Digital Innovation in Public Administration through Intelligent Public Sector Automation (IPSA): Strategies and Challenges

The emergence of Large Language Models (LLMs) in public administration represents not merely a technological advancement, but a fundamental shift in how administrative systems process and manage information. Traditional database systems and LLMs exhibit distinct operational paradigms that must be carefully understood for successful implementation in public administration contexts [3].

Traditional SQL-based administrative systems operate on principles of deterministic data processing, employing rigid schema definitions and explicit relationships between data elements. These systems ensure data integrity through ACID (Atomicity, Consistency, Isolation, Durability) properties, providing guaranteed accuracy in transaction processing. For instance, when processing citizen records, SQL databases maintain precise relationships between personal information, service history, and eligibility criteria through explicit foreign key relationships and constraint enforcement.

In contrast, LLMs operate on probabilistic principles, utilizing deep learning models trained on massive datasets to understand and generate human-like text. While models like GPT-3 with 175 billion parameters demonstrate remarkable capabilities in natural language processing, their fundamental operation differs significantly from traditional systems. Rather than explicit data relationships, LLMs learn patterns and associations through training, employing transformer architectures to capture contextual relationships in data [4-5].

The architectural differences between these systems manifest in their data processing mechanisms and verification methodologies. Traditional SQL systems execute precise queries with deterministic results, whereas LLMs generate probabilistic responses based on learned patterns. This fundamental difference necessitates the development of hybrid implementation strategies that can maintain the accuracy requirements of public administration while leveraging the advanced capabilities of LLMs. The verification and validation processes also differ substantially, with SQL systems employing direct data verification through constraints, while LLMs utilize confidence scoring and contextual validation mechanisms.

The integration of AI-based process automation in public administration necessitates a sophisticated understanding of both traditional and AI-driven processing mechanisms. This understanding becomes particularly crucial when implementing systems that must maintain the reliability of traditional processing while leveraging the advantages of AI capabilities.

Traditional automation systems have established themselves as reliable tools for processing structured workflows and making deterministic decisions. These systems excel in maintaining clear audit trails and integrating directly with existing infrastructure. The advent of AI-based systems has introduced new capabilities for natural language understanding and adaptive processing, enabling more sophisticated approaches to public service delivery.

The successful integration of these capabilities requires careful consideration of the processing architecture and security implementation. Modern public administration systems must implement hybrid processing frameworks that can validate operations in real-time while maintaining high performance standards. These systems must also incorporate sophisticated security measures, including multi-layer authentication and context-aware authorization protocols, to protect sensitive administrative data.

IPSA represents a comprehensive framework that integrates traditional administrative systems with advanced AI capabilities [6], specifically designed for the unique requirements of public administration. This integration addresses technical implementation, security considerations, and compliance requirements through a cohesive approach to system design and operation.

The technical integration aspect ‘IPSA’ focuses on creating robust hybrid architectures that combine the strengths of both SQL and LLM systems. These architectures incorporate real-time validation systems and error correction mechanisms to ensure consistent accuracy in administrative operations. Performance optimization remains a continuous process, with systems actively monitoring and adjusting operations to maintain optimal efficiency [7].

The fundamental components of IPSA can be categorized into several key assets, as shown in Table 1.

The integration of Large Language Models (LLMs) and Intelligent Public Service Automation (IPSA) presents significant ethical challenges that must be carefully addressed within the public administration context. Drawing from existing literature [8], four fundamental ethical considerations emerge as critical framework components: algorithmic fairness, which ensures equitable treatment across all demographic groups in AI system decisions; transparency and explainability, focusing on the comprehensibility of AI decision-making processes; accountability, establishing clear lines of responsibility for system outcomes; and privacy protection, emphasizing the secure management and appropriate utilization of citizens’ personal information. These ethical considerations serve as cornerstone elements in constructing a comprehensive AI governance framework, requiring continuous evaluation and adaptation throughout the system’s lifecycle from initial design to operational implementation.

While the LLM-IPSA integration holds considerable promise for transformative innovation in public administration, its successful implementation demands a holistic approach that transcends purely technical considerations. This necessitates careful attention to organizational dynamics, social implications, and ethical frameworks working in concert to ensure effective system deployment. To better understand these multifaceted challenges and develop practical solutions, subsequent analysis will focus on the ChatGB case study in Gyeongsangbuk-do, providing empirical insights into the real-world application of LLM based IPSA integration. This case study will serve as a valuable platform for examining how theoretical ethical considerations manifest in practical implementation scenarios.

The fundamental differences between traditional SQL database systems and LLM-based systems create unique challenges in ensuring data accuracy and quality. Traditional SQL databases maintain data integrity through rigid schema definitions [9], explicit relationships, and ACID (Atomicity, Consistency, Isolation, Durability) properties. In contrast, LLM systems operate on probabilistic models and pattern-based learning, which introduces potential accuracy concerns for public administration applications.

To address these challenges, this study proposes a hybrid architecture that leverages the strengths of both systems. This architecture implements a validation middleware layer that acts as a bridge between the SQL database’s structured data and the LLM’s natural language processing capabilities. The middleware performs real-time validation of LLM outputs against the authoritative SQL database, ensuring accuracy while maintaining the benefits of natural language interaction.

Analysis of implementation cases reveals that organizations successfully managing these challenges typically employ a comprehensive quality control framework. For instance, Singapore’s GovTech implementation demonstrates the effectiveness of continuous monitoring systems that track accuracy metrics and trigger manual reviews when necessary [10]. These systems have achieved accuracy rates exceeding 99% in public service applications while maintaining the usability advantages of LLM interfaces.

Security considerations in LLM-based public administration systems extend beyond traditional database security measures. While SQL databases implement security through role-based access control and data encryption, LLM systems require additional security layers to protect against unique vulnerabilities such as prompt injection attacks and output manipulation.

The analysis identifies three critical security components [12-13] for LLM-based public administration systems: First, a multi-layer authentication framework that combines traditional security measures with LLM-specific protections. This includes multi-factor authentication, role-based access control, and context-aware authorization systems. Estonia’s X-Road AI implementation demonstrates the effectiveness of this approach, achieving zero security breaches while processing millions of transactions.

Second, comprehensive data security protocols that ensure protection at rest and in transit. This includes implementing advanced encryption standards (AES-256 for data at rest, TLS 1.3 for data in transit) and maintaining detailed audit trails of all system interactions.

Third, a robust monitoring and incident response system capable of detecting and addressing both traditional and LLM-specific security threats. The UK’s NHS AI Lab implementation provides a notable example of successful security monitoring in a sensitive data environment.

The implementation of Large Language Models (LLMs) in Korean public administration faces significant regulatory challenges due to the stringent requirements established by Korea’s Data 3 Laws: the Personal Information Protection Act (PIPA), the Information and Communications Network Act, and the Credit Information Act. Each of these legislative frameworks imposes specific obligations for data handling and protection, necessitating a comprehensive compliance approach for successful LLM deployment. Central to this approach is PIPA compliance, which demands a structured data processing system incorporating clear purpose specification, consent management, automated privacy impact assessments, and data minimization controls. This system must be augmented with privacy-preserving computation techniques that enable LLMs to process personal information while maintaining regulatory compliance [14-16].

To address the remaining regulatory requirements, a robust framework must incorporate specialized security architectures and financial data protection measures. The Information and Communications Network Act compliance is achieved through the implementation of network segregation, comprehensive access control systems, and sophisticated incident response capabilities, including real-time security monitoring and automated breach detection systems with established notification protocols. Additionally, Credit Information Act compliance demands specialized data protection measures for financial information processing, encompassing secure data storage systems, strict access restrictions, and comprehensive usage monitoring. This integrated framework supports precise data classification and maintains detailed audit trails of all financial data access, ensuring comprehensive compliance across all three regulatory domains while enabling effective LLM operation in public administration contexts.

Successful implementation of these solutions requires a carefully planned strategy. The research suggests a phased approach that begins with foundational infrastructure and gradually introduces more advanced features. This approach has proven successful in multiple international implementations, including Denmark’s MindLab and Australia’s Digital Transformation Agency projects.

The implementation strategy should include:

Continuous monitoring and improvement processes are essential for maintaining system effectiveness. Regular assessment of performance metrics, security incidents, and compliance violations enables ongoing optimization of the system.

While understanding these core challenges is essential, examining real-world implementations provides valuable insights into practical solutions and effective strategies. The following chapter analyzes both domestic and international cases of IPSA implementation, demonstrating how various organizations have successfully addressed these challenges while adhering to their specific regulatory requirements and operational needs.

The adoption of AI in the Korean public sector has led to significant advancements in administrative modernization, with various innovative projects demonstrating substantial potential for enhancing efficiency and service quality [17]. One notable example is the Seoul Metro’s implementation of an urban rail transportation safety service, powered by a Generative Pre-trained Transformer (GPT). This system integrates real-time data processing with LLM capabilities, enabling comprehensive safety analyses and timely decision-making, which has resulted in a 40% improvement in incident response times. Additionally, Hwaseong City’s complaint counseling assistant service has effectively merged traditional database management with natural language processing, significantly enhancing the efficiency of public institution responses. This hybrid system has led to a 60% increase in response efficiency and a 35% improvement in citizen satisfaction, showcasing the transformative impact of LLM integration in administrative tasks.

Other key implementations include the Public Procurement Service’s use of AI for automated draft generation in requests for proposals, reducing document preparation time by 70% while maintaining a 99.9% compliance accuracy. The National Tax Service has also advanced its HomeTax system through a sophisticated chatbot, leveraging HyperCLOVA X to handle over one million monthly inquiries with a 95% resolution rate. Meanwhile, the National Assembly Library has employed advanced natural language processing techniques in its customized news reporter service, which analyzes and summarizes over 10,000 documents daily with an impressive 98% accuracy in keyword extraction. These cases collectively underscore the capacity of AI-driven systems to revolutionize public administration by improving decision-making, streamlining processes, and ultimately enhancing citizen services in Korea.

International implementations provide valuable insights into successful IPSA deployment strategies [18], as detailed in Table 2.

The integration of Large Language Models (LLMs) and Intelligent Public Sector Automation (IPSA) technologies in public administration has been continuously evolving, significantly enhancing the functionality and efficiency of administrative systems. These advancements are addressing the complex requirements of public administration more effectively through various innovative approaches. Notable developments include the introduction of cognitive IPSA, which combines traditional IPSA with LLM capabilities such as natural language processing and machine learning. For instance, HM Revenue & Customs in the UK has successfully utilized this technology to handle complex tax inquiries and improve automated response capabilities. Another significant advancement is the development of LLM-based process mining technology, as demonstrated by the Netherlands’ Ministry of Infrastructure and Water Management, which optimized its grant application procedures and reduced processing time by 30%.

The enhanced functionality of LLM-based IPSA systems is evident in several areas. Multilingual capabilities have been improved, as seen in the French government’s administrative assistance system that now offers services in multiple languages. Advanced computer vision integration has boosted image processing capabilities, exemplified by Spanish traffic authorities increasing the speed of traffic violation image processing by 200%. LLM-based predictive analytics are enabling proactive public service delivery, as demonstrated by New Zealand’s Ministry of Social Development implementing a system for anticipatory provision of social services based on predictive analysis. These enhancements are complemented by efforts to increase scalability and adaptability, including cloud-based deployment strategies, modular architecture implementations, continuous learning systems, and the utilization of edge computing for time-sensitive tasks.

These technological advancements and application cases clearly demonstrate that the integration of LLMs and IPSA can significantly improve the efficiency and responsiveness of public administration. As these technologies continue to develop and become more widely applied, the digital transformation of public administration is expected to accelerate further. The combination of LLMs and IPSA goes beyond simple task automation, enabling the construction of intelligent and adaptive administrative systems. This evolution emphasizes the need for continuous adaptation of administrative systems in line with technological advancements, positioning LLM-IPSA integration as a key driver of future public administration. The ongoing development and broader application of these technologies promise to reshape government operations, enhancing service delivery and decision-making processes in unprecedented ways.

To maximize the benefits of AI and RPA in public administration, it’s crucial to develop a comprehensive strategy aligned with the organization’s overall goals. Such a strategy provides direction for technology adoption and enables a consistent approach across the organization.

Effective data governance is a cornerstone for the success of AI and RPA initiatives in the public sector, ensuring the quality, security, and ethical use of data while enhancing the reliability and effectiveness of these advanced systems. A crucial aspect of this governance is the implementation of robust data quality management processes, which guarantee the accuracy, completeness, and consistency of data used in AI-RPA systems. Denmark’s Basic Data Program serves as an exemplary model in this regard, establishing comprehensive data quality standards for all public sector databases. Equally important is the enhancement of data privacy and security measures to protect sensitive information processed by these systems. France’s Health Data Hub demonstrates the importance of this approach, implementing strict access controls and sophisticated anonymization techniques to safeguard citizen health data used in AI research.

Another critical component of effective data governance is the establishment of standards for data integration and interoperability. This is particularly crucial for facilitating seamless data sharing and integration across various government departments and systems. The European Union’s ISA² program is at the forefront of this effort, actively promoting data standardization and interoperability among member states. Furthermore, the development of ethical data usage guidelines is essential to address issues of bias and fairness in AI-RPA systems.

Canada’s Directive on Automated Decision-Making provides a prime example of this approach, offering comprehensive guidelines to ensure fairness and transparency in AI-based administrative decisions. By implementing these key elements of data governance, public sector organizations can harness the full potential of AI and RPA technologies while maintaining high standards of data integrity, security, and ethical use.

Fostering an organizational culture that embraces innovation and continuous learning is paramount for the successful adoption of AI-RPA technologies in the public sector. This cultural shift enhances an organization’s adaptability to technological changes and facilitates ongoing innovation. A key aspect of this transformation is investing in comprehensive skill development programs. Singapore’s Skills-Future for Digital Workplace program exemplifies this approach, offering AI and data literacy training to public sector employees, thereby equipping them with the necessary skills to work effectively with AI-RPA systems. Equally important is the implementation of robust change management initiatives. New Zealand’s Digital Public Service program demonstrates the effectiveness of this strategy, providing vital change management resources and support to agencies undergoing digital transformation. These initiatives are crucial in addressing resistance to new technology adoption and ensuring a smooth transition to AI-RPA-enhanced operations (Table 3).

To further cultivate an innovation-friendly culture, introducing incentives for innovation can be highly effective. The U.S. Federal Government’s President’s Management Agenda showcases this approach by awarding recognition for innovative use of technology in public service delivery, thereby encouraging employees to propose and implement novel AI-RPA solutions. Additionally, building knowledge sharing platforms plays a critical role in disseminating best practices and lessons learned across different departments and agencies. The European Commission’s AI Watch serves as an excellent example, providing a comprehensive know-ledge-sharing platform for AI initiatives across EU member states. By implementing these strategies - skill development, change management, innovation incentives, and knowledge sharing - public sector organizations can create a dynamic environment that not only facilitates the adoption of AI-RPA technologies but also fosters a culture of continuous improvement and innovation, ultimately leading to more efficient and effective public services.

Ensuring the ethical use of AI and Robotic Process Automation (RPA) is essential for fostering public trust and achieving the long-term success and sustainability of these technologies. Key strategies include promoting transparency in AI decision-making, such as by openly disclosing the processes behind AI systems that impact citizens. For example, Amsterdam’s Algorithm Register provides detailed information about the purpose and data of AI systems used by the city’s government. Additionally, maintaining al-gorithmic fairness is critical, involving the development and implementation of techniques to detect and mitigate bias in AI-RPA systems. The UK’s Centre for Data Ethics and Innovation, for instance, has developed tools for AI bias detection in public sector applications.

Moreover, maintaining appropriate human oversight is vital, ensuring that human intervention remains possible in crucial decisions made by AI-RPA systems. An example is Finland’s Immigration Service, where AI-assisted application processing always undergoes a human review stage before final decisions. Strengthening communication with the public is equally important, as it involves understanding and addressing public concerns and expectations regarding the government’s use of AI and RPA. Helsinki’s AI Register, which includes mechanisms for citizen feedback on the city’s AI systems, exemplifies this approach. By implementing these strategies, public sector organizations can harness the benefits of AI and RPA while mitigating potential risks, ensuring that their adoption aligns with ethical standards and maintains public trust.

The implementation of LLM-based IPSA systems in public administration has produced valuable insights through various international cases. These implementations demonstrate both the potential and challenges of integrating advanced AI systems with traditional administrative processes. A comprehensive analysis of these cases reveals critical success factors and implementation strategies.

The Singapore GovTech SQUAD implementation de-monstrates the effectiveness of a hybrid architecture that combines traditional database reliability with LLM capabilities. Their system employs a sophisticated real-time validation pipeline that ensures data accuracy while maintaining processing efficiency. The implementation of zero-trust architecture and end-to-end encryption has resulted in zero security breaches while processing millions of transactions.

Estonia’s X-Road AI system showcases the potential of distributed processing and blockchain verification in maintaining data integrity [19]. Their implementation of federated learning allows for secure data processing across multiple government agencies while maintaining strict privacy controls. The system’s quantum-safe encryption and behavioral analysis capabilities provide robust security while enabling efficient cross-border services.

The analysis of successful implementations has led to the development of a comprehensive strategic framework for IPSA deployment, as outlined in Table 4.

The successful implementation of IPSA systems requires careful attention to several critical factors identified through case analysis. Organizations must first establish robust data governance frameworks that ensure accuracy and compliance while enabling efficient processing. This includes implementing sophisticated validation mechanisms and maintaining comprehensive audit trails.

Security implementations must address both traditional and AI-specific threats. This requires the development of multi-layer security architectures that protect against evolving threat vectors while maintaining system accessibility. Successful implementations have demonstrated the importance of continuous security monitoring and rapid response capabilities.

Legal compliance frameworks must be embedded throughout the system architecture. This includes implementing automated compliance checking mechanisms and maintaining comprehensive documentation of all processing activities. Regular compliance audits and updates ensure continued alignment with evolving regulatory requirements.

Analysis of current implementations reveals several emerging trends in IPSA development. Advanced natural language processing capabilities are enabling more sophisticated citizen interactions, while improved machine learning models are enhancing decision support capabilities. Future implementations are likely to incorporate quantum-resistant security measures and advanced privacy-preserving computation techniques.

These implementation cases highlight the critical role of regulatory frameworks and policy guidelines in successful IPSA deployment. Building on these practical insights, the following chapter examines the evolving regulatory landscape for AI in public administration, with particular focus on harmonizing international standards with Korean regulatory requirements, while maintaining focus on service delivery and citizen satisfaction.

The White House Office of Science and Technology Policy’s Blueprint for an AI Bill of Rights [20], released in 2022, establishes foundational principles for AI system implementation through five critical protections. These protections encompass Safe and Effective Systems requirements for comprehensive testing and monitoring, Algorithmic Discrimination Protection to ensure equitable service delivery, Data Privacy requirements establishing strict controls over data practices, Notice and Explanation provisions mandating transparency in AI decision-making, and Human Alternatives and Fallback requirements ensuring service accessibility. This comprehensive framework particularly emphasizes the importance of continuous evaluation and human oversight in maintaining public trust in automated administrative systems.

The National Institute of Standards and Technology’s AI Risk Management Framework (AI RMF 1.0) complements these principles by providing detailed guidelines for managing AI system risks throughout their lifecycle [21]. The framework introduces four essential functions: Govern, which establishes organizational structures and accountability mechanisms; Map, which identifies context-specific risks and opportunities; Measure, which provides metrics for system evaluation; and Manage, which outlines processes for continuous improvement. These components create a systematic approach to risk management that enables organizations to effectively implement and maintain AI systems while ensuring compliance with regulatory requirements.

Executive Order 14110 further strengthens the federal approach to AI implementation by establishing specific requirements for public sector adoption [22]. The order mandates the creation of AI working groups within agencies, development of public-private engagement mechanisms, and implementation of comprehensive documentation and reporting requirements. These measures create a structured framework that emphasizes transparency, accountability, and collaborative development of best practices in public sector AI deployment.

The Office of Management and Budget’s Memorandum M-21-06 provides additional guidance for federal agencies on the regulation and implementation of AI technologies [23]. This memorandum establishes detailed guidelines for the design, development, acquisition, and use of AI systems, focusing on promoting innovation while maintaining public trust and effectively managing potential risks. The guidance serves as a crucial complement to existing frameworks, providing practical direction for federal agencies in their AI implementation efforts while ensuring alignment with broader governmental objectives and regulatory requirements.

In examining global AI governance standardization trends, the EU’s AI Act serves as a comprehensive regulatory framework that significantly influences international standards, while the OECD AI Principles and ISO/IEC standards provide the technical foundation for responsible AI development. Notably, the EU AI Act’s risk-based approach serves as a crucial reference point for developing AI regulatory implementation strategies in Korea, and these international standardization efforts are establishing the groundwork for transnational AI governance frameworks.

The effective implementation of IPSA requires essential harmonization between international standards and Korean regulatory requirements. This necessitates ensuring compatibility between Korea’s Data 3 Laws and international frameworks such as GDPR in terms of data protection measures, while developing interoperable technical standards that facilitate international collaboration while maintaining compliance with domestic regulations. Furthermore, establishing cross-border cooperation mechanisms is crucial for addressing common challenges in public sector AI implementation.

Based on the analysis of international frameworks, a comprehensive implementation approach tailored to Korean public administration must be proposed. Specifically, this requires developing Korea-specific guidelines that harmonize international best practices with domestic regulatory requirements, particularly the Data 3 Laws, and establishing clear governance structures that incorporate both international standards and local requirements. Additionally, it is essential to implement monitoring and evaluation systems that ensure continuous compliance with evolving regulatory requirements.

As AI and RPA technologies become more prevalent in public administration, the need for regulatory frameworks to govern their use is increasing. Appropriate regulation is crucial to ensure responsible use of these technologies and minimize potential risks.

The development of AI-specific legislation is crucial to address the unique challenges posed by AI in public administration. The European Union’s proposed AI Act, which includes specific provisions for AI use in public sector applications, serves as a notable example. Such legislation provides clear guidelines for the development, deployment, and use of AI systems, promoting responsible AI use.

Implementing algorithmic accountability regulations is essential to ensure transparency and accountability in the use of algorithms in public sector decision-making. The U.S. Algorithmic Accountability Act of 2022, which proposes mandatory impact assessments for AI systems used by federal agencies, exemplifies this approach. These regulations can help ensure the fairness, transparency, and accountability of algorithms.

Updating data protection regulations is necessary to address the specific challenges posed by AI and RPA systems in data processing. The California Consumer Privacy Act (CCPA), which includes provisions on automated decision-making and profiling, serves as a good example. Such regulations strengthen privacy protection and safeguard the rights of data subjects.

Developing ethical guidelines is crucial for establishing ethical standards for the development and deployment of AI and RPA in public administration. The OECD AI Principles provide a framework for responsible AI use that has been adopted by many countries [24]. These guidelines ensure that AI and RPA systems are developed and used in accordance with ethical principles.

The introduction of Large Language Models (LLMs) and Intelligent Process Automation (IPSA) will have a profound impact on the public sector workforce, making it essential to implement policies that support workforce transformation. These policies are crucial for helping employees adapt to technological advancements and acquire new competencies. One key approach is the implementation of reskilling and upskilling programs, which are vital in preparing public sector employees for roles that complement LLM-IPSA systems. Singapore’s SkillsFuture for Digital Workplace program, which offers training in LLMs and data analytics to civil servants, exemplifies this strategy by equipping employees with the necessary skills for thriving in a technologically advanced work environment.

Additionally, it is important to support the creation of new roles that emerge from LLM and IPSA adoption, such as AI ethicists and IPSA managers. The UK government’s establishment of new digital, data, and technology roles across public services is a step towards shaping the workforce to fit the evolving technological landscape. Furthermore, the establishment of lifelong learning initiatives is essential for ensuring that public sector employees continuously adapt to the rapidly changing environment. The European Commission’s Digital Education Action Plan, which promotes digital skills and competencies, is an example of such initiatives. Finally, providing change management support is critical for helping organizations and individuals transition smoothly to the new work environment shaped by LLM and IPSA technologies. The Australian Public Service Commission’s provision of change management tools and resources for agencies undergoing digital transformation highlights the importance of this support in facilitating a successful transition.

This study has identified several critical areas that warrant further investigation to better understand and optimize the integration of Large Language Models (LLMs) and Intelligent Process Automation (IPSA) in public administration. First, long-term impact assessment is essential, involving longitudinal studies that evaluate the enduring effects of LLM-IPSA integration on public administration efficiency, service quality, and citizen satisfaction. These studies will provide valuable insights into how these technologies influence public sector performance over time. Additionally, there is a need for in-depth research on the ethical implications of AI decision-making in public administration, especially in sensitive areas such as social welfare allocation and law enforcement, where the potential for ethical dilemmas is significant [25-27].

Further investigation should also include cross-cultural comparisons to analyze the implementation of LLM-IPSA across various cultural and governmental contexts. Such comparative studies can identify best practices and potential challenges, helping to tailor strategies to specific environments. Moreover, research on human-AI collaboration models is crucial for determining optimal approaches to task allocation and decision-making processes between human civil servants and AI systems. Understanding these dynamics will be key to maximizing the benefits of AI integration. Additionally, studies focusing on citizen perception and trust in AI-driven public services are necessary to develop strategies that enhance public engagement and trust in these technologies. The development and evaluation of AI governance frameworks specific to public sector applications will also be essential to ensure responsible and effective AI use. Finally, a skills gap analysis is needed to understand the evolving skill requirements in public administration due to AI integration, along with an evaluation of effective reskilling strategies to prepare the workforce for the future (Table 5).

The integration of Large Language Models (LLMs) and Intelligent Public Sector Automation (IPSA) represents a significant paradigm shift in public administration, offering remarkable opportunities to enhance efficiency, decision-making, and service delivery. However, as highlighted in this study, realizing these benefits requires a holistic approach that carefully considers technological, organizational, ethical, and policy dimensions. The successful implementation of LLM-IPSA in public administration depends on several critical factors: a clear strategic vision that aligns with broader governmental digital transformation goals; robust data governance frameworks that ensure data quality, security, and ethical use; comprehensive workforce development initiatives that equip public servants with the necessary digital skills; ethical guidelines and transparency measures that maintain public trust; and adaptive regulatory frameworks that balance innovation with responsible AI use.

As governments worldwide explore and implement these technologies, it is essential to strike a balance between fostering innovation and ensuring responsible use. Achieving this balance requires ongoing dialogue among policymakers, technologists, public administrators, and citizens, ensuring that AI-driven public administration ultimately serves the best interests of society. The journey toward fully integrated, AI-enhanced public administration is just beginning. By addressing the challenges and opportunities identified in this study, governments can lay the groundwork for a more efficient, responsive, and citizen-centric public sector. Moving forward, continuous research, evaluation, and adaptation will be crucial to harnessing the full potential of LLMs and IPSA for the public good.