<![CDATA[Generative artificial intelligence (GenAI) has rapidly become embedded in the strategic communication function, reshaping how organizations produce messages, engage stakeholders, and manage crises. Yet evidence on whether these capabilities strengthen organizational communication effectiveness or undermine public trust remains fragmented across disciplines. This article presents a systematic narrative literature review of twenty-five peer-reviewed studies published largely between 2022 and 2026, synthesizing findings from public relations, information systems, organizational behavior, and communication research. Using thematic synthesis, the review identifies four interrelated clusters: generative AI-enabled message production and personalization, algorithmic transparency and disclosure, trust calibration through tone and competence signaling, and organizational or institutional adaptation. Results show that efficiency and personalization gains are frequently offset by a transparency-trust paradox, whereby disclosing AI authorship can simultaneously legitimize and erode credibility depending on stakeholder AI literacy, message context, and institutional responsibility signaling. The novelty of this study lies in proposing an integrative Trust-Transparency-Effectiveness framework connecting micro-level message design, meso-level organizational practice, and macro-level public trust outcomes. The framework offers strategic communicators, corporate leaders, and policymakers a structured lens for deploying generative AI responsibly while safeguarding organizational legitimacy, stakeholder relationships, and public confidence in an increasingly algorithmically mediated communication environment.]]>
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