ML项目失败主因是缺战略，工具再强也救不了没方向的项目。
The Machine Learning Canvas: Empirical Findings on Why Strategy Matters More Than AI Code Generation
- 用四维框架整合战略、流程、生态与支持，系统分析成功要素。
- 组织支持每提升一单位，策略清晰度提升0.432，基础设施改善0.547。
- 强调战略比代码生成更重要，适合管理者和团队负责人阅读。
尽管AI编程助手日益流行，超过80%的机器学习（ML）项目仍无法创造实际商业价值。本研究构建并验证了「机器学习画布」框架，融合业务战略、软件工程与数据科学，识别出四项关键成功因素：战略（明确目标与规划）、流程（工作执行方式）、生态（工具与基础设施）和支持（组织背书与资源）。通过对150名数据科学家的调查与统计建模发现，这些因素相互关联——强组织支持可显著提升策略清晰度（β=0.432, p<0.001），进而优化流程（β=0.428, p<0.001）并完善基础设施（β=0.547, p<0.001）。结果表明，尽管AI加速编码实现，但无法替代战略思考。工具解决‘如何做’，而成功取决于‘为何做’与‘做什么’。
原文摘要 · Abstract (English)
Despite the growing popularity of AI coding assistants, over 80% of machine learning (ML) projects fail to deliver real business value. This study creates and tests a Machine Learning Canvas, a practical framework that combines business strategy, software engineering, and data science in order to determine the factors that lead to the success of ML projects. We surveyed 150 data scientists and analyzed their responses using statistical modeling. We identified four key success factors: Strategy (clear goals and planning), Process (how work gets done), Ecosystem (tools and infrastructure), and Support (organizational backing and resources). Our results show that these factors are interconnected - each one affects the next. For instance, strong organizational support results in a clearer strategy (β= 0.432, p < 0.001), which improves work processes (β= 0.428, p < 0.001) and builds better infrastructure (β= 0.547, p < 0.001). Together, these elements determine whether a project succeeds. The surprising finding? Although AI assistants make coding faster, they don't guarantee project success. AI assists with the "how" of coding but cannot replace the "why" and "what" of strategic thinking.
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