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.

翻译：尽管AI编码助手日益普及，超过80%的机器学习项目仍未能实现真正的商业价值。本研究创建并测试了"机器学习画布"——一个融合商业战略、软件工程与数据科学的实践框架，旨在确定影响机器学习项目成功的关键因素。通过对150位数据科学家的问卷调查及统计建模分析，我们识别出四大成功要素：战略（清晰的目标与规划）、流程（工作执行方式）、生态（工具与基础设施）以及支持（组织支持与资源）。研究结果表明这些要素相互关联并形成传导链：例如强有力的组织支持能显著提升战略清晰度（β= 0.432, p < 0.001），进而改善工作流程（β= 0.428, p < 0.001）并构建更完善的基础设施（β= 0.547, p < 0.001）。这些要素共同决定了项目的成败。令人惊讶的发现是：虽然AI助手能提升编码效率，却无法确保项目成功。AI仅能解决"如何编码"的技术问题，而无法替代战略思考中"为何而做"与"做什么"的核心决策。