生成式 AI 的鸿沟：2025 年商业 AI 的现状（英）

pg. 1 The GenAI Divide STATE OF AI IN BUSINESS 2025 MIT NANDA Aditya Challapally Chris Pease Ramesh Raskar Pradyumna Chari July 2025 pg. 2 NOTES Preliminary Findings from AI Implementation Research from Project NANDA Reviewers: Pradyumna Chari, Project NANDA Research Period: January – June 2025 Methodology: This report is based on a multi-method research design that includes a systematic review of over 300 publicly disclosed AI initiatives, structured interviews with representatives from 52 organizations, and survey responses from 153 senior leaders collected across four major industry conferences. Disclaimer: The views expressed in this report are solely those of the authors and reviewers and do not reflect the positions of any affiliated employers. Confidentiality Note: All company-specific data and quotes have been anonymized to maintain compliance with corporate disclosure policies and confidentiality agreements, ensure neutrality, and prevent any perception of commercial advancement or opinion. pg. 3 1 CONTENTS 1. Executive Summary 2. The Wrong Side of the GenAI Divide: High Adoption, Low Transformation 3. Why Pilots Stall: The Learning Gap Behind the Divide 4. Crossing the GenAI Divide: How the Best Builders Succeed 5. Crossing the GenAI Divide: How the Best Buyers Succeed 6. Conclusion: Bridging the GenAI Divide 2 EXECUTIVE SUMMARY Despite $30–40 billion in enterprise investment into GenAI, this report uncovers a surprising result in that 95% of organizations are getting zero return. The outcomes are so starkly divided across both buyers (enterprises, mid-market, SMBs) and builders (startups, vendors, consultancies) that we call it the GenAI Divide. Just 5% of integrated AI pilots are extracting millions in value, while the vast majority remain stuck with no measurable P&L impact. This divide does not seem to be driven by model quality or regulation, but seems to be determined by approach. Tools like ChatGPT and Copilot are widely adopted. Over 80 percent of organizations have explored or piloted them, and nearly 40 percent report deployment. But these tools primarily enhance individual productivity, not P&L performance. Meanwhile, enterprise-grade systems, custom or vendor-sold, are being quietly rejected. Sixty percent of organizations evaluated such tools, but only 20 percent reached pilot stage and just 5 percent reached production. Most fail due to brittle workflows, lack of contextual learning, and misalignment with day-to-day operations. From our interviews, surveys, and analysis of 300 public implementations, four patterns emerged that define the GenAI Divide: • Limited disruption: Only 2 of 8 major sectors show meaningful structural change • Enterprise paradox: Big firms lead in pilot volume but lag in scale-up • Investment bias: Budgets favor visible, top-line functions over high-ROI back office • Implementation advantage: External partnerships see twice the success rate of internal builds The core