Research background and purpose:
Artificial intelligence (AI) is increasingly embedded in sustainability initiatives, governance arrangements, and digital communication systems, reshaping how information circulates, decisions are made, and social value is distributed. While AI-driven systems enhance efficiency and data-driven coordination, their organizational deployment also intensifies challenges related to algorithmic opacity, misinformation, legitimacy, social inequality, and fragmented governance. Existing research remains fragmented across technical, ethical, and policy-oriented domains and offers limited insight into how organizations simultaneously manage these interconnected impacts. This study aims to develop an organization-centered understanding of AI’s societal implications by examining how AI-driven transformations are mediated through organizational decision-making and governance practices across media, culture, social identities, and public policy.

Design/methodology/approach:
The study adopts a qualitative, exploratory scoping review methodology. It systematically synthesizes peer-reviewed academic literature and selected international policy frameworks published between 2019 and 2025. Using an inductive thematic synthesis, the review maps recurring patterns and mechanisms through which AI’s social implications materialize within organizational contexts, with a focus on conceptual integration rather than causal evaluation.

Findings:
The findings show that AI’s societal impacts are not direct technological effects but outcomes of organizational governance choices under conditions of fragmented regulation. In digital media, algorithmic ranking, personalization, and moderation restructure information visibility through organizational dependence on opaque platform infrastructures, creating legitimacy and accountability risks. In cultural contexts, AI gains legitimacy through dominant narratives of efficiency and objectivity while reinforcing embedded norms and symbolic authority. Regarding social identities, AI-driven organizational practices reconfigure work roles, agency, and access to resources in socially stratified ways. At the policy level, fragmented AI governance shifts responsibility toward organizations, positioning them as central intermediaries translating regulatory expectations into operational practice.

Value added and limitations:
This study contributes by integrating dispersed literatures into an organization-centered framework that links AI, governance, and social impact. It highlights organizational responsibility as a key determinant of whether AI supports inclusive and sustainable development. The study is limited by its qualitative scoping review design and reliance on secondary sources. Future research should pursue comparative empirical studies and examine organizational decision-making processes in greater depth.