Strengthening Policy Capacity through AI-GOV: An AHP-Based Assessment of State-Owned Asset Disposal Policy - Ilman Rezadi, Agil; Saleh, Choirul; Sarwono (Badan Penerbitan Jurnal (BPJ) FIA-UB, 2026)
State-Owned Asset (BMN) Disposal Policy is one of the important stages in the state asset management cycle. However, its implementation still faces significant challenges such as inadequate policy capacity, complex bureaucracy, and database-based technology. With the increasing number of BMN from time to time coming from new BMN procurement, an efficient BMN write-off procedure is also needed so that the amount of BMN managed becomes balanced with the capacity of BMN managers. One of the developments in public administration in the E-Government (E-GOV) era that can be an alternative solution is the use of Artificial Intelligence (AI) based technology. This article aims to analyse how AI-Governance (AI-GOV) can be used to strengthen policy capacity in BMN disposal in Indonesia. This research uses an Analytical Hierarchy Process (AHP) approach, as well as a literature study. The results show that strengthening the operational and analytical capacity of policies at the organisational and system levels is the key to successful BMN disposal policy. AI-GOV accommodates many inputs from respondents and has the potential to strengthen the policy capacity by supporting the process of identification, prediction, classification, recommendation, and even automation in the bureaucracy of BMN proposals that are eligible for disposal, prediction of auction limit prices, automation of cross-unit auction bundling and scheduling, and anomaly audit trail. This study recommends strengthening policy capacity through the application of AI-GOV in the implementation of BMN disposal policy as part of digital and historical data-based policy reform.