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

Juan Carlos Rodríguez Luna

doi:doi:10.11648/j.hss.20261402.19

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

Juan Carlos Rodríguez Luna^*

Published in Humanities and Social Sciences (Volume 14, Issue 2)

Received: 5 January 2026 Accepted: 26 March 2026 Published: 13 April 2026

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Abstract

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.

Published in	Humanities and Social Sciences (Volume 14, Issue 2)
DOI	10.11648/j.hss.20261402.19
Page(s)	141-149
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2026. Published by Science Publishing Group

Keywords

Artificial Intelligence, Public Procurement, Risk Management, Machine Learning, Digital Transformation, Law 32069, Peru, Public Administration

References

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Luna, J. C. R. (2026). Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069. Humanities and Social Sciences, 14(2), 141-149. https://doi.org/10.11648/j.hss.20261402.19

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ACS Style

Luna, J. C. R. Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069. Humanit. Soc. Sci. 2026, 14(2), 141-149. doi: 10.11648/j.hss.20261402.19

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AMA Style

Luna JCR. Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069. Humanit Soc Sci. 2026;14(2):141-149. doi: 10.11648/j.hss.20261402.19

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@article{10.11648/j.hss.20261402.19,
  author = {Juan Carlos Rodríguez Luna},
  title = {Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069},
  journal = {Humanities and Social Sciences},
  volume = {14},
  number = {2},
  pages = {141-149},
  doi = {10.11648/j.hss.20261402.19},
  url = {https://doi.org/10.11648/j.hss.20261402.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hss.20261402.19},
  abstract = {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.},
 year = {2026}
}

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TY  - JOUR
T1  - Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069
AU  - Juan Carlos Rodríguez Luna
Y1  - 2026/04/13
PY  - 2026
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AB  - 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.
VL  - 14
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Juan Carlos Rodríguez Luna

Sub-Management of Budget Process and Expenditure Quality, EsSalud, Lima, Peru

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Luna, J. C. R. (2026). Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069. Humanities and Social Sciences, 14(2), 141-149. https://doi.org/10.11648/j.hss.20261402.19

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Luna, J. C. R. Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069. Humanit. Soc. Sci. 2026, 14(2), 141-149. doi: 10.11648/j.hss.20261402.19

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AMA Style

Luna JCR. Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069. Humanit Soc Sci. 2026;14(2):141-149. doi: 10.11648/j.hss.20261402.19

Copy | Download

@article{10.11648/j.hss.20261402.19,
  author = {Juan Carlos Rodríguez Luna},
  title = {Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069},
  journal = {Humanities and Social Sciences},
  volume = {14},
  number = {2},
  pages = {141-149},
  doi = {10.11648/j.hss.20261402.19},
  url = {https://doi.org/10.11648/j.hss.20261402.19},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hss.20261402.19},
  abstract = {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.},
 year = {2026}
}

Copy | Download

TY  - JOUR
T1  - Artificial Intelligence in Public Procurement Risk Management: An Implementation Analysis Under Peru's Law 32069
AU  - Juan Carlos Rodríguez Luna
Y1  - 2026/04/13
PY  - 2026
N1  - https://doi.org/10.11648/j.hss.20261402.19
DO  - 10.11648/j.hss.20261402.19
T2  - Humanities and Social Sciences
JF  - Humanities and Social Sciences
JO  - Humanities and Social Sciences
SP  - 141
EP  - 149
PB  - Science Publishing Group
SN  - 2330-8184
UR  - https://doi.org/10.11648/j.hss.20261402.19
AB  - 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.
VL  - 14
IS  - 2
ER  -

Copy | Download