Why 95% of AI Commentary Fails (aiascendant.com)

The widely reported claim that "95% of AI projects fail"—based on the recent MIT NANDA paper—has been largely misunderstood and misrepresented. While headline-grabbing outlets suggest that 95% of organizations see no return on AI investments, the actual study focuses more narrowly on the failure of custom-built, highly specific internal AI tools to transition from pilot phases to full implementation. In contrast, standard large language model (LLM) chatbots and partnerships with external AI vendors show considerable success, indicating that AI adoption is already pervasive and impactful in many enterprise settings. This nuanced view challenges the sensationalist narrative that AI, particularly generative AI, is broadly ineffective or doomed. The article also highlights a critical epistemic issue in AI reporting: many summaries neglect to link to or accurately represent primary sources, spreading misinformation that could easily be corrected with the help of modern AI tools themselves. Advanced models like GPT-5, Claude, and Gemini can rapidly and reliably analyze primary papers, offering clearer, less biased interpretations compared to much human journalism. This points to a broader opportunity for AI-enhanced epistemics, where combining human judgment with AI’s attention to detail and information recall could greatly improve the accuracy and depth of tech reporting—helping the AI/ML community avoid hype cycles and better understand progress in the field.