一句话：提出 AI 赋能框架，在采用 CI 前评估项目适配性、推荐服务并给出配置指导。
持续集成（CI）在现代软件开发中广泛采用，但采用决策常缺少对项目上下文的系统考量。GitHub Actions 等平台降低了 CI 采用门槛，却难以为基于项目特征的采用决策提供支持，导致冗余服务、无人维护的工作流和高成本迁移。现有研究与工具多聚焦采用后的 CI 改进，很少指导采用前的适配性评估，使 CI 常被视为普遍有益而非依赖上下文。本文提出从默认采用转向审慎的上下文感知决策：构建一个 AI 赋能框架，判断项目是否可能从 CI 受益，依据项目特征推荐合适的 CI 服务，并提供定制化配置指导。作者还给出研究议程，结合开发者研究、大规模仓库挖掘与推荐系统设计，以支持知情的 CI 采用决策并预防低效。
Continuous Integration (CI) is widely adopted in modern software development, yet adoption decisions are often made without systematic consideration of project context. Platforms such as GitHub Actions lower the barrier to CI adoption but provide limited support for grounding adoption decisions in project characteristics, leading to redundant services, unmaintained workflows, and costly migrations. Existing research and tooling primarily focus on improving CI after adoption, offering little guidance for assessing suitability before adoption. As a result, CI is frequently treated as universally beneficial rather than context-dependent. This paper envisions a shift from default CI adoption to deliberate, context-aware decision-making. We propose an AI-enabled framework that assesses whether projects are likely to benefit from CI, recommends suitable CI services based on project characteristics, and provides configuration guidance tailored to project needs. We outline a research agenda combining developer studies, large-scale repository mining, and recommendation system design to enable informed CI adoption decisions and prevent inefficiencies before they occur.