# The GenAI Divide: Why Most Pilots Stall and How Yours Won’t

MIT NANDA’s State of AI in Business 2025 report has sparked headlines about “95% of AI projects failing.” That number is real but it applies to task-specific enterprise GenAI. Meanwhile, the same research claims general-purpose LLMs (think ChatGPT/Copilot) show far better traction, with ~40% of orgs reporting success.  
  
**What the data actually says**  
  
\- Devil in the detail: General-purpose LLMs are widely piloted and deployed (40%). Task-specific GenAI stalls only ~5% reach production.  
\- Good signal for Tech & Media: Despite the hype, AI shows up clear structural disruption mainly in Tech and Media so far out of 9 researched industries.  
\- Start simple: AI has already “won” simple work. 70% prefer AI for drafting emails, 65% for basic analysis. For complex, multi-week work, humans still dominate 9:1.  
\- Approach matters: The divide isn’t driven by model quality or regulation; it’s driven by workflow fit, learning, and memory.  
\- Where ROI hides: Early, measurable savings come from reducing BPO and external agency spend especially in back-office operations more than headcount cuts.  
  
**Playbooks that work for organizations (from the 5% who succeed)**  
  
\- Land small, visible wins in narrow workflows; then expand.  
\- Choose tools with low configuration and immediate visible value. Initially avoid heavy enterprise customization.  
\- Prioritize learning, memory, and workflow adaptation not flashy UX.  
\- Partner to build pilots: AI pilots via strategic partnerships are 2x as likely to reach full deployment as internal builds.  
  
Research Report Link: [**https://lnkd.in/g3UCZ8Nr**](https://lnkd.in/g3UCZ8Nr)