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It proposes a three-phase implementation strategy—inception, consolidation, and optimization—designed to incrementally build capacity, establish governance structures, and optimize AI systems over time, enabling improved efficiency, better taxpayer services, strengthened compliance, and sustained public trust and transparency.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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AI INTEGRATION MODEL FOR REVENUE ADMINISTRATIONS  \nINSTITUTIONS  \nINSTITUTIONS  \nINSTITUTIONS  \nINSTITUTIONS  \nPublic Disclosure A thorizedINSTITUTION  P blicNDSicITlos reUTI NS Authorized | INSTITUTIONPublic Disc los re Authorized|   \nINSTITUTIONS   INSTITUTIONS | INSTITUTIONSINSTITUTIONS| INSTITUTIONS  \nINSTITUTIONS  \nINSTITUTIONS  \nINSTITUTIONS  \nINSTITUTIONS  \nNSTIT UT IONS  \nINSTITUTIONS Pub lic Dis| c losureINSTITUTIONA thorized  \nNSTIT UT IONS | INSTITUTIONS |  \n  INSTITUTIONS | INSTITUTIONS  \nRaúl Junquera, Ivan Krsul, Vladimir Calderón, Joey Ghaleb, and Cristian Lucas  \nGOVERNANC  \nGOVERNANCE  \nGOVERNANCE  \nGOVERNANCE  \nGOVERNANCE  \nGOVERNANCE  \nFROM THEORY TO PRACTICE: A STRATEGIC AI INTEGRATION MODEL FOR REVENUE ADMINISTRATIONS  \nRaúl Junquera, Ivan Krsul, Vladimir Calderón, Joey Ghaleb, and Cristian Lucas  \n© 2025 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW  \nWashington DC 20433 Telephone: 202-473-1000  \n[Internet:](Internet: www.worldbank.org)[ ](Internet: www.worldbank.org)[www.worldbank.org](Internet: www.worldbank.org)   \nThis work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent.  \nThe World Bank does not guarantee the accuracy, completeness, or currency of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. The boundaries, colors, denominations, links/footnotes and other information shown in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. The citation of works authored by others does not mean the World Bank endorses the views expressed by those authors or the content of their works.  \nNothing herein shall constitute or be construed or considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved.  \nRights and Permissions  \nThe material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given.  \nAny queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522- 2625; e-mail: [pubrights@worldbank.org](pubrights@worldbank.org).  \nTABLE  \nOF CONTENTS  \nAbstract  vi  \n1. Introduction  1  \n2. A Brief Overview of AI  4  \n3. Use of AI in Revenue Administration  7  \n3.1 Objectives and Goals of AI Integration into a Revenue Administration  8  \n3.2 Potential Impact of AI on the Organizational Structure and Operations of  \nRevenue Administrations 9  \n3.3 The Importance of Human-AI Collaboration  11  \n3.4 The Evolutionary Nature of AI Development  12  \n3.5 Challenges in Implementing AI Without a Framework  14  \n4. Data Governance and its Relation to Synthetic Data  15  \n5. A Strategic Framework for AI Integration  17  \n5.1 Key Components of the Framework  18  \n5.2 Framework Genesis  19  \n5.3 Phases  20  \n 5.3.1 Inception Phase  20  \n 5.3.2 Consolidation Phase  22  \n 5.3.3 Optimization Phase  22  \n5.4 Framework Roadmap  24  \n6. Use Cases  27  \n6.1 AI in Tax Administration  29  \n6.2 AI in Customs Administration  38  \n7. Conclusion  43  \nReferences  45  \nABSTRACT  \nThis paper presents a comprehensive strategic framework for integrating Artificial Intelligence (AI) into revenue administrations. The framework addresses the challe","cbCainFHpD5sbwG4","https://ap.wps.com/l/cbCainFHpD5sbwG4","pdf",20044265,63,"English","# Abstract\n# 1. Introduction\n# 2. A Brief Overview of AI\n# 3. Use of AI in Revenue Administration\n## 3.1 Objectives and Goals of AI Integration into a Revenue Administration\n## 3.2 Potential Impact of AI on the Organizational Structure and Operations of Revenue Administrations\n## 3.3 The Importance of Human-AI Collaboration\n## 3.4 The Evolutionary Nature of AI Development\n## 3.5 Challenges in Implementing AI Without a Framework\n# 4. Data Governance and its Relation to Synthetic Data\n# 5. A Strategic Framework for AI Integration\n## 5.1 Key Components of the Framework\n## 5.2 Framework Genesis\n## 5.3 Phases\n## 5.3.1 Inception Phase\n## 5.3.2 Consolidation Phase\n## 5.3.3 Optimization Phase\n## 5.4 Framework Roadmap\n# 6. Use Cases\n## 6.1 AI in Tax Administration\n## 6.2 AI in Customs Administration\n# 7. Conclusion\n# References","[{\"question\":\"What does the paper propose for integrating AI into revenue administrations?\",\"answer\":\"It proposes a comprehensive strategic framework and a three-phase implementation strategy: inception, consolidation, and optimization.\"},{\"question\":\"Why does the framework emphasize human-AI collaboration?\",\"answer\":\"The framework addresses challenges of unstructured AI implementation and stresses collaboration to support governance, operational effectiveness, and fair outcomes.\"},{\"question\":\"What governance and implementation challenges are highlighted for AI adoption?\",\"answer\":\"The paper focuses on data governance, model transparency, potential bias, public trust, and the explainability of AI models used in tax administration.\"}]","FROM THEORY TO PRACTICE - A STRATEGIC AI INTEGRATION MODEL FOR REVENUE ADMINISTRATIONS | PDF",159]