Governing the Issue: Strategic Delayed Moralization of AI in Law Enforcement

Artificial intelligence (AI) technologies are typically first adopted as embodiments of rationality and neutrality, and only later do their moral implications receive public scrutiny. This temporal gap between adoption and moral recognition has been widely observed, yet how organizations actively sustain it remains undertheorized. This study theorizes strategic delayed moralization as the process through which organizations actively shape the timing and trajectory of AI transition from technical artifacts to morally recognized objects. We focus on ShotSpotter, an AI-powered gunshot detection system used in law enforcement, analyzing a decade of organizational communications and public discourse. Combining issue management and strategic ambiguity theory, we develop a process model showing how organizations deploy evolving forms of strategic ambiguity to delay moral recognition, and how this process is ultimately constrained by societal moralization that progressively narrows the space for ambiguity. This research contributes to research on AI in society by showing how business and society interact in shaping the moral trajectory of AI.