Designing AI-Assisted Policy Intelligence for Agricultural Digital Government: A Case Study of the Mexican Agricultural Policy Observatory

Public administrations increasingly publish programmatic and sectoral data, yet fragmented formats, inconsistent terminology, and uneven documentation limit their joint use. This article presents the design and functional evaluation of the Mexican Agricultural Policy Observatory (MAPO), a digital artifact that integrates agricultural support programs with territorial and production indicators for Mexico’s 32 states. Following a design science research approach, the study constructed a 429-record documentary inventory and retained 407 records with both a recognized state and a validated primary category as the main analytical base. This base was used to generate a 32-by-8 descriptive benchmarking matrix, interactive maps, state profiles, downloadable reports, and a structured-context generative artificial intelligence module. Input acquisition, financing, and technification accounted for 71.01% of the comparable analytical base, while state comparisons revealed marked variation in program counts and thematic diversity. A bounded audit of 50 GenAI outputs showed that prompt constraints did not eliminate causal language, policy prescriptions, or count-to-impact equivalences. MAPO separates deterministic calculations from generative interpretation and requires human review before narrative outputs are used. The study contributes a replicable approach to traceable data integration and design principles for AI-assisted public-sector observatories. MAPO supports exploration, communication, and agenda setting; it does not evaluate program impact, budget adequacy, or beneficiary coverage.