Enhancing Public Sector Decision-Making through Artificial Intelligence Models: A Comparative Study

Abstract

As governments worldwide embrace digital transformation, the role of artificial intelligence (AI) in public policy formulation and analysis has gained unprecedented relevance. This study explores the capabilities and limitations of two advanced AI models (customized ChatGPT and DeepSeek) as decision-support tools. Briefing notes were generated using three different approaches: one by human policy analyst and two by AI models. The aim was to evaluate whether contemporary natural language processing (NLP) technologies can produce briefing notes that are relevant and useful for public policy decision-making. The AI-generated content was tested through simulated policy scenarios to assess performance in tasks such as information retrieval, stakeholder-specific communication, policy brief generation, and scenario analysis. To ensure a robust evaluation, a panel of subject-matter experts assessed the quality of all briefing notes using a structured heuristic evaluation rubric. Results indicate that AI model can enhance analytical capacity, improve policy document drafting, and foster more responsive decision-making. However, the study also identifies critical challenges, including model bias, explainability deficits, and the need for sustained human oversight. Drawing the importance of hybrid governance frameworks that combine AI tools with institutional safeguards. The findings contribute to ongoing discussions on ethical AI integration and provide actionable recommendations for responsibly incorporating large language models into public sector workflows, especially in digitally transforming nations.