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Worldwide, advancements in artificial intelligence (AI) are widely positioned and predicted to spur societal and economic progress and help address challenges such as the growing demands of an ageing population. In long-term care (LTC) for older adults, AI technologies can enhance care quality and alleviate caregiver shortages by improving efficiency. However, alongside these opportunities come societal and ethical concerns, such as the erosion of privacy, autonomy, interpersonal interactions, and risks of bias and opacity. It is therefore essential that those developing and using AI technologies in LTC endorse responsible innovation. This dissertation explores how responsible AI innovation is practically enacted in LTC for older adults. Building on concepts from the discourse on Responsible AI and the field of Responsible Innovation (RI), it offers a practice-based exploration on how to bridge the gap between theory and practical cases of AI development and deployment in LTC. Drawing from empirical and conceptual findings, this dissertation identifies three overarching and interconnected pathways for responsible AI innovation in LTC for older adults, namely: (1) proactive balancing of AI’s opportunities and risks; 2) fostering user-centric learning to guide responsibility by design, configuration, and implementation; and 3) reconciling context-sensitivity with scalability in AI innovations for diverse care settings. These pathways synthesize theoretical insights, empirical evidence, and practical recommendations, outlining strategies, frameworks, and decision-making processes that support the responsible development and deployment of AI technologies in LTC for older adults.