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@article{175231,
        author = {Gaurang Wadhawan},
        title = {Transformative AI Adoption in Rural Public Procurement: A Multinational Longitudinal Study with Policy Implications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {2156-2158},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175231},
        abstract = {This 18-month multinational study presents ir- refutable evidence for AI-driven transformation of rural pro- curement systems, analyzing 584,312 transactions across 127 municipalities in 9 developing nations. Through randomized controlled trials (RCTs) and machine learning analysis, we demonstrate 68.4% reduction in procedural delays (p ¡ 0.001) and 41.7% cost savings (CI: 39.2-44.1%) using ChatGPT/Gemini integrations. Our three-phase implementation framework shows strong correlation between AI adoption and SDG achievement ( = 0.79, SE = 0.03), while addressing ethical concerns through novel Federated Learning architecture. The research introduces a Procurement Maturity Index (PMI) validated by World Bank experts, providing governments with actionable roadmaps for digital transformation. Comprehensive cost-benefit analysis re- veals 3.8:1 ROI within 24 months, establishing AI as essential infrastructure for equitable development.},
        keywords = {Artificial intelligence, public sector innovation, procurement optimization, rural development, SDG implementa- tion, machine learning governance},
        month = {April},
        }