Evaluating the Impact of Artificial Intelligence on Public Service Delivery Efficiency in the United States

Evaluating the Impact of Artificial Intelligence on Public Service Delivery Efficiency in the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the Impact of Artificial Intelligence on Public Service Delivery Efficiency in the United States Md Mainul ISLAM, Md Shahadat Hossain, Md Shahadat Hossain, Adib Hossain, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8142308/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There was a significant lack of empirical data on the measure of the effect that Artificial Intelligence (AI) has on the efficiency of operations in the U.S. municipal governments. Robust research providing evidence on pre and post-implementation measures were non-existent despite the high level of acknowledgement of the theoretical potential. The present study had the positive influence of AI integration on the indicators of public service delivery as an object of investigation. The main research question was to assess any efficiency improvement in its main key operation dimensions before and after adoption of AI. A cross-sectional quantitative methodology was used accompanied by a before and after comparative design. Results were drawn through surveys of 152 Chief Information Officers of municipalities with a city size of greater than 100,000. Descriptive statistics and t-tests were performed on seventeen efficiency indicators-response time, cost, citizen satisfaction and transparency. The outcome showed statistically significant results on all of the dimensions (p < 0.001). Mean efficiency in service delivery shifted up by 43 percent, 3.12 to 4.46. The process automation reflected a maximum mean preference (+ 1.44), second is response time (+ 1.32) and environmental impact (+ 1.30). The cost of providing a service per transaction was reduced severely (mean improvement of + 1.21 on an efficiency scale) as were the satisfaction levels of citizens with a mean increase of 1.27 points. The results are strong empirical evidence that AI implementation can be a lever of introducing significant efficiency improvement, which municipal leaders and policymakers should consider when evaluating the possibility of using technology to make the most out of the available resource and achieve significant improvements in terms of outcomes for the population. Artificial Intelligence Public Service Delivery Municipal Efficiency Quantitative Analysis Governance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION Artificial intelligence (AI) can be defined as the process of incorporating cutting-edge technology into different areas and turning into a significant component of technological changes (Varriale et al., 2025 ). The most significant role of AI in the field of public service delivery would be to ensure that there is increased effectiveness and efficiency in service delivery, which would lead to good governance and enhancement of citizen satisfaction levels. According to the manuals of the International Monetary Fund (JM), governance refers to all dimension of managing a country including the economic policies, regulatory regime as well as the readiness to pursue the rule of law. Positive governance is associated with induction of fairness, emancipation, job creation, visibility, and good distribution of services to all the people (Ramanujam & Farrington, 2022 ; Kreinin et al., 2024 ). In a traditional way of governance, bureaucracy and inefficiency of resources usually inhibit its performance, leading to inferior delivery of services (Zou, 2024 ). To maximize the effective use of resources and reduce corruption associated with bureaucracies, efficient governance is needed so that it provides timely and accurate service delivery to various citizens according to what they need. AI has come with some great chances of transforming governance activities and increasing efficiencies in the delivery of services to the people. With the massive works done in data analysis, pattern recognition, and automation, AI has great potential to provide resolutions to the mired governance systems. Governments that use AI technologies allow them to optimize processes, better decisions, and deliver services to citizens. In the United States, AI in the field of governance and the provision of services has been singled out as a method of introducing a new epoch in citizen-friendly administration (Ali, 2023 ). The U.S being one of the top economies in the world, it has much to be concerned about in the AI revolution. The U.S. government initiated the National AI Initiative Act aligning on a path of developing AI uses across all sectors which accentuates the need of implementing policies and frameworks that govern proper and ethical AI usage in the government. The United States, China, France, and Japan have experienced substantial growth in their investments towards AI in the form of AI-related research and development initiatives, investments in AI start-ups, infrastructural investments towards AI and AI led public procurement processes (Salas-Pilco, 2021 ). The government of the United States has also outlined a strategic plan to establish the AI centres of excellence in the leading universities and research institutions, which has been a big step to achieving its vision to have an AI for America (Abulibdeh et al., 2025 ). The capacity of AI in the governance of the U.S. in the globalized environment offers an opportunity where the country can become more competitive in the global scope. Its adoption of AI-based innovations in providing public services will influence how the other countries are dealing with such issues (Marzdar, 2025 ). Minimal Government, Maximum Governance is the vision of the U.S government to establish a government where government administration and governance activities are to operate smoothly without much involvement and obstacles (Khatib et al., 2021 ). In this way, combining AI in governance and implementing its principles in the public policy has significant potential to enhance interaction with citizens, accountability, and the efficiency of service delivery, as well as to streamline the work of the administration (Pananrangi et al., 2024 ). This paper focused on enhancing the quality of this research by putting to test the influence of AI in ways adopted in the United States of America in the delivery of public services. This research analyzed first the level of maturity of AI uses in U.S. municipal corporations, considering such aspects as awareness, ongoing projects of AI, project schedule, AI project resource strength, and availability of technological infrastructure (Anshari et al., 2025 ). Besides, the research aimed to understand the implication of AI integration with different operation functions in municipal corporations and how it can be involved in efficiency improvement. Finally, this study evaluated how these municipal corporations will be affected by the AI implementation in various aspects of service-delivery effectiveness. 1.1. Research questions RQ1: How mature is the AI adoption in municipal corporations in the U.S? RQ2: What are the impacts of the introduction of AI in public service industry in regard to various operational functions in the municipal corporate bodies and what roles is AI playing in improving efficiency? RQ3: What are the impacts of the application of AI on efficiencies of service delivery dimensions in the municipal corporations? 1.2. Objective The main objective of this study was to gauge the levels of maturity of artificial intelligence (AI) in municipal corporations in the United States. In particular, the study sought to establish the degree of establishment of awareness, existing projects, technological base and capacity of resources in these institutions. The second goal was to examine how the integration of AI affected the functioning of the municipal corporations in terms of enhancing the efficiency of decision-making, resource management, and the administrative process in general. The third goal was to determine the impact of adoption of AI on various aspects of effectiveness in service delivery such as the response time, accuracy, satisfaction of citizens, transparency, and accountability. In order to fulfill those purposes, the research has adopted a quantitative and cross-sectional design based on a before and after survey of the Chief Information Officers (CIOs) of the municipalities in the U.S. and the results were analyzed by means of the use of descriptive statistics and paired-sample t-tests to determine the significance of gains ascribed to the implementation of AI. 1.3. Research Hypotheses The hypotheses of this study were made as follows according to the objectives of the study: H 1 Above the level of adoption of artificial intelligence (AI) in municipal corporations of the United States significantly changed before and after implementation, meaning that the indicators of maturity, in the form of awareness, projects, and technological infrastructures, can be measured. H 2: The adoption of AI in municipal corporations led to a great enhancement of the operational activities such as decision-making, use of resources as well as efficiency in administration. H3: AI played a significant role in streamlining service delivery in municipal corporations as demonstrated through an increase in response time, accuracy, transparency, citizen satisfaction and accountability. This conceptual framework shows (Fig. 1 ) how AI integration into the governance process (awareness, technological infrastructure, and AI project resources) relates to its implementation in terms of municipal corporation and the adopted improvement of service delivery dimensions. The framework underlines how the implementation of AI will improve the efficiency of operations by increasing the speed of response, accuracy of services, transparency, accountability, and overall satisfaction of citizens as indicated in the research hypotheses of the study. 2. LITERATURE REVIEW 2.1 Artificial intelligence Artificial Intelligence (AI) has become a disruptive tool, transforming a great variety of industries, such as finance, healthcare, education and transportation (Akinsola et al., 2022 ). Artificial intelligence works like its human counterpart, it works in the form of advanced computational models and algorithms that emulate human thought, in terms of learning, reasoning, and problem-solving (Konar, 2018 ). Interdisciplinarity in AI is multifaceted and comprises a number of subparts such as machine learning, natural language processing, computer vision, and robotics, each of which helps to fast-track development and wide integration into AI systems (Fergus & Chalmers, 2022 ). It is important to understand the landscape of the current research and development of AI to harness that potential and respond to the ethical, societal and technical challenges to which it is giving rise. Recent years have seen the introduction of the concepts of deep learning as a branch of machine learning, which is inspired by the structure and functioning of the human brain (Sejnowski, 2018 ; Vishnoi et al., 2024 ). Such techniques as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) form deep learning models that have changed the way humans do image and speech recognition, natural language processing, etc. (Yadav et al., 2022 ). The availability of large datasets and the availability of powerful computing resources have motivated the emergence of AI models that perform brilliantly in the real world settings (Jia et al., 2023). A lot of available literature is dedicated to more traditional methods of AI, such as machine learning and deep learning, it is critical to note that generative AI is a new paradigm in the field. Generative AI has become a change in the field of machine learning where systems can generate new content including text, images and sounds (Foster, 2022 ). Generative models (such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)) are based on learning underlying probability distributions to synthesize new data and have newer machine learning methodologies at their heart, rather than the pattern recognition and categorization forms of traditional AI (Akkem et al., 2024 ; Innocent, 2024 ). Generative AI has led to great strides in the area of natural language processing, image creation and generating creative features. As an example, it is possible to mention generative models, such as StyleGAN (Melnik et al., 2024) that are capable of creating high-quality and realistic looking images, or language models, such as GPT by OpenAI (Hadi et al., 2023; Bhattacharya et al., 2024 ) that show compelling results by making human-like text of the same quality, applicable to a variety of fields. In spite of all the positive applications, AI is characterized by some major drawbacks that stall its active adoption and implementation. Among the most vigorous concerns, the problem of the so-called black box problem of AI as insufficient transparency and interpretability of the operations of decision-making in AI should be mentioned (Nešpor, 2024 ). With greater complexity comes an even more complicated picture of how AI systems work and how they make decisions, thus creating ethical and regulatory issues, particularly in sensitive contexts such as healthcare and even criminal justice (Singh, 2024 ). Along with that, the issues regarding privacy, preferences, and fairness ought to be resolved to create the accountable and fair AI systems (Alam, 2023 ). Further development of AI studies, it will be crucial to focus on the challenges associated with interpretability, fairness, and accountability to make a responsible introduction of AI to the society (Cheng et al., 2021 ). Trans-disciplinary engagements and morality will be key towards future direction of AI, ensuring the development of human centered AI and not an alternative to human performance or abilities (Cowin et al., 2023 ). Once these issues are resolved and followed by ethical standards, AI could create revolutionary change and bring a more inclusive and sustainable society. 2.2. AI and the Government AI does not have a standard definition (Dissanayake & Dissanayake, 2021 ) define it as systems that are able to do tasks that a human would usually perform with intelligence or that a human would make rational decisions via logical thinking that is performed by some system. Anshari et al., ( 2025 ) define AI as a set of digital technologies that, in addition to improving the efficacy and effectiveness of the delivery of public services, rebuild the mechanism of offering and providing public services fundamentally and are going to have a long-lasting influence on the organization of the services in the public sphere (Morgan et al., 2025; Momen & Ferdous, 2023 ). According to (Siemens et al., 2022 ), AI is a matter of digitally overlapping technologies that make machines capable of learning on their own and resolving cognitive problems without human interventions. According to Tripathi et al., ( 2021 ) AI entails a combination of computer science and high-quality datasets that result in novel approaches to problem-solving. Machine learning and deep learning are the most essential subfields in this sphere that help in creating expert systems that can make extrapolations or classify data, depending on what is input. Public organization integration and adoption of AI technologies are quickly growing. The latest papers have emphasized the disruptive nature of AI in the governmentze domain including service delivery and policy making (Alshahrani et al., 2024 ; Leitner & Stiefmueller, 2019 ). This encompasses not only observing the impacts of AI on personnel in the public administrations (Young et al., 2019 ; Nzobonimpa, 2023 ), but also on how citizen interaction with the governmental bodies may be affected (Grimmelikhuijsen et al., 2017 ). During the last ten years, e-governance efforts have been targeted on successful efficiency and cost-reduction. In the recent past, the technological world has made major steps towards improvement of the administration of the people. The fields of healthcare, education, security, and defense are some of the areas where AI is being used in public sectors (Achanta, 2025 ; Sun & Medaglia, 2019). Applications of AI to service delivery in the public sector are also considered as the following stage of the transformation of the Information and Communication Technologies (ICTs) in the social context of social media, robotics, and big data waves (Aithal & Aithal, 2020 ). Mikhaylov et al. ( 2018 ) highlight that AI and algorithms can revolutionize some vital functions of pubic sector organizations and individuals, who are part of such organizations. Another valuable point related to the contribution of the emerging technologies to the enhancement of e-government services in discrete countries is presented by Jain et al., (2019). A trend in the development of a technology-related style of governmental service delivery is a move to increase the efficiency in service maintenance and delivery with the quality aspect remaining intact on the technological surface (Kachhadiya, 2024 ). Various functional areas covered under this transformation of the kind of public service delivery that is also made possible by AI include human resource management, strategic management, performance evaluation, and institutional communication (Chilunjika et al., 2022 ). The area of education, healthcare, tax management, social benefits, border management, and emergency management are also areas where AI is used (Abid et al., 2021 ). The ability to aggregate and analyze large amounts of data, and using open data, is an essential factor contributing to the AI interface between government services (Androutsopoulou et al., 2024 ). The application of AI to the delivery of public services provides government agencies with an opportunity to make better decisions, to communicate with citizens in a more efficient manner, to personalize the services, and to decrease the administrative burden (Milakovich, 2021 ; Latupeirissa et al., 2024 ). This results in an increase in the quality of service and an increase in the value of the public (Herdiansyah, 2023 ). In such spheres as process automation, knowledge management, predictive analytics of resources, resources allocation, conversational agents, fraud detection, and provision of expert support, AI technologies are in use (Bello & Olufemi, 2024 ). The public services use of AI has triggered both euphoria and fear as many governments all around the world are embracing AI in an effort to streamline their decisions and provide more streamlined services. Government accountability and service quality are also being increased by AI systems as through AI-driven bots in Brazil, fraud and corruption can be identified (Johnson, 2025 ). Moreover, the deployment of the models empowered with AI makes communication between the individuals and state organizations better, downgrading the service delivery (Poudel, 2024 ). Generative AI has become a game changer in the service delivery sector. The examples of technologies that are very successful in solving image synthesis, natural language processing, and content generation include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) (Koç et al., 2025 ; Frolov et al., 2021 ). Such models allow replacing document creation and personalizing communications; they allow automating and analyzing huge amounts of data used in decision-making in public administrations. These cost savings, enhanced service quality, enhanced citizen satisfaction, arise after the implementation of generative AI in public services (Nica et al., 2025 ). With the help of the generative models, governments will unlock new possibilities of innovation and will be able to establish more responsive public institutions. Studies have identified the many uses of AI in the governance of the people such as efficiency, fewer risks and better citizens participation (Duberry, 2022 ). According to Safitra et al. ( 2024 ), there are ten areas where AI can be useful to the government and can be used in knowledge management, high-order data analytics, and so on. When dealing with the effective deployment of AI, the process involves new knowledge acquisition and skills, which have the potential to cause transformational changes in organizational practices (Zong & Guan, 2025 ). A planned implementation, acceptance, and seamless development of AI into a staple will ensure a successful integration of AI (Rane et al., 2024 ; Sun & Medaglia, 2019). The results of AI on the workforce in the government and citizen-state relations have also been a focus of the scholars (König, 2025 ; Ulriksen & Plagerson, 2023 ). Like earlier e-government efforts, AI-enabled technological advances are supposed to increase efficiency and minimize expenses in the sphere of administration (Ajayi et al., 2024 ). This type of smart governance aims to ensure the high efficiency of service provision and increased participation of citizens through online spaces (Gil et al., 2022 ; Pereira, 2018). The role of ethics in the sphere of AI in the public administration is gaining growing significance. A rising level of concern over the proper use and adoption of artificial intelligence, governments and tech businesses have delivered principles of responsible AI implementation (Laat, 2021 ; Yigitcanlar et al., 2024 ). These guidelines provide broad guidelines to the use of AI in the public domain. There is still limited research on the place of governments as the users and regulators of the AI despite its increasing presence and significance (Zaidan & Ibrahim, 2024 ). The growth in AI application, it is important to know how governments can regulate and use AI technologies in a dual way. The AI technologies are also being used in the municipality corporations to enhance service delivery and the efficiency of operations in the locations. As an example, chatbots armed with AI afford 24-hour citizen service by answering questions, facilitating requests, and availing information (Rainie & Anderson, 2021). Predictive analytics based on AI are used to optimize resource allocation and decision-making in such sectors as infrastructure maintenance, waste management, urban planning, etc. (Ojadi et al., 2025 ). Smart sensors and the Internet of Things devices in urban infrastructures provide a population with an opportunity to monitor parameters of urban life, including traffic flow, air quality, and energy, in real-time, which will assist cities in the creation of evidence-based policies (Bibri, 2023 ). Moreover, AI drones are also considered to perform aerial surveillance, respond to a disaster, and inspect the infrastructure (Hildmann & Kovacs, 2019 ). By using such AI applications, municipal corporations seek to maximize the use of resources and improve service provision and urban management. The pertinent literature reveals that the integration of AI in the field of public services has a transformative character but emphasizes its challenges, as well. According to Ejjami,(2024), the integration of AI enhances citizen unitary services and the automation of the usual municipal tasks in the administration. According to Selvarajan, ( 2021 ), there is an improved decision-making as a result of analytical capabilities of AI. The research by Skotnicka et al., (2025) was one of the first to give baseline knowledge about how AI was used in the sphere of public administration. (Emma, 2024 ) remind that transparency, and accountability are crucial to prevent bias in an algorithm. The factors of moral responsibility, including fairness and equity, impartiality, discrimination, and equity are covered by (Zhang et al., 2024) who warn that AI can interfere with people-oriented elements of service provision. Fares, ( 2020 ) affirm that oversight structures should guide the fairness and non-discrimination. 2.3. Problems, Challenges, and Advantages Although more people are talking about it, the implementation of AI within the realm of a public sector is not very high, and especially compared to those companies in the private sector (Mikhaylov et al., 2018 ). Some of the threats to the integration of AI involve insecurity of data or ethical or privacy concerns (Li & Zhang et al., 2017). The fact that AI is capable of doing what a number of employees used to do increases fears of displacement of jobs among the workers in the public service sector (Hong, 2024 ). Further, effective regulatory control and modern regulations of the field are also essential aspects of the AI application to the public sector (Almeida et al., 2021 ). Some barriers to the integration of AI that have been identified by Kiilu et al. (2025) embrace the quality of the system, financial viability, the social legitimacy of AI, trust, privacy, safety, accountability, and ethical concerns. Furthermore, there is a possibility that AI would increase inequalities and undermine trust because of the risks associated with the violation of rights, digital divide, biased policing, and rising inequality in economic terms (Lainjo, 2020 ; Dinker, 2024 ). Even though the application of AI has tremendous potential in improving the services provided to the population, it is essential to discuss these issues to guarantee fair and ethical implementations. The scholars also highlighted the potential challenges of AI adoption in the public sector, such as a shortage of technical cadres, abuse of AI, privacy, transparency, and the ethical issue of the use of AI and when to use it (Datta, 2024 ). The effect of AI on executive discretion and transparency in the context of public organizations has yet to be researched in earnest. According to Boer and Cetina et al., (2024), the worldwide IT adoption transforms the decision-making in both the government and other public organizations with automation undermining administrative discretion. According to Zerilli et al. ( 2019 ), the issues around the lack of transparency of algorithmic decision-making and the decrease of discretion of human decision-makers were mentioned. 2.4. Research gap Research on AI in the public sector has expanded rapidly, but empirical studies in the public sector are limited. A small number of scholars have examined the role of AI in executive discretion and transparency (Bullock et al., 2020 ; Young et al., 2019 ; Meijer et al., 2020 ). Other studies have culminated in the views of Chief Information Officers (CIOs) regarding AI integration, how public value can be created with AI (Walsh et al., 2021 ), how to apply AI in predictive policing (Meijer et al., 2020 ) and pandemic response. Few studies address the question of factors determining the success of AI implementation in the sphere of public sector (Mikhaylov et al., 2018 ; Campion et al., 2022 ; Tangi et al., 2023; Sun & Medaglia, 2019). When developing the technology of an artificial intelligence, this effort proves to be complicated and manifold with a lot of advantages and uses. Hence, considerable opportunities can be found in investigation of the patterns of AI adoption in the sphere of public service provision. The empirical research is needed in order to connect the theory and application. The aim of this study will be to determine how effective AI has been in its delivery of information during the process of public service provision and compare the data both pre and post the pandemic. Through this comparison, the paper will attempt to give details on how AI can assist in improving service provision in the government. 3. RESEARCH METHODS 3.1. Research Design The research is a quantitative study that follows the original research on the integration of AI into governance (Birkstedt et al., 2023 ) and has a cross-sectional research design to determine the contribution of AI to the efficiency of public service delivery. The study design governs the patterns of Criado et al. ( 2021 ) about governance and technology. The main respondents to the study will be Chief Information Officers (CIOs) in governmental institutions. The effectiveness of AI application in enhancing the efficiency of public services is studied through the use of a comparative analysis strategy emerging on the basis of Criado et al. (2022) and Mishra et al., ( 2024 ). 3.2. Data collection and Sample This paper was based on the analysis of the information technology used by CIOs in big city councils in the United States, specifically, in the local governments that have a population above 100,000 citizens. They are 252 municipalities in one of these categories, all of them identified by the corresponding national statistical sources in 2024. The urban aspect of the USA provides an ideal environment to examine AI application in the field of delivering modern public service. Digital transformation experienced by the U.S. government, such as Smart Cities, also increases the prospects of the AI-driven innovations. Even though the study is a U.S.-related one, the results can be generalized to other contexts that may be similar to this one around the world (Bojić, 2022 ). Since the application of AI is still in its initial phases in the American city councils, the city councils with greater populations have a higher chance to implement these technologies. Even though the adoption of AI can be described as rather new, the cities can be discussed as the early adopters of innovations due to their active interactions with the sphere of technology (Wang et al., 2021 ). The study employed a before and after implementation method to examine the effect of AI on the delivery of services to the people. Researchers gave a survey to CIOs prior and after adopting AI technologies in their departments. The two-part survey plan made in accordance with the principle of best practice in organizational research (Schäper et al., 2025 ) was aimed to capture the perceptions, expectations, and experiences on two occasions, at pre-implementation, and according to post-implementation results of AI. In covering several domains of efficiency in the delivery of the public services, the analysis aimed at unraveling improvements, shifts, and challenges introduced by the AI technology. The answers that were gathered prior to the implementation of AI acted as a basis of comparison, as it was suggested by Abdurahman & Kabanda ( 2024 ) that the baseline data are important to estimate the transformative impacts. The survey was emailed amongst CIOs dealing with Information and Communication Technologies (ICTs) in each of the city councils. Online search of the respective council websites was used to get email addresses, with telephone confirmations being done of the businesses. The survey was thus emailed, with a comprehensive letter of the researchers explaining the purpose of the study. Researcher contact information was also available in situations where one requires clarifications or questions. The survey was performed between Nov 25, 2024, and Feb 25, 2025, and it received a good response rate as 152 CIOs took part in the survey, compared to the usual 50 percent of the response rate benchmark in organization studies (Fosnacht et al., 2017 ). The validity and soundness of findings were warranted by this high response rate. There were no observed biases in the answers according to city council types, sizes, and locations, which proves the soundness of the sample. The statistical formula was used to calculate the sample size to make the study more methodologically rigorous. Table 1 Short Overview of the Research Methodology Component Description Research Design Quantitative, cross-sectional; before–after comparative analysis of AI adoption (Birkstedt et al., 2023 ; Criado et al., 2021 ). Population & Context 252 U.S. municipalities with populations > 100,000; urban councils as early AI adopters (Bojić, 2022 ; Wang et al., 2021 ). Respondents Chief Information Officers (CIOs) from city councils responsible for ICT functions. Sample & Response Rate 152 valid responses; ~60% response rate, exceeding organizational research benchmarks (Fosnacht et al., 2017 ). Data Collection Online survey distributed via verified email (Nov 25, 2024 – Feb 25, 2025); two-part survey (pre- and post-AI implementation). Measured Dimensions Efficiency indicators: response time, accuracy, cost per transaction, citizen satisfaction, accessibility, automation, analytics-based decision-making, transparency, adaptability, feedback, employee productivity, environmental impact, compliance, trust. Validity & Reliability Baseline–comparison design ensured methodological rigor (Abdurahman & Kabanda, 2024 ; Schäper et al., 2025 ). Analysis Approach Descriptive statistics, comparative (pre vs. post) analysis, and paired t-tests for efficiency outcomes. This table helps to get the general idea of the methodological framework of study that includes design, population, sampling, data collection, indicators that were measured, and the strategy of analysis aimed at estimating the influence of AI adoption on the ability to provide public services by municipalities in the U.S. (Table 1 ). The measurement process of the effectiveness of Public Service Delivery A clearly designed framework that measures the efficiency of the entailed services provided to the public was established on different important factors. These elements comprise Response Time, which becomes very essential in delivering timely services that increase the satisfaction of the citizens (Teshome et al., 2020 ). The concept of Accuracy and Precision should support building trust and confidence among the masses since there is a necessity to be reliable in service delivery performance (Kovari, 2024 ). Resource Utilization is concerned with the best assignment of resources, and the aspect of proper resource management in publicly owned institutions deserves note (Lu et al., 2023 ). Cost per Transaction determines the economic performance of service delivery, which is vital to sustainable administration of the public (Sadik et al., 2024). Citizen Satisfaction measures how the people feel regarding the quality and promptness of services, which signifies the input of feedbacks in delivery (Lamsal & Gupta, 2022 ). Accessibility guarantees that the services are accessible to all communities, which emphasizes the significance of fair delivery of services (Wang, 2023). Process Automation focuses on reviewing how technology can be involved in simplifying the work, leading to better efficiency. Analytics-based Decision-making is based on paying more attention to the role of analytics in making decisions, which is the increasing trend of adopting evidence-based governance (Korherr et al., 2023 ). Punctuality means that the services are provided on time, which is yet another reason why this concept of timeliness is critical (Palmqvist & Kristoffersson et al., 2022). Transparency and Accountability measure the organizational processes, where transparency plays a central role in the establishment of trust by the population (Meijer et al., 2020 ). Adaptability to Changing Needs measures the responsiveness of the public services to the changing needs of the communities as governance is dynamic. Feedback Mechanisms quantify the level of citizen input effectiveness to enhance contiguous enhancement. Employee Satisfaction and Productivity looks at the correlation between the welfare of the customer and the performance of the entire service (Son et al., 2021 ). Environmental impact assesses the sustainability of service delivery (Mukwarami & van, 2024). Emergency Response Time is an indicator of efficiency of emergency services that are critical in the administration of the general population (Damaševičius et al., 2023 ). Lastly, Compliance with Standards verifies that services are in line with legal and regulatory frameworks, which is a mean to highlight the role of governance (Bolanle et al., 2024 ). We have Public Trust and Perception that attributes to the system where efficiency plays a part in shaping the citizen perception (He & Ma, 2021 ). Current framework boldly presents essential instrument to evaluate efficiency of delivery of public services, whose literature substantiates the incorporation of such factors in the modern times of public administration. The methodological process of the study is shown in this workflow diagram (Fig. 2 ). It starts with the research design (quantitative, cross-sectional), next is the U.S. municipal Chief Information Officers (CIO) data collection, efficiency indicator measurement, analysis through SPSS descriptive statistics and paired-sample t-tests and the research findings in the last. The framework offers an organized course of action to assess the use of AI to improve governance and delivery of the services after the public. 3.4 Method of Analysis of Data This research used data analysis, a procedure that required rigorous data cleaning with assistance of SPSS software in order to guarantee effective data quality. The before/after mean differences of AI implementation were analyzed in the form of a paired-sample t-test, determining whether the mean differences were significant. The descriptive statistics has been used to show central tendencies and variability in data. The evaluation, which can be attributed to the study hypotheses, considered the impact of the AI on the essential aspects of the efficiency of the deliverance of the services to the population, offering a better understanding of the transformational power. The interplay of paired-sample t-tests and descriptive statistics made it possible to get an in-depth analysis of the effects of AI, which can enlighten opinions about utilizing technologies in the government sector. 4. RESULTS 4.1. Convergent Corrulence and convergent validity Assessment of internal consistency and convergent validity of the measurement model was the first step in the evaluation of the measurement model. Internal consistency guarantees that the questions that were created to measure the latent construct do not contradict one another and provide a consistent response, whereas convergent validity determines that the indicators of an element are, in fact, measuring the specified underlying dimension (Cheah et al., 2018 ). Cronbachs Alpha, Composite Reliability (CR) and the Kaiser-Meyer-Olkin (KMO) test were used to determine reliability of the study in question, whereas convergent validity was measured with the help of the Average Variance Extracted (AVE). Cronbach values of all the constructs were significantly higher than the recommended minimum of 0.70 and ranged between 0.937 (Environmental Impact) and 0.959 (Data-Driven Decision-Making), as showed in Table 2 . These values reveal good internal consistency in all constructs, and that items were reliable in their ability to measure the constructs that they purported to measure. Likewise, the CR values (0.936 to 0.963) were above the course requirement of 0.70, thus confirming reliability of construct. AVE values were all above 0.50 (0.662 and 0.762) as recommended by (Gebremedhin et al., 2022 ). This observation shows an average of over 66 percent of the variance in the indicators were explained by their related latent construct giving strong evidence of convergent validity. As an example, the construct Timeliness of Services demonstrated AVE value of 0.762, which corresponds to high explanatory power of the measurement items. Measures like Public Trust and Perception (0.726) and Data-Driven Decision Making (0.743) also present great evidence of the robustness of convergent validity in the measurement instrument. The values of KMO of all constructs were higher than 0.90 reaffirming sampling adequacy and validity of the data to undergo factor analysis (Shrestha, 2021 ). Data-Driven Decision Making received the highest KMO value (0.947) and thus its statistical adequacy. All the findings support the idea that the constructs were not only reliable but also valid, and the measurement framework used in the analysis of the effects of AI on the efficiency of the public service delivery was robust enough. The findings of this step of analysis are congruent with other works, which explored the adoption of technology in the organizations within the sphere of the world of the government administration (Neumann et al., 2024 ; Criado et al., 2021 ). Similarly to the prior results, the values of reliability and validity found to be very high in this case highlight the fact that constructs regarding AI, namely automation, data-driven approaches to decision-making, and citizen satisfaction, can be not only conceptually but also empirically measured with great accuracy. Discriminant Validity Convergent validity, discriminant validity was also checked to ascertain that each construct was untouched by another construct. Developing the discriminant validity is vital, because it states that the specified construct has stronger variance with the indicators than the rest of the constructs in a model (Lim, 2024 ). In this research, it is suggested to use Heterotrait-Monotrait ratio of correlations (HTMT) as compared to the classical Fornell-Larcker criterion, it was demonstrated to be more successful in identifying discriminant validity concerns (Dirgiatmo, 2023 ). Confirmed by the HTMT results, inter-construct correlations were far below the conservative criterion of 0.85, meaning that there was sufficient discriminant validity. As an example, the relationship between Citizen Satisfaction and Public Trust and Perception was found to be 0.64 which is far much less when compared to the square roots of their AVEs (0.79 and 0.85). Likewise, Resource Utilization and Process Automation reported an HTMT ratio of 0.71, which also lies, below the critical value. The results of all these findings simply demonstrates that each construct in the model measured a different dimension of the role of AI in service provision, and there was minimal overlap among the constructs. The role of discriminant validity in the research conducted in a public sector cannot be underestimated. Constructs in the area of AI tend to have conceptually related dimensions - e.g., transparency, accountability, and citizen satisfaction - that might theoretically be correlated. The definition of empirical distinction of these constructs by the measurement model itself strengthens the validity of the further structural model test. Such finding is not only consistent with current studies that have potentially used HTMT to pre-validate AI and digital transformation constructs in settings of governance (Poth, 2021; Bellamy et al., 2023 ); it also aligns with the potential to validate HTMT itself to the predictive domain of constructs of AI and digital transformation in governance settings. 4.2. Respondents Demographics The demographic analysis gives an insight into the backgrounds and the characteristic of the Chief Information Officers (CIOs) involved in the study. The survey itself was completed by 210 CIOs in local government organizations in United States. Table 3 summarizes the distribution of the respondents in the age category and it was found that their mean age was 47 years with a range of between 34 and 62 years. The nature of this distribution reflects an experienced and active cohort of leadership in the trends of modern technological transitions. Genderwise the profile is more diverse as compared with previous international studies in the results. Although the sample of male CIOs is 71.9 percent, the female CIOs number is 28.1 percent. This is in contrast to the previous studies in Asia and Europe where women representation was found to be less than 20% (Roscher & Nissen, 2021 ; Criado et al., 2021 ). In this manner, this rise in female public sector leadership in the U.S. is an indication of slow improvement towards the elimination of the gender gap in leader positions in technology. On academic qualification, most of the respondents have at least a bachelor degree (55.7%), 34.3 with a master degree and 7.6 with a Ph.D degree qualification. Fewer (2.4%) cited other academic qualifications, such as professional certifications in either public administration or management information systems. The distribution shows that not only U.S. public sector CIOs are technically trained ones but also are diverse in terms of their education, which makes them better in handling the complex AI implementation projects. The dominance of technical discipline is also indicated in the academic background of respondents whereby computer science (59 percent) and engineering (17.1 percent) are the leading ones. That notwithstanding, vocations like public administration (6.2%) and social sciences (8.6%) were also evident. Such expertise is significant since the application of AI in the area of public service delivery demands the skills beyond technical systems that would stretch into governance, morals, and community-civic participation (Grant et al., 2024). The amount of professional experience of respondents also deserves to be mentioned (the average respondent is 19.2 years of IT-related experience and 11.7 years as a member of the public sector organization). This signifies the knowledge, technical and sectorial experience of the respondents to give informed opinion on the integration of AI in service delivery. The management of the adopters of AI in the local governments in the United States can be determined through the demographic nature of the sample which is quite informative on who leads this trend in the United States local governments. The technical background, educational degrees, and extensive professional experience seem to imply that the obtained findings represent the informed views that take into account the real-life experience as well as theory. Further, the general percentages of female CIOs and the presence of non-technical academic experience puts more of an emphasis on a broader and more inclusive (and less technical) approach toward adopting AI than has been reported previously in similar reports. Table 2 Construct reliability and validity using CR, AVE, Cronbach’s Alpha, and KMO Test Items (Examples) Items Coding Std. Factor Loadings Composite Reliability AVE Cronbach Alpha KMO Response Time RT1 0.782 0.940 0.662 0.935 0.926 Accuracy & Precision AP1 0.854 0.947 0.701 0.942 0.938 Resource Utilization RU1 0.823 0.952 0.693 0.945 0.927 Cost per Transaction CPT1 0.861 0.955 0.724 0.948 0.932 Citizen Satisfaction CS1 0.846 0.949 0.711 0.944 0.941 Accessibility AC1 0.811 0.936 0.678 0.930 0.912 Process Automation PA1 0.867 0.953 0.699 0.947 0.919 Data-Driven Decision Making DDM1 0.899 0.961 0.752 0.958 0.943 Timeliness of Service Delivery TSD1 0.914 0.958 0.768 0.955 0.933 Transparency & Accountability TA1 0.889 0.945 0.699 0.939 0.921 Adaptability to Changing Needs ACN1 0.902 0.941 0.682 0.936 0.922 Feedback Mechanisms FM1 0.876 0.948 0.728 0.942 0.918 Employee Satisfaction & Productivity ESP1 0.891 0.946 0.717 0.940 0.925 Environmental Impact EI1 0.803 0.933 0.664 0.927 0.911 Emergency Response Time ERT1 0.857 0.949 0.736 0.944 0.934 Compliance with Standards CSR1 0.842 0.941 0.691 0.936 0.928 Public Trust & Perception PTP1 0.864 0.950 0.724 0.945 0.936 Observation All Cronbach’s Alpha > 0.92, CR > 0.93, AVE > 0.66 → very strong reliability and validity. Table 3 Demographic data of CIOs in U.S. local governments Variable Value CIOs (n) 210 Average Age 47 years Gender Male: 71.9% / Female: 28.1% Academic Degree Bachelor’s: 55.7% / Master’s: 34.3% / PhD: 7.6% / Other: 2.4% Academic Background Computer Science: 59.0% / Engineering: 17.1% / Social Science: 8.6% / Public Administration: 6.2% / Mathematics: 3.3% / Others: 5.8% 4.3. AI Degree of Maturity The progressiveness level of the AI adoption in the U.S. local governments was measured, to determine the level of awareness, implementation, and institutionalization of AI-related programs. Maturity levels are necessary to understand the extent to which technology investments may increase the efficiency of improving public service and governance (Molinari et al., 2021; Castonguay et al., 2024 ). These findings, presented in Table 4 , exhibited a great awareness and familiarity of AI by the respondent Chief Information Officers (CIOs) that averaged 4.52 score out of a hypothetical total of 5. With this score, there is a great familiarity with AI technologies and that AI is gaining prominence in digital transformation strategies of local governments. Van Noordt & Misuraca, ( 2022 ) and Boobier et al. (2022) report that senior government officials currently focus on AI implementation in order to improve their operations and the services they provide to the population. In terms of project implementation, 93 percent of organizations indicated having active AI projects with 61 percent (medium-scale project), and 25 percent (large-scale project) representing a jump to production beyond pilot. Small-scope AI projects only constituted 14 percent of AI projects. This indicates that local governments are scaling up their AI projects, and integrating them into fundamental service delivery systems as opposed to prototyping isolated, scale-up applications. The history of AI application was characterized by a dramatic boost especially in 2016 onwards. Although 5 percent of the organizations had already started with AI projects in 2010 or earlier, 63 percent started them in 2019–2022 with another 20 percent initiating afterward. Such a rise in adoption coincides with the worldwide tendencies in the investments in AI, especially after the breakthroughs in machine learning and predictive analytics (Ramya et al, 2024 ; El Hajj & Hammoud, 2023 ). This dramatic raise in 2020, and even further since the emergence of the COVID-19 pandemic, implies that the pandemic has served as the impetus to the municipalities to expedite the process of implementing digital services and engaging automation. There has been an upswing in financial investments in AI as well. The value of AI-related projects divided by local government budget, on average, increased by 25–45 per cent, in comparison with the last decade where their proportion was never more than 10 per cent (Criado et al., 2021 ). This trend shows that AI is no longer being considered as an expenditure, but a strategic priority. Moreover, 96 percent of organizations stated the presence of specialized teams/ working groups, and such practice indicates the integration of AI programs in institutions where highly qualified staff can be used to develop, implement and supervise them (George & Wooden, 2023 ). Evaluation of technological infrastructure preparedness, 33% of organizations said they had mature technological infrastructure to implement AI systems and 37% were more matured. An additional 22 percent were at an early phase of developing infrastructure and 8 percent had poor or no AI infrastructure. This gap indicates that larger municipalities that can allocate more fiscal resources are ahead in terms of investments into AI infrastructure, whereas smaller jurisdictions are affected by the issues of low budgets and technical capacity, which confirms the evidence of digital divides in public administration in earlier studies (Omweri et al., 2024). 4.5 . AI Implementation Functionalities that are Affected Different functions of the municipal government have been significantly affected by AI technologies that have enhanced service delivery and internal functions of administration. The findings, which are depicted in Table 5 , illustrate the ways AI is changing essential organizational activities. Major areas that have been hit the most were transaction processing (94%) and public service delivery (88%). Such functions are helped by the fact that AI is capable of simplifying workflows, limiting bureaucratic inefficiency and increasing speed and accuracy of delivery of services. As an example, the chat-bot interface powered by AI is now a widespread solution to address the inquiries raised by citizens, whereas the predictive analysis enables optimizing resource use. Robotic process automation (RPA) has found application in back-office functions also and has been shown to increase operating efficiency (Kitsantas et al., 2024 ; Sun & Medaglia, 2022). AI also showed a significant effect in the field of organization network management (63%), the cooperation between departments and the flow of information. Management of big data used across several government agencies has improved decision-making and coordination across various units and optimised efficiency, as well as decreased redundancy, through the aid of AI (Neupane, 2024 ). Internally, AI has made a difference when it comes to clerical work (47%) and technical work (38%). Robotization of clerical routine jobs, like document processing, data entry has reduced the occurrence of errors in these areas and released a lot of staff to perform more demanding tasks. Within the sphere of technical professions, the AI has contributed to the process of surveillance of IT systems and the virtue of managing cybersecurity, which has led to the strengthening of digital equipment within the projects of the public government. These developments involved less input in AI in terms of executive management (22 percent) and political advisory roles (19 percent). Even though AI applications, including sensorimotor analysis and scenario modeling, are starting to aid policy formulations, their incorporation is still accompanied by some reservation as some issues of trust, ethics, and subjectivity of human decision-making in politics could arise (Lindebaum et al., 2024 ). Also, regulatory functions (26%) have been mildly impacted with the increased utilization of AI in areas of compliance monitoring and detection of frauds, although the regulatory structures in force have dampened the integration process (Engin & Treleaven, 2019 ). Table 4 Maturity level of AI adoption (U.S. public sector) Variable Response Awareness & Understanding Avg. 4.52 Ongoing AI Projects Yes: 93% / No: 7% Scope of Projects Small: 14% / Medium: 61% / Large: 25% First AI Project Initiation Before 2010: 5% / 2010–2015: 12% / 2016–2018: 22% / 2019–2021: 41% / 2022+: 20% Budget Allocation Avg. 25–45% Dedicated AI Teams Yes: 96% / No: 4% Technological Infrastructure Advanced: 33% / Moderate: 37% / Early Stage: 22% / Limited: 8% Table 5 Functionalities Affected by AI Implementation Functionality % Affected Capacitation 61% Executive Management 22% Political Advisory 19% Technical Duties 38% Clerical & Assistant Tasks 47% Regulation 26% Management of Networks 63% Public Service Delivery 88% Processing of Transactions 94% 4.6. AI Influence on the Efficiency of Service delivery To test the hypothesis that application of AI improved service delivery efficiency relative to the efficiency rating before AI implementation, a paired sample t-test was used to compare pre and post AI rating on efficiency. The findings, which were tabulated in Table 6 , indicated that the level of efficiency improved in all of the surveyed organizations at statistically significant level. The average service delivery efficiency rating went up, M = 3.12 (SD = 0.87) before the adoption of AI to M = 4.46 (SD = 0.54) after implementation. The paired t -test revealed this difference as very significant ( t = 18.42, p < 0.001 ), with great effect size (Cohen s d = 1.36). These results provide empirical support to the idea that AI improves the efficiency of operation in government service organizations. The degree of the detected improvement indicates that the AI technologies are not the gradual tools but the revolutionary instruments that can help to redesign the way municipalities may provide the necessary services. The same evidence has been reported in previous research, which revealed that AI tends to minimize the service response time, automation of routine administrative tasks, and citizen satisfaction (Sun & Medaglia, 2022). The efficiency improvements were especially significant in areas where the same or similar operations were repeatedly performed using large amounts of data e.g. transaction processing, records maintenance, and answering of questions in the public. Such findings are supported by the previous descriptive analysis (Section 4.5 ) pointing at the agglomeration effect of the AI effects in the transactional and service delivery processes. Optimizing bottlenecks and the ability to predetermine resource distribution seems to solve inherent inefficiencies in bureaucratic systems that have plagued the modern system (Mämmelä et al., 2018 ). Table 6 – Paired Sample t-Test (Before vs After AI Implementation) Indicator Mean (Before) Mean (After) Mean Diff. t-value p-value Response Time 3.02 4.34 + 1.32 31.42 < 0.001 Accuracy & Precision 3.15 4.41 + 1.26 29.57 < 0.001 Resource Utilization 3.08 4.36 + 1.28 30.21 < 0.001 Cost per Transaction 2.97 4.18 + 1.21 28.44 < 0.001 Citizen Satisfaction 3.12 4.39 + 1.27 29.88 < 0.001 Accessibility 3.05 4.28 + 1.23 28.73 < 0.001 Process Automation 2.89 4.33 + 1.44 32.11 < 0.001 Data-Driven Decisions 3.01 4.35 + 1.34 31.89 < 0.001 Timeliness of Services 3.09 4.37 + 1.28 30.64 < 0.001 Transparency 3.11 4.32 + 1.21 29.04 < 0.001 Adaptability 3.07 4.29 + 1.22 29.67 < 0.001 Feedback Mechanisms 3.10 4.36 + 1.26 30.05 < 0.001 Employee Satisfaction 3.14 4.38 + 1.24 29.55 < 0.001 Environmental Impact 2.92 4.22 + 1.30 30.72 < 0.001 Emergency Response 3.08 4.40 + 1.32 31.18 < 0.001 Compliance 3.16 4.41 + 1.25 29.82 < 0.001 Public Trust 3.20 4.47 + 1.27 30.44 < 0.001 This chart shown (Fig. 3 ) the composite reliability (CR), average variance extracted (AVE) and the Cronbach alpha values of the five constructs (Efficiency, Trust, Innovation, Awareness and Maturity). All measures surpassed recommended limits (CR > 0.70, AVE > 0.50, 0.70 > 0.70), which is a proof of internal consistency and convergent validity. HTMT values across constructs heatmap (Fig. 4 ). The ratios were all under the critical level of 0.85, which affirmed discriminant validity according to Henseler et al. (2015). Stronger associations between the inter-construct are shown in darker shades. Box of square roots of AVE (the diagonal elements) with the inter-construct correlations (the non-diagonal elements) (Fig. 5 ) (F. All the diagonals were higher than the relevant correlations, as it is a confirmation of discriminant validity following the Fornell-Larcker criterion. Pie chart (Fig. 6 ) illustrating the gender distribution of Chief Information Officers (CIOs) that were surveyed. There was a gender disparity in tech leadership with two-thirds of the respondents being male (72%) and female CIOs making 28 percent of the sample. Bar chart analysing the most influenced organisational areas by the adoption of AI (Fig. 7 ). The most affected areas included transactional tasks (85%) and service delivery (78%) followed by operations (69%), internal management (64%) and decision-making (52%). Results ( comparison of the estimated means of before and after AI integration based on the results of the paired-sample t-test) (Fig. 8 ). The level of efficiency increased at a great level, as it was measured at 3.12 (SD = 0.87) before and 4.46 (SD = 0.54) after the intervention, showing the positive value of AI in improving the industry of delivering public service (p < 0.001). 5. DISCUSSION The current paper was an exploration of the way in which artificial intelligence (AI) is transforming the delivery of services by the government to the populations of the US, with emphasis on the levels of maturity, functional use, and efficacy advantages by local government. The results can be used to support an expanding body of knowledge pertaining to the fact that AI is not merely a technological marvel, but that it also serves as a tactical agent of administrative change. A confirmatory analysis of the measurement model validity proved that constructs of automation, efficiency, transparency, and citizen satisfaction were reliably measured. This is in line with their precedents regarding the use of strong methodological frameworks in the studies of the field of public administration, especially in assessing the emergent technologies (Ajayi et al., 2024 ; Skotnicka-Zasadzie specialized in August 2025; Skotnicka-Zasadzie specialized in Wolniak, 2025). Both the demographic profile of respondents (the majority of them have a background in computer science and engineering) and the technical application of AI in local government clearly point to the fact that adoption is highly driven by expertise in the field. Nevertheless, the appearance of public administration leaders and social scientists indicates a slow transition to a more interdisciplinary representation, which is required to deal with the issue of governance and ethics (Yigitcanlar et al., 2024 ). Remarkably, the number of women in the role of a chief information officer, however modest, was higher than those reported in some global settings, which means gradual movement toward inclusions in the realm of digital managerial practice. The findings on AI maturity confirmed that the majority of municipalities have transitioned past pilot initiatives into an institutionalized practice of AI with 93 percent of respondents currently having activities in place, with a quarter of them having large-scale programs. This conclusion can be matched with the contemporary research of the COVID-19 pandemic that served as a driver of digital acceleration, as it significantly raised public-sector AI investments worldwide (Alshahrani et al., 2024 ; Datta, 2024 ). Nevertheless, there were disparities in the infrastructure with smaller jurisdictions recording lower levels of preparedness in line with the digital divides that were present. This unbalanced uptake was also observed in European and Asian settings, where AI solutions cannot be scaled because of resource constraints (Yigitcanlar et al., 2024 ). These differences highlight the necessity of specific capacity-building initiatives, which would guarantee the pro-protional benefits of AI among municipalities. The functional levels, the greatest effects were realized in the transactional processes and the frontline service delivery, whereby, more than 90 percent of the respondents indicated enhancements in accuracy and timeliness. These results are aligned with the conclusions made by global researchers referring to the transformation of bureaucratic work processes due to the use of AI applications, including chatbots, predictive analytics, and robotic process automation, and the enhanced communication between citizens and the government (Ajayi et al., 2024 ; Dwivedi et al., 2023). Governments achieve administrative convenience, as well as can re-distribute human resources to more value-added processes thus increasing overall efficiency, by automating routine functions. An increase in the scores of transparency and accountability identified in the current study corresponds well with prior studies that highlight the AI capability of enhancing confidence in governance once responsibly implemented (Skotnicka-Zasadzień & Wolniak, 2025 ). In spite of these achievements, the research found less conspicuous uptake of AI in executive managerial, political advisory and regulatory governance roles. This is comparable to the condition in the rest of the world where the use of AI in strategic decision-making is minimal due to ethical, legal, problems of trust (Datta, 2024 ; Yigitcanlar et al., 2024 ). Though the initial projects of predictive governance and compliance observation are encouraging, human decision making remains as an important policy maker and watchdog. The results, in turn, add weight to the position that AI is not an alternative to leaders but rather should be introduced as an addition to human decision-making abilities (Ajayi et al., 2024 ). Paired t-test findings gave good statistical evidence that the adoption of AI led to a high degree of efficiency resulting in improved service delivery ratings averaging 3.4 and 5.0 in the pre-AI and post-AI periods respectively. Citizen satisfaction, timeliness, and cost-effectiveness improvements were similar to previous empirical research results of the same findings in various governance environments (Alshahrani et al., 2024 ; Skotnicka-Zasadzie ( 2025). Notably, these findings indicate the general use of AI in the service of inclusivity that brings solutions to underserved populations in history because of inefficiencies in the bureaucracy and corruption. AI can reduce the extent of discretionary bias by more significantly limiting human input into transactional procedure, which promotes fair access to services in a sideways way (Datta, 2024 ). Earlier criticisms in the literature, respondents were concerned about the possibility of empowering algorithmic bias, cybersecurity, and employing labour-replacing rules (Yigitcanlar et al., 2024 ; Skotnicka-Zasadzie 2025). This is compounded by infrastructure gaps that further constrain solutions based on the AI since some municipalities will have underdeveloped infrastructure, especially in rural areas or in general where resources are a constraint. Such constraints imply that although AI has proven highly efficient, it will not fulfill its transformative potential until investments in matching governance mechanisms and safeguards as well as capacity building are undertaken in parallel. It is also richer to compare the international experiences to the U.S. context when interpreting these findings. Research in other countries, like Australia and Hong Kong, finds an easier path to adopt AI because they had stronger infrastructure and advanced digital literacy, whereas countries like India still have more skepticism and unequal adoption (Yigitcanlar et al., 2024 ). The homogenous U.S. experience, defined by a high adoption rate and marked inequalities, can therefore be positioned somewhere between these two extremes and hold clues as to how AI strategies should be modified to suit socio-economically and institutionally diverse contexts. Finally, this research has proven that AI is a central catalyst in efficiency, inclusiveness, and transparency in the local government service delivery. This is because the meaning of constructs was confirmed, and the research proved that efficiency had in fact improved by a significant amount of degrees, a factor that brings the research considerable empirical evidence to the discussion of digital governance. It also, however, makes the need of managing infrastructural disparities, ethical hazards, and human-AI complementarities more pronounced. Policy should therefore target to create equitable infrastructures, formulation of ethical AI and interdisciplinary leadership to achieve the greatest good of AI public administration. CONCLUSION The major aim of the research work was the determination of the effect of artificial intelligence (AI) on the efficiency of the delivery of public services in the United States. Through strict statistical analysis, such as reliability and validity testing, discriminant validity testing, and paired t-tests, the study came up with substantial empirical data on the role of AI in changing municipal services. The results showed that the degree of awareness and practical comprehension of AI is pronounced among Chief Information Officers (CIOs) in American cities. Moreover, such financial investments and the creation of special AI working groups are indications of how seriously local governments are taking up the use of AI-driven solutions. Demonstrated also in the study is the development of AI maturity with the help of advanced infrastructure and the specialization of the workforce. Notably, the analysis also ascertained that the performance of service delivery is highly improved by the adoption of AI. Citizen-centric activities, which included the responsiveness of services, management of records, and the response to query, were reported to have improved in measurable proportions by the municipalities. Besides cutting out bureaucratic lag, these advances also cut out unwanted human labor and thus eradicate the chances of such mistakes as inefficiency or the insertion of unnecessary procedure stops. Furthermore, and possibly more importantly, AI-powered technologies open up the limited access to underserved cohorts, leading to a more inclusive and diverse universe of the Public sector. These results highlight the prospect of AI to promote transparency, enhance trust in the government institutions, and make a significant contribution to citizen contentment. The key value of the study is providing straightforward recommendations to the public institutions at the beginning of the AI adoption. The findings can provide a benchmark to policymakers and administrators who need to align the efforts of digital transformation to efficiency, accountability, and inclusiveness. The results are further valuable suggestions to the governments since they can utilize the findings to design conducive policies that enrich the adoption of responsible AI and at the same time develop sustainable and citizen friendly e-governance systems. The research does not lack limitations. It is possible that the emphasis on U.S. municipalities restricts the generalizability of findings to other countries, ones with socio-economic and governance settings that differ. Additionally, although efficiency improvements were shown convincingly, the threats in the form of ethical concerns, algorithm biasing, or security-related issues were rather unexplored. Future research needs to take on an international, cross-national comparative view as well as examine the wider socio-political implications of AI in governance. A further discussion on ethical protection, labor responsiveness, and citizen sentiment will also be instrumental to make sure that the implementation of AI in government services goes without being biased, non-transparent, and unsustainable. Declarations Funding: No external funding was received for this research. Clinical Trial Number: Clinical trial number: not applicable. Consent to Participate: Consent to Participate declaration: not applicable. Consent to Publish: Consent to Publish declaration: not applicable. Ethics Declaration: Ethics declaration: not applicable. 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16:16:37","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":257722,"visible":true,"origin":"","legend":"","description":"","filename":"aa92e2b4557c4cab9b5d3db29db371171structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/1fb4b8749e0e02e01360764d.xml"},{"id":98978880,"identity":"61c4367a-9b01-42c2-8f16-363f695eccad","added_by":"auto","created_at":"2025-12-25 06:02:08","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":269722,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/349d46942075d85ee59ff6ca.html"},{"id":98978852,"identity":"3dd5cee2-f483-48af-b42c-279214509dd4","added_by":"auto","created_at":"2025-12-25 06:02:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":383662,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Structure of the Artificial Intelligence Integration and its Effect on the Public Service Provision Efficiency in the United States\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/96412b39180872e1526733be.png"},{"id":98978858,"identity":"e5d11317-5368-42d0-a12e-9ac250af6852","added_by":"auto","created_at":"2025-12-25 06:02:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":494043,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical Research Method Process in researching the effects of artificial intelligence on the efficiency of the delivery of public services in the United States.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/ad85709c296a0a3867305a21.png"},{"id":98978853,"identity":"2bea39e0-5f5f-4e9f-ae28-b8362d5af1bc","added_by":"auto","created_at":"2025-12-25 06:02:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41089,"visible":true,"origin":"","legend":"\u003cp\u003eConstruct reliability and validity of a study\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/a91ec2762cd2636f6f1bcfdb.png"},{"id":98978871,"identity":"055b7b85-f2fa-4668-9cd8-720eafcf34bc","added_by":"auto","created_at":"2025-12-25 06:02:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69763,"visible":true,"origin":"","legend":"\u003cp\u003eConstructs may be compared with HeterotraitMonotrait (HTMT) ratios\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/0d245edd8d254b16818a6344.png"},{"id":98978862,"identity":"dc211a02-537c-409d-9e24-2baa8c9475ae","added_by":"auto","created_at":"2025-12-25 06:02:07","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":61675,"visible":true,"origin":"","legend":"\u003cp\u003eFornellLarcker definition of discriminant validity\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/7c1dff518e3067f7539c6080.png"},{"id":99311681,"identity":"eda2fda3-e899-4b9a-ad57-6050e091f75c","added_by":"auto","created_at":"2025-12-31 16:16:27","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16963,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of CIOs gender in the sample\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/f69f648cc91f0f17b488bca5.png"},{"id":98978874,"identity":"2e2522dd-460e-4356-8764-4ccbeeb88673","added_by":"auto","created_at":"2025-12-25 06:02:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":41128,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of AI on the operations of organizations\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/b5eac380b8f2af04600b3c6a.png"},{"id":98978861,"identity":"8a2a74e6-eaf7-4376-a2c2-ff9d500ddead","added_by":"auto","created_at":"2025-12-25 06:02:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":20547,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of the use of AI on service delivery efficiency\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/c0627710655fed5d20b41c7f.png"},{"id":100950917,"identity":"b958060e-01ad-4740-8180-c3f17b4a397e","added_by":"auto","created_at":"2026-01-23 07:09:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2132018,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8142308/v1/c5e7fb6e-2069-4b4a-a8bd-2c2eed334479.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEvaluating the Impact of Artificial Intelligence on Public Service Delivery Efficiency in the United States\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eArtificial intelligence (AI) can be defined as the process of incorporating cutting-edge technology into different areas and turning into a significant component of technological changes (Varriale et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The most significant role of AI in the field of public service delivery would be to ensure that there is increased effectiveness and efficiency in service delivery, which would lead to good governance and enhancement of citizen satisfaction levels. According to the manuals of the International Monetary Fund (JM), governance refers to all dimension of managing a country including the economic policies, regulatory regime as well as the readiness to pursue the rule of law. Positive governance is associated with induction of fairness, emancipation, job creation, visibility, and good distribution of services to all the people (Ramanujam \u0026amp; Farrington, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kreinin et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In a traditional way of governance, bureaucracy and inefficiency of resources usually inhibit its performance, leading to inferior delivery of services (Zou, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To maximize the effective use of resources and reduce corruption associated with bureaucracies, efficient governance is needed so that it provides timely and accurate service delivery to various citizens according to what they need. AI has come with some great chances of transforming governance activities and increasing efficiencies in the delivery of services to the people. With the massive works done in data analysis, pattern recognition, and automation, AI has great potential to provide resolutions to the mired governance systems. Governments that use AI technologies allow them to optimize processes, better decisions, and deliver services to citizens. In the United States, AI in the field of governance and the provision of services has been singled out as a method of introducing a new epoch in citizen-friendly administration (Ali, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe U.S being one of the top economies in the world, it has much to be concerned about in the AI revolution. The U.S. government initiated the National AI Initiative Act aligning on a path of developing AI uses across all sectors which accentuates the need of implementing policies and frameworks that govern proper and ethical AI usage in the government. The United States, China, France, and Japan have experienced substantial growth in their investments towards AI in the form of AI-related research and development initiatives, investments in AI start-ups, infrastructural investments towards AI and AI led public procurement processes (Salas-Pilco, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The government of the United States has also outlined a strategic plan to establish the AI centres of excellence in the leading universities and research institutions, which has been a big step to achieving its vision to have an AI for America (Abulibdeh et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe capacity of AI in the governance of the U.S. in the globalized environment offers an opportunity where the country can become more competitive in the global scope. Its adoption of AI-based innovations in providing public services will influence how the other countries are dealing with such issues (Marzdar, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Minimal Government, Maximum Governance is the vision of the U.S government to establish a government where government administration and governance activities are to operate smoothly without much involvement and obstacles (Khatib et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this way, combining AI in governance and implementing its principles in the public policy has significant potential to enhance interaction with citizens, accountability, and the efficiency of service delivery, as well as to streamline the work of the administration (Pananrangi et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis paper focused on enhancing the quality of this research by putting to test the influence of AI in ways adopted in the United States of America in the delivery of public services. This research analyzed first the level of maturity of AI uses in U.S. municipal corporations, considering such aspects as awareness, ongoing projects of AI, project schedule, AI project resource strength, and availability of technological infrastructure (Anshari et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Besides, the research aimed to understand the implication of AI integration with different operation functions in municipal corporations and how it can be involved in efficiency improvement. Finally, this study evaluated how these municipal corporations will be affected by the AI implementation in various aspects of service-delivery effectiveness.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Research questions\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRQ1: How mature is the AI adoption in municipal corporations in the U.S?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ2: What are the impacts of the introduction of AI in public service industry in regard to various operational functions in the municipal corporate bodies and what roles is AI playing in improving efficiency?\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRQ3: What are the impacts of the application of AI on efficiencies of service delivery dimensions in the municipal corporations?\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Objective\u003c/h2\u003e \u003cp\u003eThe main objective of this study was to gauge the levels of maturity of artificial intelligence (AI) in municipal corporations in the United States. In particular, the study sought to establish the degree of establishment of awareness, existing projects, technological base and capacity of resources in these institutions. The second goal was to examine how the integration of AI affected the functioning of the municipal corporations in terms of enhancing the efficiency of decision-making, resource management, and the administrative process in general. The third goal was to determine the impact of adoption of AI on various aspects of effectiveness in service delivery such as the response time, accuracy, satisfaction of citizens, transparency, and accountability. In order to fulfill those purposes, the research has adopted a quantitative and cross-sectional design based on a before and after survey of the Chief Information Officers (CIOs) of the municipalities in the U.S. and the results were analyzed by means of the use of descriptive statistics and paired-sample t-tests to determine the significance of gains ascribed to the implementation of AI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3. Research Hypotheses\u003c/h2\u003e \u003cp\u003eThe hypotheses of this study were made as follows according to the objectives of the study:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eH 1 Above the level of adoption of artificial intelligence (AI) in municipal corporations of the United States significantly changed before and after implementation, meaning that the indicators of maturity, in the form of awareness, projects, and technological infrastructures, can be measured.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH 2: The adoption of AI in municipal corporations led to a great enhancement of the operational activities such as decision-making, use of resources as well as efficiency in administration.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eH3: AI played a significant role in streamlining service delivery in municipal corporations as demonstrated through an increase in response time, accuracy, transparency, citizen satisfaction and accountability.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis conceptual framework shows (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) how AI integration into the governance process (awareness, technological infrastructure, and AI project resources) relates to its implementation in terms of municipal corporation and the adopted improvement of service delivery dimensions. The framework underlines how the implementation of AI will improve the efficiency of operations by increasing the speed of response, accuracy of services, transparency, accountability, and overall satisfaction of citizens as indicated in the research hypotheses of the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"2. LITERATURE REVIEW","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Artificial intelligence\u003c/h2\u003e \u003cp\u003eArtificial Intelligence (AI) has become a disruptive tool, transforming a great variety of industries, such as finance, healthcare, education and transportation (Akinsola et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Artificial intelligence works like its human counterpart, it works in the form of advanced computational models and algorithms that emulate human thought, in terms of learning, reasoning, and problem-solving (Konar, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Interdisciplinarity in AI is multifaceted and comprises a number of subparts such as machine learning, natural language processing, computer vision, and robotics, each of which helps to fast-track development and wide integration into AI systems (Fergus \u0026amp; Chalmers, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is important to understand the landscape of the current research and development of AI to harness that potential and respond to the ethical, societal and technical challenges to which it is giving rise.\u003c/p\u003e \u003cp\u003eRecent years have seen the introduction of the concepts of deep learning as a branch of machine learning, which is inspired by the structure and functioning of the human brain (Sejnowski, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Vishnoi et al., \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Such techniques as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) form deep learning models that have changed the way humans do image and speech recognition, natural language processing, etc. (Yadav et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The availability of large datasets and the availability of powerful computing resources have motivated the emergence of AI models that perform brilliantly in the real world settings (Jia et al., 2023).\u003c/p\u003e \u003cp\u003eA lot of available literature is dedicated to more traditional methods of AI, such as machine learning and deep learning, it is critical to note that generative AI is a new paradigm in the field. Generative AI has become a change in the field of machine learning where systems can generate new content including text, images and sounds (Foster, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Generative models (such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)) are based on learning underlying probability distributions to synthesize new data and have newer machine learning methodologies at their heart, rather than the pattern recognition and categorization forms of traditional AI (Akkem et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Innocent, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Generative AI has led to great strides in the area of natural language processing, image creation and generating creative features. As an example, it is possible to mention generative models, such as StyleGAN (Melnik et al., 2024) that are capable of creating high-quality and realistic looking images, or language models, such as GPT by OpenAI (Hadi et al., 2023; Bhattacharya et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that show compelling results by making human-like text of the same quality, applicable to a variety of fields.\u003c/p\u003e \u003cp\u003eIn spite of all the positive applications, AI is characterized by some major drawbacks that stall its active adoption and implementation. Among the most vigorous concerns, the problem of the so-called \u003cb\u003eblack box problem\u003c/b\u003e of AI as insufficient transparency and interpretability of the operations of decision-making in AI should be mentioned (Nešpor, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With greater complexity comes an even more complicated picture of how AI systems work and how they make decisions, thus creating ethical and regulatory issues, particularly in sensitive contexts such as healthcare and even criminal justice (Singh, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Along with that, the issues regarding privacy, preferences, and fairness ought to be resolved to create the accountable and fair AI systems (Alam, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther development of AI studies, it will be crucial to focus on the challenges associated with interpretability, fairness, and accountability to make a responsible introduction of AI to the society (Cheng et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Trans-disciplinary engagements and morality will be key towards future direction of AI, ensuring the development of human centered AI and not an alternative to human performance or abilities (Cowin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Once these issues are resolved and followed by ethical standards, AI could create revolutionary change and bring a more inclusive and sustainable society.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. AI and the Government\u003c/h2\u003e \u003cp\u003eAI does not have a standard definition (Dissanayake \u0026amp; Dissanayake, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) define it as systems that are able to do tasks that a human would usually perform with intelligence or that a human would make rational decisions via logical thinking that is performed by some system. Anshari et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) define AI as a set of digital technologies that, in addition to improving the efficacy and effectiveness of the delivery of public services, rebuild the mechanism of offering and providing public services fundamentally and are going to have a long-lasting influence on the organization of the services in the public sphere (Morgan et al., 2025; Momen \u0026amp; Ferdous, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to (Siemens et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), AI is a matter of digitally overlapping technologies that make machines capable of learning on their own and resolving cognitive problems without human interventions. According to Tripathi et al., (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) AI entails a combination of computer science and high-quality datasets that result in novel approaches to problem-solving. Machine learning and deep learning are the most essential subfields in this sphere that help in creating expert systems that can make extrapolations or classify data, depending on what is input.\u003c/p\u003e \u003cp\u003ePublic organization integration and adoption of AI technologies are quickly growing. The latest papers have emphasized the disruptive nature of AI in the governmentze domain including service delivery and policy making (Alshahrani et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Leitner \u0026amp; Stiefmueller, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This encompasses not only observing the impacts of AI on personnel in the public administrations (Young et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nzobonimpa, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but also on how citizen interaction with the governmental bodies may be affected (Grimmelikhuijsen et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). During the last ten years, e-governance efforts have been targeted on successful efficiency and cost-reduction. In the recent past, the technological world has made major steps towards improvement of the administration of the people. The fields of healthcare, education, security, and defense are some of the areas where AI is being used in public sectors (Achanta, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun \u0026amp; Medaglia, 2019). Applications of AI to service delivery in the public sector are also considered as the following stage of the transformation of the Information and Communication Technologies (ICTs) in the social context of social media, robotics, and big data waves (Aithal \u0026amp; Aithal, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mikhaylov et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) highlight that AI and algorithms can revolutionize some vital functions of pubic sector organizations and individuals, who are part of such organizations. Another valuable point related to the contribution of the emerging technologies to the enhancement of e-government services in discrete countries is presented by Jain et al., (2019).\u003c/p\u003e \u003cp\u003eA trend in the development of a technology-related style of governmental service delivery is a move to increase the efficiency in service maintenance and delivery with the quality aspect remaining intact on the technological surface (Kachhadiya, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Various functional areas covered under this transformation of the kind of public service delivery that is also made possible by AI include human resource management, strategic management, performance evaluation, and institutional communication (Chilunjika et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The area of education, healthcare, tax management, social benefits, border management, and emergency management are also areas where AI is used (Abid et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The ability to aggregate and analyze large amounts of data, and using open data, is an essential factor contributing to the AI interface between government services (Androutsopoulou et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The application of AI to the delivery of public services provides government agencies with an opportunity to make better decisions, to communicate with citizens in a more efficient manner, to personalize the services, and to decrease the administrative burden (Milakovich, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Latupeirissa et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This results in an increase in the quality of service and an increase in the value of the public (Herdiansyah, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In such spheres as process automation, knowledge management, predictive analytics of resources, resources allocation, conversational agents, fraud detection, and provision of expert support, AI technologies are in use (Bello \u0026amp; Olufemi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The public services use of AI has triggered both euphoria and fear as many governments all around the world are embracing AI in an effort to streamline their decisions and provide more streamlined services. Government accountability and service quality are also being increased by AI systems as through AI-driven bots in Brazil, fraud and corruption can be identified (Johnson, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, the deployment of the models empowered with AI makes communication between the individuals and state organizations better, downgrading the service delivery (Poudel, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Generative AI has become a game changer in the service delivery sector. The examples of technologies that are very successful in solving image synthesis, natural language processing, and content generation include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) (Ko\u0026ccedil; et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Frolov et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such models allow replacing document creation and personalizing communications; they allow automating and analyzing huge amounts of data used in decision-making in public administrations. These cost savings, enhanced service quality, enhanced citizen satisfaction, arise after the implementation of generative AI in public services (Nica et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). With the help of the generative models, governments will unlock new possibilities of innovation and will be able to establish more responsive public institutions.\u003c/p\u003e \u003cp\u003eStudies have identified the many uses of AI in the governance of the people such as efficiency, fewer risks and better citizens participation (Duberry, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Safitra et al. (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), there are ten areas where AI can be useful to the government and can be used in knowledge management, high-order data analytics, and so on. When dealing with the effective deployment of AI, the process involves new knowledge acquisition and skills, which have the potential to cause transformational changes in organizational practices (Zong \u0026amp; Guan, \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). A planned implementation, acceptance, and seamless development of AI into a staple will ensure a successful integration of AI (Rane et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sun \u0026amp; Medaglia, 2019). The results of AI on the workforce in the government and citizen-state relations have also been a focus of the scholars (K\u0026ouml;nig, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ulriksen \u0026amp; Plagerson, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Like earlier e-government efforts, AI-enabled technological advances are supposed to increase efficiency and minimize expenses in the sphere of administration (Ajayi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This type of smart governance aims to ensure the high efficiency of service provision and increased participation of citizens through online spaces (Gil et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pereira, 2018).\u003c/p\u003e \u003cp\u003eThe role of ethics in the sphere of AI in the public administration is gaining growing significance. A rising level of concern over the proper use and adoption of artificial intelligence, governments and tech businesses have delivered principles of responsible AI implementation (Laat, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yigitcanlar et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These guidelines provide broad guidelines to the use of AI in the public domain. There is still limited research on the place of governments as the users and regulators of the AI despite its increasing presence and significance (Zaidan \u0026amp; Ibrahim, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The growth in AI application, it is important to know how governments can regulate and use AI technologies in a dual way.\u003c/p\u003e \u003cp\u003eThe AI technologies are also being used in the municipality corporations to enhance service delivery and the efficiency of operations in the locations. As an example, chatbots armed with AI afford 24-hour citizen service by answering questions, facilitating requests, and availing information (Rainie \u0026amp; Anderson, 2021). Predictive analytics based on AI are used to optimize resource allocation and decision-making in such sectors as infrastructure maintenance, waste management, urban planning, etc. (Ojadi et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Smart sensors and the Internet of Things devices in urban infrastructures provide a population with an opportunity to monitor parameters of urban life, including traffic flow, air quality, and energy, in real-time, which will assist cities in the creation of evidence-based policies (Bibri, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, AI drones are also considered to perform aerial surveillance, respond to a disaster, and inspect the infrastructure (Hildmann \u0026amp; Kovacs, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By using such AI applications, municipal corporations seek to maximize the use of resources and improve service provision and urban management.\u003c/p\u003e \u003cp\u003eThe pertinent literature reveals that the integration of AI in the field of public services has a transformative character but emphasizes its challenges, as well. According to Ejjami,(2024), the integration of AI enhances citizen unitary services and the automation of the usual municipal tasks in the administration. According to Selvarajan, (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), there is an improved decision-making as a result of analytical capabilities of AI. The research by Skotnicka et al., (2025) was one of the first to give baseline knowledge about how AI was used in the sphere of public administration. (Emma, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) remind that transparency, and accountability are crucial to prevent bias in an algorithm. The factors of moral responsibility, including fairness and equity, impartiality, discrimination, and equity are covered by (Zhang et al., 2024) who warn that AI can interfere with people-oriented elements of service provision. Fares, (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) affirm that oversight structures should guide the fairness and non-discrimination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Problems, Challenges, and Advantages\u003c/h2\u003e \u003cp\u003eAlthough more people are talking about it, the implementation of AI within the realm of a public sector is not very high, and especially compared to those companies in the private sector (Mikhaylov et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Some of the threats to the integration of AI involve insecurity of data or ethical or privacy concerns (Li \u0026amp; Zhang et al., 2017). The fact that AI is capable of doing what a number of employees used to do increases fears of displacement of jobs among the workers in the public service sector (Hong, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Further, effective regulatory control and modern regulations of the field are also essential aspects of the AI application to the public sector (Almeida et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome barriers to the integration of AI that have been identified by Kiilu et al. (2025) embrace the quality of the system, financial viability, the social legitimacy of AI, trust, privacy, safety, accountability, and ethical concerns. Furthermore, there is a possibility that AI would increase inequalities and undermine trust because of the risks associated with the violation of rights, digital divide, biased policing, and rising inequality in economic terms (Lainjo, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dinker, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Even though the application of AI has tremendous potential in improving the services provided to the population, it is essential to discuss these issues to guarantee fair and ethical implementations. The scholars also highlighted the potential challenges of AI adoption in the public sector, such as a shortage of technical cadres, abuse of AI, privacy, transparency, and the ethical issue of the use of AI and when to use it (Datta, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The effect of AI on executive discretion and transparency in the context of public organizations has yet to be researched in earnest. According to Boer and Cetina et al., (2024), the worldwide IT adoption transforms the decision-making in both the government and other public organizations with automation undermining administrative discretion. According to Zerilli et al. (\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the issues around the lack of transparency of algorithmic decision-making and the decrease of discretion of human decision-makers were mentioned.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Research gap\u003c/h2\u003e \u003cp\u003eResearch on AI in the public sector has expanded rapidly, but empirical studies in the public sector are limited. A small number of scholars have examined the role of AI in executive discretion and transparency (Bullock et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Young et al., \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Meijer et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Other studies have culminated in the views of Chief Information Officers (CIOs) regarding AI integration, how public value can be created with AI (Walsh et al., \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), how to apply AI in predictive policing (Meijer et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and pandemic response. Few studies address the question of factors determining the success of AI implementation in the sphere of public sector (Mikhaylov et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Campion et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tangi et al., 2023; Sun \u0026amp; Medaglia, 2019).\u003c/p\u003e \u003cp\u003eWhen developing the technology of an artificial intelligence, this effort proves to be complicated and manifold with a lot of advantages and uses. Hence, considerable opportunities can be found in investigation of the patterns of AI adoption in the sphere of public service provision. The empirical research is needed in order to connect the theory and application. The aim of this study will be to determine how effective AI has been in its delivery of information during the process of public service provision and compare the data both pre and post the pandemic. Through this comparison, the paper will attempt to give details on how AI can assist in improving service provision in the government.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESEARCH METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Research Design\u003c/h2\u003e \u003cp\u003eThe research is a quantitative study that follows the original research on the integration of AI into governance (Birkstedt et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and has a cross-sectional research design to determine the contribution of AI to the efficiency of public service delivery. The study design governs the patterns of Criado et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) about governance and technology. The main respondents to the study will be Chief Information Officers (CIOs) in governmental institutions. The effectiveness of AI application in enhancing the efficiency of public services is studied through the use of a comparative analysis strategy emerging on the basis of Criado et al. (2022) and Mishra et al., (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data collection and Sample\u003c/h2\u003e \u003cp\u003eThis paper was based on the analysis of the information technology used by CIOs in big city councils in the United States, specifically, in the local governments that have a population above 100,000 citizens. They are 252 municipalities in one of these categories, all of them identified by the corresponding national statistical sources in 2024. The urban aspect of the USA provides an ideal environment to examine AI application in the field of delivering modern public service. Digital transformation experienced by the U.S. government, such as Smart Cities, also increases the prospects of the AI-driven innovations. Even though the study is a U.S.-related one, the results can be generalized to other contexts that may be similar to this one around the world (Bojić, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince the application of AI is still in its initial phases in the American city councils, the city councils with greater populations have a higher chance to implement these technologies. Even though the adoption of AI can be described as rather new, the cities can be discussed as the early adopters of innovations due to their active interactions with the sphere of technology (Wang et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study employed a before and after implementation method to examine the effect of AI on the delivery of services to the people. Researchers gave a survey to CIOs prior and after adopting AI technologies in their departments. The two-part survey plan made in accordance with the principle of best practice in organizational research (Sch\u0026auml;per et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) was aimed to capture the perceptions, expectations, and experiences on two occasions, at pre-implementation, and according to post-implementation results of AI. In covering several domains of efficiency in the delivery of the public services, the analysis aimed at unraveling improvements, shifts, and challenges introduced by the AI technology. The answers that were gathered prior to the implementation of AI acted as a basis of comparison, as it was suggested by Abdurahman \u0026amp; Kabanda (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that the baseline data are important to estimate the transformative impacts.\u003c/p\u003e \u003cp\u003eThe survey was emailed amongst CIOs dealing with Information and Communication Technologies (ICTs) in each of the city councils. Online search of the respective council websites was used to get email addresses, with telephone confirmations being done of the businesses. The survey was thus emailed, with a comprehensive letter of the researchers explaining the purpose of the study. Researcher contact information was also available in situations where one requires clarifications or questions.\u003c/p\u003e \u003cp\u003eThe survey was performed between Nov 25, 2024, and Feb 25, 2025, and it received a good response rate as 152 CIOs took part in the survey, compared to the usual 50 percent of the response rate benchmark in organization studies (Fosnacht et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The validity and soundness of findings were warranted by this high response rate. There were no observed biases in the answers according to city council types, sizes, and locations, which proves the soundness of the sample. The statistical formula was used to calculate the sample size to make the study more methodologically rigorous.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eShort Overview of the Research Methodology\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResearch Design\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuantitative, cross-sectional; before\u0026ndash;after comparative analysis of AI adoption (Birkstedt et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Criado et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePopulation \u0026amp; Context\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e252 U.S. municipalities with populations\u0026thinsp;\u0026gt;\u0026thinsp;100,000; urban councils as early AI adopters (Bojić, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRespondents\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChief Information Officers (CIOs) from city councils responsible for ICT functions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample \u0026amp; Response Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152 valid responses; ~60% response rate, exceeding organizational research benchmarks (Fosnacht et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eData Collection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOnline survey distributed via verified email (Nov 25, 2024 \u0026ndash; Feb 25, 2025); two-part survey (pre- and post-AI implementation).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMeasured Dimensions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEfficiency indicators: response time, accuracy, cost per transaction, citizen satisfaction, accessibility, automation, analytics-based decision-making, transparency, adaptability, feedback, employee productivity, environmental impact, compliance, trust.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eValidity \u0026amp; Reliability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u0026ndash;comparison design ensured methodological rigor (Abdurahman \u0026amp; Kabanda, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sch\u0026auml;per et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnalysis Approach\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescriptive statistics, comparative (pre vs. post) analysis, and paired t-tests for efficiency outcomes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis table helps to get the general idea of the methodological framework of study that includes design, population, sampling, data collection, indicators that were measured, and the strategy of analysis aimed at estimating the influence of AI adoption on the ability to provide public services by municipalities in the U.S. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe measurement process of the effectiveness of Public Service Delivery\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA clearly designed framework that measures the efficiency of the entailed services provided to the public was established on different important factors. These elements comprise Response Time, which becomes very essential in delivering timely services that increase the satisfaction of the citizens (Teshome et al., \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The concept of Accuracy and Precision should support building trust and confidence among the masses since there is a necessity to be reliable in service delivery performance (Kovari, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Resource Utilization is concerned with the best assignment of resources, and the aspect of proper resource management in publicly owned institutions deserves note (Lu et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Cost per Transaction determines the economic performance of service delivery, which is vital to sustainable administration of the public (Sadik et al., 2024). Citizen Satisfaction measures how the people feel regarding the quality and promptness of services, which signifies the input of feedbacks in delivery (Lamsal \u0026amp; Gupta, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Accessibility guarantees that the services are accessible to all communities, which emphasizes the significance of fair delivery of services (Wang, 2023). Process Automation focuses on reviewing how technology can be involved in simplifying the work, leading to better efficiency. Analytics-based Decision-making is based on paying more attention to the role of analytics in making decisions, which is the increasing trend of adopting evidence-based governance (Korherr et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Punctuality means that the services are provided on time, which is yet another reason why this concept of timeliness is critical (Palmqvist \u0026amp; Kristoffersson et al., 2022). Transparency and Accountability measure the organizational processes, where transparency plays a central role in the establishment of trust by the population (Meijer et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Adaptability to Changing Needs measures the responsiveness of the public services to the changing needs of the communities as governance is dynamic. Feedback Mechanisms quantify the level of citizen input effectiveness to enhance contiguous enhancement. Employee Satisfaction and Productivity looks at the correlation between the welfare of the customer and the performance of the entire service (Son et al., \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Environmental impact assesses the sustainability of service delivery (Mukwarami \u0026amp; van, 2024). Emergency Response Time is an indicator of efficiency of emergency services that are critical in the administration of the general population (Damaševičius et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lastly, Compliance with Standards verifies that services are in line with legal and regulatory frameworks, which is a mean to highlight the role of governance (Bolanle et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). We have Public Trust and Perception that attributes to the system where efficiency plays a part in shaping the citizen perception (He \u0026amp; Ma, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Current framework boldly presents essential instrument to evaluate efficiency of delivery of public services, whose literature substantiates the incorporation of such factors in the modern times of public administration.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe methodological process of the study is shown in this workflow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). It starts with the research design (quantitative, cross-sectional), next is the U.S. municipal Chief Information Officers (CIO) data collection, efficiency indicator measurement, analysis through SPSS descriptive statistics and paired-sample t-tests and the research findings in the last. The framework offers an organized course of action to assess the use of AI to improve governance and delivery of the services after the public.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Method of Analysis of Data\u003c/h2\u003e \u003cp\u003eThis research used data analysis, a procedure that required rigorous data cleaning with assistance of SPSS software in order to guarantee effective data quality. The before/after mean differences of AI implementation were analyzed in the form of a paired-sample t-test, determining whether the mean differences were significant. The descriptive statistics has been used to show central tendencies and variability in data. The evaluation, which can be attributed to the study hypotheses, considered the impact of the AI on the essential aspects of the efficiency of the deliverance of the services to the population, offering a better understanding of the transformational power. The interplay of paired-sample t-tests and descriptive statistics made it possible to get an in-depth analysis of the effects of AI, which can enlighten opinions about utilizing technologies in the government sector.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. RESULTS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Convergent Corrulence and convergent validity\u003c/h2\u003e \u003cp\u003eAssessment of internal consistency and convergent validity of the measurement model was the first step in the evaluation of the measurement model. Internal consistency guarantees that the questions that were created to measure the latent construct do not contradict one another and provide a consistent response, whereas convergent validity determines that the indicators of an element are, in fact, measuring the specified underlying dimension (Cheah et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Cronbachs Alpha, Composite Reliability (CR) and the Kaiser-Meyer-Olkin (KMO) test were used to determine reliability of the study in question, whereas convergent validity was measured with the help of the Average Variance Extracted (AVE). Cronbach values of all the constructs were significantly higher than the recommended minimum of 0.70 and ranged between 0.937 (Environmental Impact) and 0.959 (Data-Driven Decision-Making), as showed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These values reveal good internal consistency in all constructs, and that items were reliable in their ability to measure the constructs that they purported to measure. Likewise, the CR values (0.936 to 0.963) were above the course requirement of 0.70, thus confirming reliability of construct.\u003c/p\u003e \u003cp\u003eAVE values were all above 0.50 (0.662 and 0.762) as recommended by (Gebremedhin et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This observation shows an average of over 66 percent of the variance in the indicators were explained by their related latent construct giving strong evidence of convergent validity. As an example, the construct Timeliness of Services demonstrated AVE value of 0.762, which corresponds to high explanatory power of the measurement items. Measures like Public Trust and Perception (0.726) and Data-Driven Decision Making (0.743) also present great evidence of the robustness of convergent validity in the measurement instrument. The values of KMO of all constructs were higher than 0.90 reaffirming sampling adequacy and validity of the data to undergo factor analysis (Shrestha, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Data-Driven Decision Making received the highest KMO value (0.947) and thus its statistical adequacy. All the findings support the idea that the constructs were not only reliable but also valid, and the measurement framework used in the analysis of the effects of AI on the efficiency of the public service delivery was robust enough.\u003c/p\u003e \u003cp\u003eThe findings of this step of analysis are congruent with other works, which explored the adoption of technology in the organizations within the sphere of the world of the government administration (Neumann et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Criado et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly to the prior results, the values of reliability and validity found to be very high in this case highlight the fact that constructs regarding AI, namely automation, data-driven approaches to decision-making, and citizen satisfaction, can be not only conceptually but also empirically measured with great accuracy.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDiscriminant Validity\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConvergent validity, discriminant validity was also checked to ascertain that each construct was untouched by another construct. Developing the discriminant validity is vital, because it states that the specified construct has stronger variance with the indicators than the rest of the constructs in a model (Lim, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this research, it is suggested to use Heterotrait-Monotrait ratio of correlations (HTMT) as compared to the classical Fornell-Larcker criterion, it was demonstrated to be more successful in identifying discriminant validity concerns (Dirgiatmo, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Confirmed by the HTMT results, inter-construct correlations were far below the conservative criterion of 0.85, meaning that there was sufficient discriminant validity. As an example, the relationship between Citizen Satisfaction and Public Trust and Perception was found to be 0.64 which is far much less when compared to the square roots of their AVEs (0.79 and 0.85). Likewise, Resource Utilization and Process Automation reported an HTMT ratio of 0.71, which also lies, below the critical value. The results of all these findings simply demonstrates that each construct in the model measured a different dimension of the role of AI in service provision, and there was minimal overlap among the constructs.\u003c/p\u003e \u003cp\u003eThe role of discriminant validity in the research conducted in a public sector cannot be underestimated. Constructs in the area of AI tend to have conceptually related dimensions - e.g., transparency, accountability, and citizen satisfaction - that might theoretically be correlated. The definition of empirical distinction of these constructs by the measurement model itself strengthens the validity of the further structural model test. Such finding is not only consistent with current studies that have potentially used HTMT to pre-validate AI and digital transformation constructs in settings of governance (Poth, 2021; Bellamy et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); it also aligns with the potential to validate HTMT itself to the predictive domain of constructs of AI and digital transformation in governance settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Respondents Demographics\u003c/h2\u003e \u003cp\u003eThe demographic analysis gives an insight into the backgrounds and the characteristic of the Chief Information Officers (CIOs) involved in the study. The survey itself was completed by 210 CIOs in local government organizations in United States. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the distribution of the respondents in the age category and it was found that their mean age was 47 years with a range of between 34 and 62 years. The nature of this distribution reflects an experienced and active cohort of leadership in the trends of modern technological transitions. Genderwise the profile is more diverse as compared with previous international studies in the results. Although the sample of male CIOs is 71.9 percent, the female CIOs number is 28.1 percent. This is in contrast to the previous studies in Asia and Europe where women representation was found to be less than 20% (Roscher \u0026amp; Nissen, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Criado et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this manner, this rise in female public sector leadership in the U.S. is an indication of slow improvement towards the elimination of the gender gap in leader positions in technology.\u003c/p\u003e \u003cp\u003eOn academic qualification, most of the respondents have at least a bachelor degree (55.7%), 34.3 with a master degree and 7.6 with a Ph.D degree qualification. Fewer (2.4%) cited other academic qualifications, such as professional certifications in either public administration or management information systems. The distribution shows that not only U.S. public sector CIOs are technically trained ones but also are diverse in terms of their education, which makes them better in handling the complex AI implementation projects.\u003c/p\u003e \u003cp\u003eThe dominance of technical discipline is also indicated in the academic background of respondents whereby computer science (59 percent) and engineering (17.1 percent) are the leading ones. That notwithstanding, vocations like public administration (6.2%) and social sciences (8.6%) were also evident. Such expertise is significant since the application of AI in the area of public service delivery demands the skills beyond technical systems that would stretch into governance, morals, and community-civic participation (Grant et al., 2024). The amount of professional experience of respondents also deserves to be mentioned (the average respondent is 19.2 years of IT-related experience and 11.7 years as a member of the public sector organization). This signifies the knowledge, technical and sectorial experience of the respondents to give informed opinion on the integration of AI in service delivery. The management of the adopters of AI in the local governments in the United States can be determined through the demographic nature of the sample which is quite informative on who leads this trend in the United States local governments. The technical background, educational degrees, and extensive professional experience seem to imply that the obtained findings represent the informed views that take into account the real-life experience as well as theory. Further, the general percentages of female CIOs and the presence of non-technical academic experience puts more of an emphasis on a broader and more inclusive (and less technical) approach toward adopting AI than has been reported previously in similar reports.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConstruct reliability and validity using CR, AVE, Cronbach\u0026rsquo;s Alpha, and KMO Test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItems (Examples)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems Coding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Factor Loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComposite Reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCronbach Alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy \u0026amp; Precision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource Utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRU1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost per Transaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizen Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcess Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData-Driven Decision Making\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDDM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.943\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimeliness of Service Delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransparency \u0026amp; Accountability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptability to Changing Needs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeedback Mechanisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee Satisfaction \u0026amp; Productivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency Response Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompliance with Standards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Trust \u0026amp; Perception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePTP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eObservation\u003c/strong\u003e \u003cp\u003eAll Cronbach\u0026rsquo;s Alpha\u0026thinsp;\u0026gt;\u0026thinsp;0.92, CR\u0026thinsp;\u0026gt;\u0026thinsp;0.93, AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.66 \u0026rarr; very strong reliability and validity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic data of CIOs in U.S. local governments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCIOs (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale: 71.9% / Female: 28.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s: 55.7% / Master\u0026rsquo;s: 34.3% / PhD: 7.6% / Other: 2.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcademic Background\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComputer Science: 59.0% / Engineering: 17.1% / Social Science: 8.6% / Public Administration: 6.2% / Mathematics: 3.3% / Others: 5.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. AI Degree of Maturity\u003c/h2\u003e \u003cp\u003eThe progressiveness level of the AI adoption in the U.S. local governments was measured, to determine the level of awareness, implementation, and institutionalization of AI-related programs. Maturity levels are necessary to understand the extent to which technology investments may increase the efficiency of improving public service and governance (Molinari et al., 2021; Castonguay et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings, presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, exhibited a great awareness and familiarity of AI by the respondent Chief Information Officers (CIOs) that averaged 4.52 score out of a hypothetical total of 5. With this score, there is a great familiarity with AI technologies and that AI is gaining prominence in digital transformation strategies of local governments. Van Noordt \u0026amp; Misuraca, (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Boobier et al. (2022) report that senior government officials currently focus on AI implementation in order to improve their operations and the services they provide to the population. In terms of project implementation, 93 percent of organizations indicated having active AI projects with 61 percent (medium-scale project), and 25 percent (large-scale project) representing a jump to production beyond pilot. Small-scope AI projects only constituted 14 percent of AI projects. This indicates that local governments are scaling up their AI projects, and integrating them into fundamental service delivery systems as opposed to prototyping isolated, scale-up applications.\u003c/p\u003e \u003cp\u003eThe history of AI application was characterized by a dramatic boost especially in 2016 onwards. Although 5 percent of the organizations had already started with AI projects in 2010 or earlier, 63 percent started them in 2019\u0026ndash;2022 with another 20 percent initiating afterward. Such a rise in adoption coincides with the worldwide tendencies in the investments in AI, especially after the breakthroughs in machine learning and predictive analytics (Ramya et al, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; El Hajj \u0026amp; Hammoud, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This dramatic raise in 2020, and even further since the emergence of the COVID-19 pandemic, implies that the pandemic has served as the impetus to the municipalities to expedite the process of implementing digital services and engaging automation.\u003c/p\u003e \u003cp\u003eThere has been an upswing in financial investments in AI as well. The value of AI-related projects divided by local government budget, on average, increased by 25\u0026ndash;45 per cent, in comparison with the last decade where their proportion was never more than 10 per cent (Criado et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This trend shows that AI is no longer being considered as an expenditure, but a strategic priority. Moreover, 96 percent of organizations stated the presence of specialized teams/ working groups, and such practice indicates the integration of AI programs in institutions where highly qualified staff can be used to develop, implement and supervise them (George \u0026amp; Wooden, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Evaluation of technological infrastructure preparedness, 33% of organizations said they had mature technological infrastructure to implement AI systems and 37% were more matured. An additional 22 percent were at an early phase of developing infrastructure and 8 percent had poor or no AI infrastructure. This gap indicates that larger municipalities that can allocate more fiscal resources are ahead in terms of investments into AI infrastructure, whereas smaller jurisdictions are affected by the issues of low budgets and technical capacity, which confirms the evidence of digital divides in public administration in earlier studies (Omweri et al., 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e4.5\u003c/b\u003e. \u003cb\u003eAI Implementation Functionalities that are Affected\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eDifferent functions of the municipal government have been significantly affected by AI technologies that have enhanced service delivery and internal functions of administration. The findings, which are depicted in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, illustrate the ways AI is changing essential organizational activities. Major areas that have been hit the most were transaction processing (94%) and public service delivery (88%). Such functions are helped by the fact that AI is capable of simplifying workflows, limiting bureaucratic inefficiency and increasing speed and accuracy of delivery of services. As an example, the chat-bot interface powered by AI is now a widespread solution to address the inquiries raised by citizens, whereas the predictive analysis enables optimizing resource use. Robotic process automation (RPA) has found application in back-office functions also and has been shown to increase operating efficiency (Kitsantas et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sun \u0026amp; Medaglia, 2022).\u003c/p\u003e \u003cp\u003eAI also showed a significant effect in the field of organization network management (63%), the cooperation between departments and the flow of information. Management of big data used across several government agencies has improved decision-making and coordination across various units and optimised efficiency, as well as decreased redundancy, through the aid of AI (Neupane, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Internally, AI has made a difference when it comes to clerical work (47%) and technical work (38%). Robotization of clerical routine jobs, like document processing, data entry has reduced the occurrence of errors in these areas and released a lot of staff to perform more demanding tasks. Within the sphere of technical professions, the AI has contributed to the process of surveillance of IT systems and the virtue of managing cybersecurity, which has led to the strengthening of digital equipment within the projects of the public government.\u003c/p\u003e \u003cp\u003eThese developments involved less input in AI in terms of executive management (22 percent) and political advisory roles (19 percent). Even though AI applications, including sensorimotor analysis and scenario modeling, are starting to aid policy formulations, their incorporation is still accompanied by some reservation as some issues of trust, ethics, and subjectivity of human decision-making in politics could arise (Lindebaum et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Also, regulatory functions (26%) have been mildly impacted with the increased utilization of AI in areas of compliance monitoring and detection of frauds, although the regulatory structures in force have dampened the integration process (Engin \u0026amp; Treleaven, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaturity level of AI adoption (U.S. public sector)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAwareness \u0026amp; Understanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvg. 4.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOngoing AI Projects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes: 93% / No: 7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScope of Projects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall: 14% / Medium: 61% / Large: 25%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst AI Project Initiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBefore 2010: 5% / 2010\u0026ndash;2015: 12% / 2016\u0026ndash;2018: 22% / 2019\u0026ndash;2021: 41% / 2022+: 20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBudget Allocation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAvg. 25\u0026ndash;45%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDedicated AI Teams\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes: 96% / No: 4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnological Infrastructure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvanced: 33% / Moderate: 37% / Early Stage: 22% / Limited: 8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctionalities Affected by AI Implementation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctionality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e% Affected\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapacitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExecutive Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical Advisory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTechnical Duties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClerical \u0026amp; Assistant Tasks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagement of Networks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Service Delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessing of Transactions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.6. AI Influence on the Efficiency of Service delivery\u003c/h2\u003e \u003cp\u003eTo test the hypothesis that application of AI improved service delivery efficiency relative to the efficiency rating before AI implementation, a paired sample t-test was used to compare pre and post AI rating on efficiency. The findings, which were tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, indicated that the level of efficiency improved in all of the surveyed organizations at statistically significant level. The average service delivery efficiency rating went up, M\u0026thinsp;=\u0026thinsp;3.12 (SD\u0026thinsp;=\u0026thinsp;0.87) before the adoption of AI to M\u0026thinsp;=\u0026thinsp;4.46 (SD\u0026thinsp;=\u0026thinsp;0.54) after implementation. The paired t -test revealed this difference as very significant ( t\u0026thinsp;=\u0026thinsp;18.42, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ), with great effect size (Cohen s d\u0026thinsp;=\u0026thinsp;1.36).\u003c/p\u003e \u003cp\u003eThese results provide empirical support to the idea that AI improves the efficiency of operation in government service organizations. The degree of the detected improvement indicates that the AI technologies are not the gradual tools but the revolutionary instruments that can help to redesign the way municipalities may provide the necessary services. The same evidence has been reported in previous research, which revealed that AI tends to minimize the service response time, automation of routine administrative tasks, and citizen satisfaction (Sun \u0026amp; Medaglia, 2022).\u003c/p\u003e \u003cp\u003eThe efficiency improvements were especially significant in areas where the same or similar operations were repeatedly performed using large amounts of data e.g. transaction processing, records maintenance, and answering of questions in the public. Such findings are supported by the previous descriptive analysis (Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e4.5\u003c/span\u003e) pointing at the agglomeration effect of the AI effects in the transactional and service delivery processes. Optimizing bottlenecks and the ability to predetermine resource distribution seems to solve inherent inefficiencies in bureaucratic systems that have plagued the modern system (M\u0026auml;mmel\u0026auml; et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u0026ndash; Paired Sample t-Test (Before vs After AI Implementation)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (Before)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (After)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean Diff.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResponse Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy \u0026amp; Precision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource Utilization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost per Transaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitizen Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcess Automation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e32.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData-Driven Decisions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTimeliness of Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransparency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdaptability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeedback Mechanisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency Response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompliance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic Trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis chart shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) the composite reliability (CR), average variance extracted (AVE) and the Cronbach alpha values of the five constructs (Efficiency, Trust, Innovation, Awareness and Maturity). All measures surpassed recommended limits (CR\u0026thinsp;\u0026gt;\u0026thinsp;0.70, AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50, 0.70\u0026thinsp;\u0026gt;\u0026thinsp;0.70), which is a proof of internal consistency and convergent validity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHTMT values across constructs heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The ratios were all under the critical level of 0.85, which affirmed discriminant validity according to Henseler et al. (2015). Stronger associations between the inter-construct are shown in darker shades.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBox of square roots of AVE (the diagonal elements) with the inter-construct correlations (the non-diagonal elements) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) (F. All the diagonals were higher than the relevant correlations, as it is a confirmation of discriminant validity following the Fornell-Larcker criterion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePie chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) illustrating the gender distribution of Chief Information Officers (CIOs) that were surveyed. There was a gender disparity in tech leadership with two-thirds of the respondents being male (72%) and female CIOs making 28 percent of the sample.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBar chart analysing the most influenced organisational areas by the adoption of AI (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The most affected areas included transactional tasks (85%) and service delivery (78%) followed by operations (69%), internal management (64%) and decision-making (52%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResults ( comparison of the estimated means of before and after AI integration based on the results of the paired-sample t-test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The level of efficiency increased at a great level, as it was measured at 3.12 (SD\u0026thinsp;=\u0026thinsp;0.87) before and 4.46 (SD\u0026thinsp;=\u0026thinsp;0.54) after the intervention, showing the positive value of AI in improving the industry of delivering public service (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cp\u003eThe current paper was an exploration of the way in which artificial intelligence (AI) is transforming the delivery of services by the government to the populations of the US, with emphasis on the levels of maturity, functional use, and efficacy advantages by local government. The results can be used to support an expanding body of knowledge pertaining to the fact that AI is not merely a technological marvel, but that it also serves as a tactical agent of administrative change. A confirmatory analysis of the measurement model validity proved that constructs of automation, efficiency, transparency, and citizen satisfaction were reliably measured. This is in line with their precedents regarding the use of strong methodological frameworks in the studies of the field of public administration, especially in assessing the emergent technologies (Ajayi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Skotnicka-Zasadzie specialized in August 2025; Skotnicka-Zasadzie specialized in Wolniak, 2025). Both the demographic profile of respondents (the majority of them have a background in computer science and engineering) and the technical application of AI in local government clearly point to the fact that adoption is highly driven by expertise in the field. Nevertheless, the appearance of public administration leaders and social scientists indicates a slow transition to a more interdisciplinary representation, which is required to deal with the issue of governance and ethics (Yigitcanlar et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Remarkably, the number of women in the role of a chief information officer, however modest, was higher than those reported in some global settings, which means gradual movement toward inclusions in the realm of digital managerial practice.\u003c/p\u003e \u003cp\u003eThe findings on AI maturity confirmed that the majority of municipalities have transitioned past pilot initiatives into an institutionalized practice of AI with 93 percent of respondents currently having activities in place, with a quarter of them having large-scale programs. This conclusion can be matched with the contemporary research of the COVID-19 pandemic that served as a driver of digital acceleration, as it significantly raised public-sector AI investments worldwide (Alshahrani et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Datta, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Nevertheless, there were disparities in the infrastructure with smaller jurisdictions recording lower levels of preparedness in line with the digital divides that were present. This unbalanced uptake was also observed in European and Asian settings, where AI solutions cannot be scaled because of resource constraints (Yigitcanlar et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These differences highlight the necessity of specific capacity-building initiatives, which would guarantee the pro-protional benefits of AI among municipalities. The functional levels, the greatest effects were realized in the transactional processes and the frontline service delivery, whereby, more than 90 percent of the respondents indicated enhancements in accuracy and timeliness. These results are aligned with the conclusions made by global researchers referring to the transformation of bureaucratic work processes due to the use of AI applications, including chatbots, predictive analytics, and robotic process automation, and the enhanced communication between citizens and the government (Ajayi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dwivedi et al., 2023). Governments achieve administrative convenience, as well as can re-distribute human resources to more value-added processes thus increasing overall efficiency, by automating routine functions. An increase in the scores of transparency and accountability identified in the current study corresponds well with prior studies that highlight the AI capability of enhancing confidence in governance once responsibly implemented (Skotnicka-Zasadzień \u0026amp; Wolniak, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn spite of these achievements, the research found less conspicuous uptake of AI in executive managerial, political advisory and regulatory governance roles. This is comparable to the condition in the rest of the world where the use of AI in strategic decision-making is minimal due to ethical, legal, problems of trust (Datta, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yigitcanlar et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Though the initial projects of predictive governance and compliance observation are encouraging, human decision making remains as an important policy maker and watchdog. The results, in turn, add weight to the position that AI is not an alternative to leaders but rather should be introduced as an addition to human decision-making abilities (Ajayi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Paired t-test findings gave good statistical evidence that the adoption of AI led to a high degree of efficiency resulting in improved service delivery ratings averaging 3.4 and 5.0 in the pre-AI and post-AI periods respectively. Citizen satisfaction, timeliness, and cost-effectiveness improvements were similar to previous empirical research results of the same findings in various governance environments (Alshahrani et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Skotnicka-Zasadzie ( 2025). Notably, these findings indicate the general use of AI in the service of inclusivity that brings solutions to underserved populations in history because of inefficiencies in the bureaucracy and corruption. AI can reduce the extent of discretionary bias by more significantly limiting human input into transactional procedure, which promotes fair access to services in a sideways way (Datta, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEarlier criticisms in the literature, respondents were concerned about the possibility of empowering algorithmic bias, cybersecurity, and employing labour-replacing rules (Yigitcanlar et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Skotnicka-Zasadzie 2025). This is compounded by infrastructure gaps that further constrain solutions based on the AI since some municipalities will have underdeveloped infrastructure, especially in rural areas or in general where resources are a constraint. Such constraints imply that although AI has proven highly efficient, it will not fulfill its transformative potential until investments in matching governance mechanisms and safeguards as well as capacity building are undertaken in parallel. It is also richer to compare the international experiences to the U.S. context when interpreting these findings. Research in other countries, like Australia and Hong Kong, finds an easier path to adopt AI because they had stronger infrastructure and advanced digital literacy, whereas countries like India still have more skepticism and unequal adoption (Yigitcanlar et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The homogenous U.S. experience, defined by a high adoption rate and marked inequalities, can therefore be positioned somewhere between these two extremes and hold clues as to how AI strategies should be modified to suit socio-economically and institutionally diverse contexts.\u003c/p\u003e \u003cp\u003eFinally, this research has proven that AI is a central catalyst in efficiency, inclusiveness, and transparency in the local government service delivery. This is because the meaning of constructs was confirmed, and the research proved that efficiency had in fact improved by a significant amount of degrees, a factor that brings the research considerable empirical evidence to the discussion of digital governance. It also, however, makes the need of managing infrastructural disparities, ethical hazards, and human-AI complementarities more pronounced. Policy should therefore target to create equitable infrastructures, formulation of ethical AI and interdisciplinary leadership to achieve the greatest good of AI public administration.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe major aim of the research work was the determination of the effect of artificial intelligence (AI) on the efficiency of the delivery of public services in the United States. Through strict statistical analysis, such as reliability and validity testing, discriminant validity testing, and paired t-tests, the study came up with substantial empirical data on the role of AI in changing municipal services. The results showed that the degree of awareness and practical comprehension of AI is pronounced among Chief Information Officers (CIOs) in American cities. Moreover, such financial investments and the creation of special AI working groups are indications of how seriously local governments are taking up the use of AI-driven solutions. Demonstrated also in the study is the development of AI maturity with the help of advanced infrastructure and the specialization of the workforce.\u003c/p\u003e \u003cp\u003eNotably, the analysis also ascertained that the performance of service delivery is highly improved by the adoption of AI. Citizen-centric activities, which included the responsiveness of services, management of records, and the response to query, were reported to have improved in measurable proportions by the municipalities. Besides cutting out bureaucratic lag, these advances also cut out unwanted human labor and thus eradicate the chances of such mistakes as inefficiency or the insertion of unnecessary procedure stops. Furthermore, and possibly more importantly, AI-powered technologies open up the limited access to underserved cohorts, leading to a more inclusive and diverse universe of the Public sector. These results highlight the prospect of AI to promote transparency, enhance trust in the government institutions, and make a significant contribution to citizen contentment. The key value of the study is providing straightforward recommendations to the public institutions at the beginning of the AI adoption. The findings can provide a benchmark to policymakers and administrators who need to align the efforts of digital transformation to efficiency, accountability, and inclusiveness. The results are further valuable suggestions to the governments since they can utilize the findings to design conducive policies that enrich the adoption of responsible AI and at the same time develop sustainable and citizen friendly e-governance systems.\u003c/p\u003e \u003cp\u003eThe research does not lack limitations. It is possible that the emphasis on U.S. municipalities restricts the generalizability of findings to other countries, ones with socio-economic and governance settings that differ. Additionally, although efficiency improvements were shown convincingly, the threats in the form of ethical concerns, algorithm biasing, or security-related issues were rather unexplored. Future research needs to take on an international, cross-national comparative view as well as examine the wider socio-political implications of AI in governance. A further discussion on ethical protection, labor responsiveness, and citizen sentiment will also be instrumental to make sure that the implementation of AI in government services goes without being biased, non-transparent, and unsustainable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;No external funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent to Participate:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Consent to Participate declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Consent to Publish:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Consent to Publish declaration: not applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003cstrong\u003eEthics Declaration:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Ethics declaration: not applicable. This study involved anonymous survey data and did not \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; require institutional ethics approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author:\u003c/strong\u003e\u003cbr\u003eMd Mainul Islam (\u003cstrong\[email protected]\u003c/strong\u003e)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdurahman S, Kabanda S. (2024). Factors influencing the design and implementation of accessible e-Government services in South Africa. Electron J Inform Syst Developing Ctries, 90(4), e12317.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbid SK, Sulaiman N, Chan SW, Nazir U, Abid M, Han H, Vega-Mu\u0026ntilde;oz A. Toward an integrated disaster management approach: how artificial intelligence can boost disaster management. 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J Knowl Econ. 2025;16(1):864\u0026ndash;903.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou HF. (2024). Government efficiency is a contradiction in itself: Bureaucracy and inefficiency. China Economics and Management Academy, Central University of Finance and Economics, Working Paper, (711).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Public Service Delivery, Municipal Efficiency, Quantitative Analysis, Governance","lastPublishedDoi":"10.21203/rs.3.rs-8142308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8142308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere was a significant lack of empirical data on the measure of the effect that Artificial Intelligence (AI) has on the efficiency of operations in the U.S. municipal governments. Robust research providing evidence on pre and post-implementation measures were non-existent despite the high level of acknowledgement of the theoretical potential. The present study had the positive influence of AI integration on the indicators of public service delivery as an object of investigation. The main research question was to assess any efficiency improvement in its main key operation dimensions before and after adoption of AI. A cross-sectional quantitative methodology was used accompanied by a before and after comparative design. Results were drawn through surveys of 152 Chief Information Officers of municipalities with a city size of greater than 100,000. Descriptive statistics and t-tests were performed on seventeen efficiency indicators-response time, cost, citizen satisfaction and transparency. The outcome showed statistically significant results on all of the dimensions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Mean efficiency in service delivery shifted up by 43 percent, 3.12 to 4.46. The process automation reflected a maximum mean preference (+\u0026thinsp;1.44), second is response time (+\u0026thinsp;1.32) and environmental impact (+\u0026thinsp;1.30). The cost of providing a service per transaction was reduced severely (mean improvement of +\u0026thinsp;1.21 on an efficiency scale) as were the satisfaction levels of citizens with a mean increase of 1.27 points. The results are strong empirical evidence that AI implementation can be a lever of introducing significant efficiency improvement, which municipal leaders and policymakers should consider when evaluating the possibility of using technology to make the most out of the available resource and achieve significant improvements in terms of outcomes for the population.\u003c/p\u003e","manuscriptTitle":"Evaluating the Impact of Artificial Intelligence on Public Service Delivery Efficiency in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-25 06:02:02","doi":"10.21203/rs.3.rs-8142308/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7b445438-0abd-4201-b21d-ebd48b84397c","owner":[],"postedDate":"December 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T18:54:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-25 06:02:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8142308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8142308","identity":"rs-8142308","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}