This thesis investigates how organisations can embed environmental sustainability within the operational use of Generative Artificial Intelligence (GenAI) tools through targeted, user-centred interventions. While research has largely focused on reducing emissions in AI model training, the inference phase, where GenAI is integrated into daily workflows, remains a substantial and under-addressed source of environmental impact.

Using a Design Science Research approach, the study combines a literature review, interviews with GenAI users and AI experts, and behavioural theories including the COM-B model, Theory of Planned Behaviour, Nudging, and Affordance Theory. Enabling factors for pro-environmental GenAI use were translated into functional and non-functional requirements, guiding the development of three persona-specific interventions: (1) Sustainable by Default for externally motivated users, embedding energy-efficient model settings and a monitoring dashboard; (2) Sustainability Guidance for aware but uncertain users, offering a sustainable prompt builder and impact estimator widget; and (3) Collective Sustainability for unaware users, providing monthly emissions feedback and rotating green tips.

The resulting integration framework and decision-support tool offer practical guidance for embedding sustainability into organisational AI practices, demonstrating that environmental impact reduction in GenAI requires socio-technical, behavioural, and cultural change alongside technical optimisation.