Super Data Science: ML & AI Podcast with Jon Krohn 924: 95% of Enterprise AI Projects Fail (Per MIT Research)
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Sep 19, 2025 A recent report reveals that a staggering 95% of enterprise AI projects are failing to deliver returns on massive investments. The findings highlight high pilot adoption rates but dismal scaling success. Critics argue that the figure may be inflated, while successful projects share common traits like learning models and proper integration. The discussion offers insightful strategies on how to navigate the AI landscape effectively and avoid the pitfalls that many businesses face.
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Most Enterprise AI Projects Don't Scale
- MIT NANDA reports that 95% of enterprise AI projects fail to deliver measurable business impact.
- The report finds 40% deploy pilots but only 5% reach scaled production due to static models and poor workflow integration.
Shadow AI Outperforms Official Tools
- Employees create a shadow AI economy using personal AI tools in over 90% of organizations.
- These unofficial tools often produce higher ROI than sanctioned corporate projects.
Report May Be Biased But Core Problem Persists
- Critics say the 95% figure may be inflated and note MIT NANDA's ties to agentic AI vendors.
- Even accounting for bias, the core finding stands: most projects lack real impact.