<![CDATA[Perkembangan teknologi kecerdasan buatan (Artificial Intelligence/AI) dan ledakan data digital telah mendorong munculnya paradigma baru dalam pengambilan keputusan organisasi yang dikenal sebagai Decision Intelligence (DI). Konsep ini mengintegrasikan pendekatan ilmu data, kecerdasan buatan, ilmu perilaku, dan ilmu keputusan ke dalam satu kerangka kerja yang koheren untuk meningkatkan kualitas dan efektivitas keputusan di berbagai jenis organisasi. Berbeda dari konstruk-konstruk yang mendahuluinya seperti Decision Support Systems (DSS), Business Intelligence (BI), dan Data-Driven Decision Making (DDDM), DI menekankan pemahaman holistik atas konteks, nilai-nilai organisasional, konsekuensi keputusan, dan dimensi manusiawi yang tak tergantikan dalam proses keputusan. Penelitian ini bertujuan memetakan perkembangan literatur DI, menganalisis secara kritis diferensiasi konseptual antara DI dengan DSS, BI, dan DDDM, mengidentifikasi faktor determinan implementasi DI dalam organisasi, serta merumuskan agenda penelitian masa depan khususnya dalam konteks administrasi publik. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan protokol PRISMA terhadap 42 artikel dari Scopus, Web of Science, dan Google Scholar (2019–2024). Temuan mengidentifikasi tiga klaster tematik utama dan empat faktor kritis keberhasilan implementasi DI. Penelitian ini berkontribusi pada pengayaan kerangka konseptual DI dalam perspektif administrasi publik serta menyediakan peta jalan penelitian bagi akademisi dan praktisi di negara berkembang. ABSTRACT The rapid advancement of artificial intelligence (AI) and the explosion of digital data have catalyzed the emergence of a new paradigm in organizational decision-making known as Decision Intelligence (DI). This concept integrates data science, artificial intelligence, behavioral science, and decision science into a coherent framework aimed at enhancing the quality and effectiveness of decisions across various types of organizations. Unlike its predecessor constructs Decision Support Systems (DSS), Business Intelligence (BI), and Data-Driven Decision Making (DDDM) DI places emphasis on a holistic understanding of context, organizational values, decision consequences, and the irreplaceable human dimension in decision-making processes. This study aims to map the development of DI literature, critically analyze the conceptual differentiation between DI and DSS, BI, and DDDM, identify the determinant factors of DI implementation in organizations, and formulate a future research agenda particularly within the context of public administration. A Systematic Literature Review (SLR) following the PRISMA protocol was employed, covering 42 articles from Scopus, Web of Science, and Google Scholar (2019–2024). The findings identify three main thematic clusters and four critical success factors for DI implementation. This study contributes to the enrichment of DI's conceptual framework from a public administration perspective and provides a research roadmap for academics and practitioners in developing countries.]]>
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