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

1. Introduction

Public procurement represents a critical component of governmental operations, accounting for approximately 12-20% of global GDP and serving as the primary mechanism through which governments acquire goods, services, and infrastructure projects

[1]

. In developing economies like Peru, public procurement constitutes approximately 15% of GDP, equivalent to over USD 30 billion annually, making it one of the most significant areas of public expenditure and a critical domain for ensuring efficient resource allocation, transparency, and accountability

[2]

. However, traditional procurement systems face substantial challenges including corruption risks, inefficiencies, cost overruns, delivery delays, and inadequate risk management frameworks

[3, 4]

The emergence of Artificial Intelligence (AI) technologies, particularly machine learning algorithms capable of pattern recognition, predictive analytics, and automated decision support, offers unprecedented opportunities for transforming public procurement processes

[5]

. Recent advances in natural language processing, data mining, and predictive modeling have demonstrated significant potential for enhancing various aspects of procurement management, including supplier evaluation, contract monitoring, fraud detection, and risk assessment

[6, 7]

. International organizations such as the OECD, World Bank, and United Nations have recognized AI as a key enabler for modernizing public administration and improving governance outcomes

[8]

In Peru, the enactment of Law 32069 (General Public Procurement Law) in November 2024, which became effective in April 2025, represents the most comprehensive reform of the country's procurement regulatory framework in over two decades

[9]

. A fundamental innovation introduced by this legislation is the mandatory requirement for public entities to implement systematic risk management throughout all phases of the procurement cycle, from planning and market analysis through contract execution and closure

[10]

. OECE Communication 016-2025 further establishes specific technical guidelines for risk identification, assessment, mitigation, and monitoring, creating both opportunities and challenges for integrating AI-based solutions into these newly mandated processes

[11]

Despite growing interest in AI applications for public administration, empirical research examining the practical implementation, effectiveness, and challenges of AI-powered risk management systems in public procurement remains limited, particularly in Latin American contexts

[12]

. Most existing studies focus on theoretical frameworks, conceptual models, or developed economy contexts, leaving significant gaps in understanding how AI technologies can be effectively deployed in resource-constrained public sector environments characterized by limited technical infrastructure, capacity constraints, and complex institutional dynamics

[13, 14]

This research addresses these gaps by investigating the following research questions: (1) How can AI technologies be effectively integrated into public procurement risk management processes under Peru's Law 32069 framework? (2) What is the comparative effectiveness of AI-powered risk assessment systems versus traditional manual methods in identifying and predicting procurement risks? (3) What are the primary implementation barriers, enablers, and organizational factors influencing AI adoption in public procurement contexts? (4) What policy recommendations and practical guidelines can support successful AI implementation in public sector procurement systems?

The study makes several important contributions to both academic literature and professional practice. Theoretically, it extends the emerging body of knowledge on AI applications in public administration by providing empirical evidence from a developing economy context. Practically, it offers actionable insights for policymakers, procurement officials, and technology developers working to modernize public sector operations through digital transformation initiatives. The findings are particularly timely given Peru's ongoing implementation of Law 32069 and the broader trend toward digital government transformation across Latin America.

2. Literature Review and Theoretical Framework

2.1. Artificial Intelligence in Public Administration

The application of AI technologies in public administration has evolved significantly over the past decade, transitioning from experimental pilot projects to mainstream adoption across various governmental functions

[15]

. AI encompasses a broad range of technologies including machine learning, natural language processing, computer vision, and robotic process automation, each offering distinct capabilities for enhancing public sector operations

[16]

. Empirical studies have documented successful AI implementations in areas such as tax administration

[17]

, healthcare delivery

[18]

, public safety

[19]

, and citizen services

[20]

, demonstrating measurable improvements in efficiency, accuracy, and service quality.

However, scholars have also identified significant challenges associated with AI adoption in governmental contexts, including algorithmic bias and fairness concerns

[21]

, transparency and explainability requirements

[22]

, data privacy and security issues

[23]

, and organizational resistance to technological change

[24]

. The public sector presents unique implementation environments characterized by complex stakeholder dynamics, rigid regulatory frameworks, accountability requirements, and political considerations that differ substantially from private sector contexts

[25]

2.2. Risk Management in Public Procurement

Risk management in public procurement has emerged as a critical concern for governments worldwide, particularly following high-profile cases of corruption, fraud, and mismanagement that have undermined public trust and resulted in substantial financial losses

[26]

. The OECD defines procurement risk management as "the systematic application of policies, procedures, and practices to identify, analyze, assess, and manage risks throughout the procurement cycle"

[27]

. Traditional approaches to procurement risk management have relied heavily on manual processes, expert judgment, and ex-post auditing mechanisms that often detect problems only after significant damage has occurred

[28]

Research has identified multiple categories of procurement risks including operational risks (delays, cost overruns, quality deficiencies), compliance risks (regulatory violations, procedural irregularities), financial risks (budget constraints, payment disputes), reputational risks (public scrutiny, media attention), and integrity risks (corruption, collusion, fraud)

[29, 30]

. Effective risk management requires not only identifying potential risks but also assessing their likelihood and impact, implementing appropriate mitigation strategies, and continuously monitoring risk indicators throughout the procurement lifecycle

[31]

2.3. AI Applications in Public Procurement Risk Management

The intersection of AI technologies and procurement risk management represents an emerging research frontier with substantial practical implications

[32]

. Machine learning algorithms have demonstrated particular promise for analyzing large volumes of procurement data to identify patterns, anomalies, and risk indicators that would be difficult or impossible for human analysts to detect manually

[33]

. Recent studies have explored AI applications for various procurement functions including supplier risk assessment

[34]

, bid evaluation and optimization

[35]

, contract management and monitoring

[36]

, and fraud detection

[37]

Predictive analytics, powered by supervised learning algorithms such as random forests, gradient boosting, and neural networks, enable procurement officials to forecast potential risks before they materialize, allowing proactive intervention rather than reactive response

[38]

. Natural language processing techniques can analyze contract documents, tender specifications, and bidder proposals to identify ambiguities, inconsistencies, or problematic clauses that may lead to disputes or execution problems

[39]

. Network analysis algorithms can detect suspicious patterns in supplier relationships, bidding behaviors, and awarding patterns that may indicate collusion or other integrity violations

[40]

2.4. Peruvian Public Procurement Reform and Law 32069

Peru's public procurement system has undergone significant evolution over the past two decades, from the initial Law 26850 (1997) through Law 28650 (2005-2008) and Law 30225 (2014-2025), each iteration seeking to enhance transparency, competition, and efficiency

[41]

. However, persistent challenges including corruption scandals (notably the Lava Jato and Club de la Construcción cases), inefficiencies in contract execution, and limited risk management capabilities motivated comprehensive reform efforts culminating in Law 32069

[42]

Law 32069 introduces several fundamental innovations including: (1) mandatory risk-based procurement planning and execution, (2) expanded use of framework agreements and centralized procurement, (3) enhanced supplier registration and qualification requirements, (4) stronger contract management and supervision mechanisms, and (5) integrated digital procurement platforms

[43]

. Articles 15-18 of the law specifically mandate that public entities must identify, assess, mitigate, and monitor risks throughout all procurement phases, with detailed technical requirements established in OECE Communication 016-2025

[44]

. These requirements create both opportunities and imperatives for implementing technological solutions, including AI-based systems, to support effective risk management.

3. Research Methodology

3.1. Research Design and Approach

This study employs a mixed-methods research design combining quantitative analysis of procurement data with qualitative assessment of implementation experiences and stakeholder perspectives

[45]

. The research was conducted in three phases: (1) Development and validation of an AI-based risk assessment model using historical procurement data, (2) Pilot implementation of the AI system in three Peruvian public entities, and (3) Comparative evaluation of AI-powered versus traditional risk assessment methods, supplemented by qualitative interviews and focus groups with procurement officials and technology users.

3.2. Data Sources and Collection

Quantitative data was collected from SEACE (Sistema Electrónico de Contrataciones del Estado), Peru's official electronic procurement platform, covering 2,847 procurement processes executed by public entities across three administrative levels (national, regional, and local) during the period January 2023 to December 2024. The dataset included information on tender specifications, bid submissions, contract awards, execution timelines, budget allocations, amendments, and completion outcomes. Additional variables related to entity characteristics, procurement modalities, sector categories, and geographic locations were incorporated to enable comprehensive risk factor analysis.

Qualitative data was gathered through semi-structured interviews with 45 procurement officials representing diverse functional roles (planning, selection, execution, supervision) and organizational contexts. Additionally, six focus groups sessions involving 8-12 participants each were conducted to explore implementation experiences, identify barriers and enablers, and gather user feedback on the AI system's functionality and usability. Documentary analysis of policy documents, technical guidelines, and institutional reports complemented the primary data collection.

3.3. AI Model Development and Training

The AI risk assessment model was developed using a supervised machine learning approach, employing ensemble methods combining random forest and gradient boosting algorithms to predict three primary risk categories: (1) execution delays (completion time exceeding planned duration by >20%), (2) cost overruns (final cost exceeding approved budget by >15%), and (3) compliance violations (documented procedural irregularities or legal violations). The model incorporated 127 predictor variables including entity characteristics, procurement specifications, market conditions, supplier attributes, and process indicators.

Model training utilized 70% of the dataset (1,993 procurement processes), with 15% allocated for validation (427 processes) and 15% for final testing (427 processes). Feature engineering included creating derived variables such as supplier performance indices, entity procurement complexity scores, market competitiveness measures, and temporal risk indicators. Hyperparameter optimization was performed using cross-validation techniques, and model performance was evaluated using standard classification metrics including accuracy, precision, recall, F1-score, and area under the ROC curve (AUC).

3.4. Pilot Implementation Strategy

Three public entities representing different organizational contexts were selected for pilot implementation: (1) a large national-level entity (EsSalud - social security system) with high procurement volume and complexity, (2) a medium-sized regional government with moderate technical capacity, and (3) a smaller municipal government with limited resources and infrastructure. The pilot phase lasted six months (April-September 2025), during which procurement officials used the AI system alongside traditional manual risk assessment methods, enabling direct comparison of approaches and identification of implementation challenges in realistic operational contexts.

3.5. Ethical Considerations and Limitations

All research activities were conducted in compliance with institutional ethics guidelines and Peruvian data protection regulations. Interview participants provided informed consent, and all identifying information was anonymized in data analysis and reporting. The study acknowledges several limitations including potential selection bias in pilot entity participation, limited generalizability beyond the Peruvian context, and the relatively short implementation period constraining long-term impact assessment. These limitations are addressed through transparent reporting and careful interpretation of findings.

4. Results and Findings

4.1. AI Model Performance and Accuracy

The AI risk assessment model demonstrated strong predictive performance across all three risk categories. For execution delay prediction, the model achieved 78.3% overall accuracy, with precision of 76.5%, recall of 81.2%, and F1-score of 78.8%. Cost overrun prediction yielded 74.7% accuracy with precision of 73.1% and recall of 77.9%. Compliance violation detection achieved 82.1% accuracy with particularly high precision (85.3%) indicating low false positive rates. The ensemble approach outperformed individual algorithms, with the combined random forest-gradient boosting model showing 7-12% improvement in accuracy compared to single-method approaches.

Feature importance analysis revealed that the most influential predictors for execution delays included entity procurement complexity scores (importance: 0.184), supplier historical performance indices (0.156), contract value relative to entity annual budget (0.143), and technical specification complexity measures (0.128). For cost overrun prediction, market competitiveness indicators (0.167), initial budget adequacy ratios (0.159), and project technical complexity (0.144) emerged as dominant factors. Compliance violation prediction was most strongly associated with entity governance quality scores (0.192), procurement modality selection (0.171), and process timeline characteristics (0.138).

4.2. Comparative Analysis: AI versus Traditional Methods

During the six-month pilot implementation period, procurement officials using the AI system identified potential risks in 67.8% of processes subsequently experiencing problems, compared to 46.9% identification rate using traditional manual assessment methods alone. This represents a 44.5% relative improvement in risk detection effectiveness. The AI system demonstrated particular advantages in identifying subtle risk patterns involving multiple interacting factors that human analysts struggled to recognize manually. For example, the AI model correctly predicted 83.7% of cases where moderate individual risk factors combined to create high overall risk, compared to 52.3% detection rate by manual methods.

Time efficiency analysis revealed that AI-assisted risk assessment reduced average assessment time from 4.7 hours per procurement process (traditional manual approach) to 0.8 hours (AI-assisted approach), representing 83%-time savings. However, qualitative interviews revealed that procurement officials initially spent additional time learning the system and validating AI-generated risk assessments, with efficiency gains becoming more pronounced after 2-3 months of regular use as users developed familiarity and trust in the system's capabilities.

4.3. Implementation Barriers and Challenges

Qualitative analysis of implementation experiences identified several significant barriers affecting AI system adoption and effectiveness. Data quality emerged as the most frequently cited challenge, with 78% of interviewees reporting problems including incomplete historical records, inconsistent data formats, and missing critical variables. The smaller municipal pilot entity experienced particular difficulties, with approximately 35% of historical procurement records lacking sufficient detail for effective model training.

Technical capacity constraints represented another major implementation barrier, particularly in smaller entities with limited IT infrastructure and specialized personnel. Interview participants identified needs for enhanced training programs, technical support mechanisms, and simplified user interfaces. Cultural and organizational resistance to technological change was also notable, with some procurement officials expressing skepticism about AI reliability, concerns about job displacement, and preference for traditional familiar methods over new technology-based approaches.

4.4. Success Factors and Best Practices

Despite implementation challenges, the research identified several factors contributing to successful AI adoption. Strong organizational leadership commitment and change management support emerged as critical success factors, with the national-level pilot entity (which demonstrated highest adoption success) having dedicated champion leaders who actively promoted the initiative, provided resources, and addressed staff concerns. Comprehensive training programs combining technical instruction with change management elements proved more effective than purely technical training approaches.

User-centered design principles incorporating procurement official feedback into system refinement generated positive responses and increased adoption rates. Features particularly appreciated by users included intuitive visual dashboards, explainable AI outputs providing rationales for risk predictions, customizable risk thresholds allowing user judgment, and integration with existing procurement platforms minimizing workflow disruption. Incremental implementation approaches starting with pilot testing before full-scale rollout allowed organizations to identify and address problems while building staff confidence and competence.

5. Discussion and Implications

5.1. Theoretical Contributions and Academic Implications

This research contributes to the growing scholarly literature on AI applications in public administration by providing empirical evidence from a developing economy context, addressing a significant gap in existing research that has predominantly focused on developed nations with substantially different institutional, technical, and resource contexts. The findings support the Technology Acceptance Model (TAM) framework

[46]

, demonstrating that perceived usefulness and ease of use significantly influence technology adoption decisions among public sector employees. However, the research also reveals contextual factors specific to public sector environments including regulatory compliance requirements, accountability pressures, and organizational culture dynamics that may require extensions to existing theoretical models.

The study's comparative analysis demonstrating AI system superiority over traditional manual methods provides quantitative validation for emerging theories on augmented intelligence in governmental decision-making

[47]

. Rather than replacing human judgment, the AI system functioned most effectively as a decision support tool augmenting procurement officials' analytical capabilities, particularly for processing large datasets and identifying complex risk patterns. This finding aligns with scholarly perspectives emphasizing human-AI collaboration rather than automation as the optimal approach for public sector contexts requiring accountability, transparency, and professional judgment

[48]

5.2. Practical Implications for Policy and Practice

For policymakers and government officials implementing Peru's Law 32069, these findings offer several actionable insights. First, AI-based risk management systems represent viable and effective tools for meeting the law's mandatory risk management requirements, particularly for larger entities processing substantial procurement volumes. However, successful implementation requires more than technology deployment; it demands comprehensive change management strategies addressing organizational culture, staff capacity development, data infrastructure improvement, and sustained leadership commitment.

Second, the research reveals the critical importance of data quality and infrastructure as foundational prerequisites for effective AI implementation. Policymakers should prioritize investments in procurement data standardization, digital platform integration, and historical data enhancement before or concurrent with AI system deployment. OSCE (Organismo Supervisor de las Contrataciones del Estado) could play a vital coordinating role by establishing national data standards, providing technical assistance to smaller entities, and developing shared AI infrastructure that smaller jurisdictions can access without requiring individual system development.

Third, the findings highlight the need for differentiated implementation strategies recognizing diverse organizational capacities and contexts. Large national entities may effectively develop and deploy sophisticated AI systems independently, while smaller municipalities may benefit more from accessing centralized systems or simplified tools requiring minimal technical infrastructure. Policy approaches should provide flexibility allowing entities to select implementation paths aligned with their specific capabilities and circumstances.

5.3. Study Limitations and Future Research Directions

Several limitations should be considered when interpreting these findings. First, the six-month pilot implementation period, while sufficient for initial assessment, precludes evaluation of long-term impacts, user adaptation patterns, and sustainability outcomes. Longitudinal research tracking AI system performance and organizational effects over 2-3 year periods would provide valuable insights into maturation processes and persistent challenges. Second, the study's focus on three pilot entities, while enabling deep contextual analysis, limits statistical generalizability across Peru's diverse public sector landscape. Future research could expand to larger entity samples representing broader organizational diversity.

Third, the research primarily examines technical and organizational dimensions of AI implementation, with limited attention to political, ethical, and societal implications including algorithmic accountability, transparency requirements, and public trust considerations. Scholarly investigation of these broader dimensions would enhance understanding of AI's role in democratic governance. Fourth, the study focuses specifically on risk management applications; future research could explore AI potentials for other procurement functions including supplier development, market intelligence, contract optimization, and performance monitoring.

6. Conclusions and Recommendations

This research demonstrates that artificial intelligence technologies offer substantial potential for enhancing public procurement risk management effectiveness, particularly in contexts like Peru where new regulatory frameworks (Law 32069) mandate comprehensive risk management throughout procurement cycles. The AI-based risk assessment system developed and tested in this study achieved 78.3% prediction accuracy and demonstrated 45% improvement over traditional manual methods, while also reducing assessment time by 83%. These performance improvements translate directly to enhanced governmental capabilities for identifying and mitigating procurement risks before they result in delays, cost overruns, or compliance violations.

However, realizing these benefits requires addressing significant implementation challenges including data quality issues, technical capacity constraints, and organizational resistance to change. Success factors identified through the pilot implementation experience emphasize the importance of strong leadership commitment, comprehensive change management, user-centered design, adequate training and support, and phased implementation approaches that allow organizational learning and adaptation.

6.1. Policy Recommendations

Based on research findings, several specific recommendations emerge for Peruvian policymakers, procurement officials, and technology developers: (1) OSCE should develop national AI infrastructure and tools that smaller entities can access as shared services, reducing individual development costs and technical requirements. (2) Regulatory guidelines should incorporate AI-based risk management as an approved methodology under Law 32069, while establishing quality standards, transparency requirements, and accountability mechanisms. (3) Government should invest in procurement data standardization and quality improvement initiatives as foundational prerequisites for effective AI implementation. (4) Comprehensive training programs should address both technical skills and organizational change management, preparing procurement professionals for evolving technology-enabled work environments. (5) Implementation approaches should emphasize human-AI collaboration rather than full automation, preserving professional judgment and accountability while leveraging AI analytical capabilities.

6.2. Future Perspectives

Looking forward, AI technologies will likely play increasingly important roles in public procurement and broader public administration domains as technical capabilities advance and implementation experience accumulates. Beyond risk management, promising applications include intelligent contract drafting assistance, automated supplier qualification screening, market intelligence analysis, and real-time contract monitoring. However, realizing these potentials requires sustained attention to ethical considerations, transparency requirements, accountability mechanisms, and inclusive approaches ensuring that digital transformation benefits all segments of society rather than creating new forms of exclusion or inequality.

This research contributes foundational empirical evidence supporting informed decision-making about AI adoption in public procurement contexts. As Peru and other Latin American nations navigate digital transformation journeys, evidence-based approaches grounded in rigorous research, pilot testing, and continuous learning will be essential for maximizing benefits while mitigating risks associated with emerging technologies in governmental operations.

Abbreviations

AI	Artificial Intelligence
AUC	Area Under the ROC Curve
GDP	Gross Domestic Product
MIGR-32069	Integrated Risk Management Model Under Law 32069
ML	Machine Learning
NLP	Natural Language Processing
OECE	Executive Office of State Procurement (Oficina Ejecutiva de Contrataciones del Estado)
OECD	Organisation for Economic Co-operation and Development
OSCE	Supervisory Body for Government Procurement (Organismo Supervisor de las Contrataciones del Estado)
ROC	Receiver Operating Characteristic
SEACE	Electronic System for Government Procurement (Sistema Electrónico de Contrataciones del Estado)
TAM	Technology Acceptance Model

Acknowledgments

The author gratefully acknowledges the support of EsSalud and Universidad Continental in facilitating this research. Special thanks to the procurement officials who participated in interviews and pilot testing, providing invaluable insights and feedback. The views expressed in this paper are those of the author and do not necessarily represent official positions of affiliated institutions.

Author Contributions

Juan Carlos Rodríguez Luna: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Conflicts of Interest

The author declares no conflicts of interest.

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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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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
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  -

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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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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}
}

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