Gov’t agencies urged to shift to prescriptive AI for disaster planning | GovMedia
, Philippines
(L-R) Lance Katigbak, Principal, Boston Consulting Group with panelists Dr. Franz A. de Leon Ph.D, Director IV, Advanced Science and Technology Institute, Department of Science and Technology (DOST-ASTI) and Lee Carlo B. Abadia, Principal, Technology, Consulting, SGV & Co.

Gov’t agencies urged to shift to prescriptive AI for disaster planning

Agencies generate vast hazard, social, and infrastructure data, but sharing remains limited.

Government agencies in the Philippines are shifting from reactive disaster alerts towards using prescriptive artificial intelligence (AI) analytics to guide planning and decision-making, officials said at the GovMedia Summit 2026 on 17 March at the Makati Shangri-La.

Government agencies generate large amounts of hazard, social, and infrastructure data, but sharing across agencies is limited, said Franz de Leon, director of the Department of Science and Technology (DOST)-Advanced Science and Technology Institute.

Projects such as GATES (Geospatial Analytics and Technology Solutions) provide a secure interface to analyse and correlate data without exposing sensitive raw data.

De Leon said the shift to prescriptive analytics allows agencies to use AI to inform infrastructure investment, resource allocation, and policy decisions instead of just alerting during emergencies.

Private sector platforms can process real-time sensor, satellite, and IoT data, automate workflows, and generate actionable recommendations, said Lee Carlo Abadia, partner and head of SyCip Gorres Velayo & Co.’s (SGV & Co.) AI and data practice.

He noted that integrating technology into government structures and ensuring that staff can act on AI outputs remain the main challenges.

The panel highlighted key limitations in adoption, including gaps in human capacity, procurement processes, and understanding of AI.

Agencies are addressing these through training programmes and the Philippines Skills Framework, which provides pathways for data analysts, engineers, and AI specialists.

Infrastructure is another constraint. The CORE (Computing and Archiving Research Environment) provides national computing capacity, but budget cycles and procurement processes can lag behind the fast pace of AI model development.

Panellists said advancing AI adoption would require improvements in infrastructure, data governance, human capacity, and clearly defined use cases.

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