Serendipity Without Intrusiveness: Recommending Conversation Opportunities through Norm-Governed Initiative in Proactive AI Assistants

As general-purpose AI assistants move beyond purely reactive prompting to engage users proactively, they increasingly function as recommenders. Yet treating proactive assistance as a feed-ranking problem mischaracterizes both what is being recommended and why the intervention is normatively consequential. In many cases, the system is not choosing an item for exposure but a conversation opportunity—a topic to reopen, a question to ask, a plan to resume, or an update to surface at a particular moment. Whether such interventions are acceptable depends not only on relevance and timing but also on social norms, including what role the assistant is entitled to play, which memories or signals it may use, and when initiative feels helpful rather than inappropriate or overly intrusive. This paper argues that proactive AI assistants should therefore pursue serendipity without intrusiveness through norm-governed initiative. Synthesizing work across proactive dialogue, human-computer interaction, recommender systems, interruption research, privacy, personalization, and normative AI, this paper shows that the literature still lacks a unified account of conversation opportunities as the recommended object and of when assistant initiation is legitimate. Building on this gap, this position paper develops assistant logic as a norm-governed policy over showing, deferring, or suppressing conversation opportunities. The policy is organized by user sovereignty, abstention, provenance, and long-term value, with social norms constraining their application in context. It yields four concrete design commitments: explicit show–defer–suppress trigger policies, source-specific governance of memory and connected signals, interaction mechanisms for explanation and repair, and evaluation that rewards appropriate restraint and long-term trust rather than immediate uptake alone. Together, these commitments recast persistent memory as a governed basis for initiative, enabling useful surprise without illegible or unauthorized overreach.