<![CDATA[Abstract: This study proposes an AI Governance Readiness Index (AGRI) to assess higher education institutions' readiness for responsible Generative Artificial Intelligence (GenAI) utilization. AGRI is operationalized as a research index synthesized from contemporary AI governance principles rather than treated as an established standardized instrument. The index comprises eight dimensions: strategy and policy, accountability, data governance, AI risk management, transparency and documentation, human oversight, security and privacy, and AI literacy. A quantitative descriptive cross-sectional design is proposed, supported by document review. Respondents include university leaders, IT managers, quality assurance personnel, lecturers, and academic administrators. Each indicator is scored on a five-point readiness scale, normalized to 0-100, and aggregated using equal dimension weights; gap analysis compares current and expected readiness. As this is a draft article, empirical values are intentionally left for field data rather than fabricated. The expected contribution is a reproducible institutional diagnostic that identifies governance strengths, critical gaps, and improvement priorities for responsible GenAI adoption in higher education. Keywords: AI governance; AGRI; generative AI; higher education; readiness index. Abstrak: Penelitian ini mengusulkan AI Governance Readiness Index (AGRI) untuk menilai kesiapan perguruan tinggi dalam mendukung pemanfaatan Generative Artificial Intelligence (GenAI) secara bertanggung jawab. AGRI dioperasionalkan sebagai indeks penelitian yang disintesis dari prinsip tata kelola AI kontemporer, bukan sebagai instrumen standar yang telah mapan. Indeks mencakup delapan dimensi: strategi dan kebijakan, akuntabilitas, tata kelola data, manajemen risiko AI, transparansi dan dokumentasi, human oversight, keamanan dan privasi, serta literasi AI. Penelitian dirancang secara kuantitatif deskriptif cross-sectional dan didukung telaah dokumen. Responden meliputi pimpinan perguruan tinggi, pengelola TI, penjaminan mutu, dosen, dan pengelola akademik. Setiap indikator dinilai dengan skala kesiapan lima tingkat, dinormalisasi menjadi 0-100, kemudian diagregasi menggunakan bobot dimensi setara; gap analysis membandingkan kondisi aktual dan harapan. Karena naskah ini merupakan draft, nilai empiris tidak dibuat secara fiktif. Kontribusi yang diharapkan adalah instrumen diagnostik institusional yang dapat direplikasi untuk mengidentifikasi kekuatan, kesenjangan kritis, dan prioritas perbaikan tata kelola GenAI. Kata kunci: AI governance; AGRI; generative AI; perguruan tinggi; indeks kesiapan.]]>
