The rapid rise of artificial intelligence (AI) and digital health technologies presents new opportunities for personalized care in multiple sclerosis (MS). However, implementation in routine practice is limited by regulatory hurdles, fragmented infrastructure and a lack of agile real-world evaluation methods. Living Labs (LLs) emerge as dynamic environments for advancing MS care and research, supporting early testing and iterative development of digital tools, while fostering structured collaboration among patients, clinicians, researchers and regulators. In this review, we conceptually frame LLs in MS and provide a concrete, clinic-ready implementation framework for AI-enabled application in real-world practice. Using a digital-based voice task as an exemplar with automated feature extraction, we detail integration patterns and define key performance indicators for feasibility, data quality, usability and clinical utility. We show how this co-designed model can generate decision-relevant evidence, may help shorten time-to-action and embed innovation seamlessly into clinical workflows. Finally, we align LL operations with ethical and regulatory standards and outline strategies to responsibly scale across centres.