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Adaptive radiotherapy (ART) has been shown to improve geometric and dosimetric accuracy, with emerging evidence for clinical benefit, but it remains resource-intensive and lacks scalability. This limitation arises from multiple factors, including the complexity of current systems, the closed and proprietary nature of radiotherapy platforms, and the need for human oversight driven in part by clinical risk considerations. Historically, major advances in radiotherapy-from Intensity-Modulated Radiation Therapy (IMRT) and Image-Guided Radiation Therapy (IGRT) to Magnetic Resonance-guided Radiotherapy (MRgRT) and Deep Learning in Radiotherapy (DLinRT) (particularly for auto-contouring)-have thrived through open collaboration and transparency. The community can accelerate ART innovation by returning to this model. Open-source initiatives such as Computational Environment for Radiotherapy Research (CERR), Open Knowledge-based Planning (OpenKBP), and matRad demonstrate how shared tools and methods improve reproducibility and drive scientific progress. The next critical step is to develop collaborative, structured frameworks that enable safe, secure interaction between academic and vendor systems-safeguarding intellectual property while fostering co-development and validation. Through structured transparency and shared accountability, the radiotherapy field can transform automation from a closed, non-transparent architecture into a collective learning ecosystem, ultimately extending the life-saving benefits of ART to more patients worldwide through openness, trust, and collective innovation.