Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069

The integration of Artificial Intelligence (AI) in public procurement processes represents a transformative opportunity for enhancing transparency, efficiency, and risk management in government contracting systems. This research examines the implementation of AI-based risk management frameworks within the context of Peru's new General Public Procurement Law 32069, which became effective in April 2025 and mandates comprehensive risk identification and mitigation strategies throughout the procurement cycle. Through a mixed-methods approach combining quantitative analysis of 2,847 procurement processes from Peruvian public entities and qualitative assessment of AI implementation experiences across three pilot institutions, this study evaluates the effectiveness, challenges, and opportunities of deploying machine learning algorithms for predictive risk analytics in public procurement. The findings reveal that AI-powered systems achieved 78.3% accuracy in predicting procurement risks related to delays, cost overruns, and compliance violations, representing a 45% improvement over traditional manual risk assessment methods. However, implementation barriers including data quality issues, limited technical capacity, and resistance to technological change significantly impact adoption rates. This research contributes to the emerging field of digital transformation in public administration by providing empirical evidence on AI applications in governmental processes and offering practical recommendations for policymakers, procurement officials, and technology developers working to modernize public sector operations in developing economies.