The data infrastructure gap killing AI initiatives
RAND Corporation's analysis confirms that over 80% of AI projects fail—twice the failure rate of non-AI technology projects. S&P Global's 2024 survey is worse: 42% of companies abandoned most of their AI initiatives this year, up from 17% in 2023.
The average organization scrapped 46% of AI proof-of-concepts before they reached production.
The problem isn't the models. It's the data.
Informatica's 2025 CDO survey identifies the top obstacles: data quality and readiness (43%), lack of technical maturity (43%), shortage of skills (35%). Deloitte found 80% of AI projects encounter difficulties related to data quality and governance.
Percentage of enterprises encountering each barrier
Source: Informatica CDO Survey 2025, RAND Corporation, S&P Global, McKinsey State of AI 2025
McKinsey's 2025 State of AI confirms it: organizations reporting "significant" financial returns from AI are twice as likely to have redesigned end-to-end workflows before selecting modeling techniques. They built the data foundation first. Everyone else is still running pilots.
“65% of organizations use generative AI regularly—double last year. But 74% still can't scale it.”
— McKinsey State of AI, 2025
Top 6% of AI performers fundamentally restructure before deploying
Source: McKinsey State of AI 2025 — Organizations attributing >=5% of EBIT to AI
The pattern is clear: companies skip the hard work of data integration, buy a model, run a pilot, declare success, then watch it die when they try to scale. The pilot worked because a data scientist manually cleaned the data. Production doesn't have that luxury.