I found the following fascinating to read because it punctures the viral “95% of enterprise GenAI projects fail” meme and replaces it with context, math, and nuance. The author shows that:

I found the following fascinating to read because it punctures the viral “95% of enterprise GenAI projects fail” meme and replaces it with context, math, and nuance. The author shows that:

• The eye-catching 95% figure is methodologically thin and likely overstated. The report’s own chart suggests 60% investigated, 20% piloted, and 5% reached impactful implementation. Since 40% never tried at all, that implies ~8.3% success across all firms and ~91.7% failure—not 95%.
• Even if failure rates are high, that’s not uniquely bad for AI. Historical baselines for enterprise IT are grim too: Forbes cites ~84% failure, CHAOS reports 61% overall failure (rising to 98% for large, complex projects), and McKinsey found ~0.5% delivered on time and on budget.
• Definitions of “success” differ but are comparable. The AI study requires sustained productivity or P&L impact; classic IT studies emphasize time, budget, and user satisfaction. Either way, hard enterprise projects are hard.
• Timing matters. Useful frontier models only stabilized recently (GPT-4 in Mar 2023; more affordable/reliable GPT-4o in May 2024), while enterprise projects typically run 2.4–3.9 years. Meanwhile, best practices are still evolving: tools vs MCP architectures, agent/sub-agent patterns, context compaction, and when to use RAG vs agentic search.
• The statistic focused on embedded/task-specific GenAI, not general-purpose LLM use. Many employees already get tangible value from ChatGPT/Copilot—often via “shadow IT”—even as formal, measurable programs lag.

Why it’s worth your time: It reframes the conversation away from sensational failure headlines toward base rates, definitions, and practical realities of shipping complex AI in enterprises. It also highlights where value is actually emerging (bottom-up tool use) and why “failed” formal programs may be more about immature infrastructure and moving goalposts than inherent AI futility.

If you’re shaping AI strategy, portfolio bets, or change management, this offers a grounded lens for interpreting vendor claims, internal pilots, and board expectations—plus a reminder to measure the right things and resist premature verdicts on a still-forming stack.

See link below. #AI #GenAI #EnterpriseIT #TechStrategy #DigitalTransformation #LLM #ProjectManagement #DataStrategy #RAG #AIAdoption #ShadowIT #GPT4 #AIProjects #MIT

Is it worrying that 95% of AI enterprise projects fail? https://www.seangoedecke.com/why-do-ai-enterprise-projects-fail/

#AI #GenAI #EnterpriseIT #TechStrategy #DigitalTransformation #LLM #ProjectManagement #DataStrategy #RAG #AIAdoption #ShadowIT #GPT4 #AIProjects #MIT

https://www.seangoedecke.com/why-do-ai-enterprise-projects-fail/

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