 46% of Proof-of-Concepts never reach production
- 75% rank data as top priority but fail to execute on improvements
This is not an algorithm problem. It's a data infrastructure crisis. Traditional data cleaning methods are insufficient for probabilistic AI models.
This guide provides frameworks from Gartner, analysis from S&P Global, and practical remediation roadmaps for marketing technology leaders.