Artificial intelligence (AI) initiatives may demonstrate technical feasibility during pilot experimentation without progressing toward sustained organizational deployment. Although existing research identifies AI implementation phases and organizational readiness factors, further understanding is needed of how implementation conditions become differently consequential when organizations attempt to move beyond bounded experimentation. This study examines the pilot-to-scaling transition in Tunisian telecommunications firms through a process-oriented and context-sensitive perspective. Drawing on 11 semi-structured interviews across three telecommunications operators, the study adopts a cross-sectional qualitative design and reconstructs participants' experiences retrospectively. Data were analyzed through an abductive approach informed by Gioia's data-structuring principles, with NVivo matrix coding used as a descriptive analytical aid. Five interconnected dimensions were identified: bounded experimentation and value proving; production exposure and technological interdependence; organizational capability mobilization; local institutional and resource embeddedness; and scaling legitimation and organizational sensemaking. Participants' accounts suggest that movement toward operational deployment makes existing technological and organizational interdependencies more visible and managerially consequential, while requiring sustained capability mobilization and continued organizational justification. The study interprets this transition as an innovation management threshold, understood as a sensitizing interpretive concept rather than a new implementation stage or an established causal mechanism. It further offers a phase-contingent interpretation of implementation conditions and examines how locally embedded institutional and resource conditions enter organizational scaling attempts. The findings highlight the importance of distinguishing pilot readiness from scaling readiness.