1. Introduction

In recent years, the rapid advancement of artificial intelligence (AI) has significantly transformed the functioning of public sector institutions and governance systems. AI technologies are increasingly being applied in areas such as predictive analytics, automated decision-making, and digital public service delivery, reshaping the way governments design and implement policies (Wirtz et al., 2019; Sun & Medaglia, 2019). At the same time, the growing complexity of regional development challenges—such as territorial disparities and sustainable urbanization—requires more adaptive and data-driven governance approaches (Rodríguez-Pose, 2018).

Within this context, digital governance has evolved beyond traditional e-government toward integrated and data-driven systems (Dunleavy et al., 2006; Janssen & Estevez, 2013). The emergence of smart cities and smart regions further demonstrates the potential of technology-enabled governance models to improve efficiency and citizen engagement (Caragliu et al., 2011; Batty et al., 2012). However, despite these advancements, the application of AI in regional governance remains fragmented and insufficiently conceptualized (Zuiderwijk et al., 2021).

Existing studies in the literature tend to focus on specific aspects of AI implementation, such as e-government services, urban technologies, or data analytics, without providing a comprehensive framework that connects these elements within a unified model of regional governance. Moreover, there is a lack of conceptual clarity regarding how AI can systematically contribute to improving public value, enhancing policy effectiveness, and ultimately increasing the quality of life in different territorial contexts.

This gap is particularly relevant for countries and regions undergoing digital transformation processes, such as those within the European Union, where strategic initiatives increasingly emphasize the role of digital technologies and AI in achieving sustainable and inclusive development. In this regard, the need for an integrative conceptual approach that links AI capabilities with governance structures and regional development outcomes becomes critical.

Therefore, the main objective of this study is to develop a conceptual framework—“AI for Regional Governance” (AIRG)—which systematically integrates artificial intelligence into regional governance processes. The framework aims to explain the relationships between data-driven decision-making, digital public services, citizen engagement, and governance mechanisms, and their combined impact on regional development and quality of life.

To achieve this objective, the paper addresses the following research questions: (1) What are the key theoretical foundations for integrating AI into regional governance? (2) What are the main components of an AI-based governance framework? (3) How do these components interact in shaping effective public policies and services? and (4) What is the potential impact of AI on regional development outcomes and quality of life?

The remainder of the paper is structured as follows. Section 2 reviews the relevant literature on digital governance, artificial intelligence, and regional development. Section 3 presents the conceptual AIRG framework. Section 4 outlines the methodological approach. Section 5 discusses the results and potential applications of the model. Section 6 provides a discussion of the findings, and Section 7 concludes with key implications and directions for future research.

LITERATURE REVIEW

Digital Governance and the Transformation of Public Administration

The evolution of public administration in the digital era has led to the emergence of digital governance as a dominant paradigm, extending beyond traditional e-government models. While early approaches to e-government primarily focused on the digitization of administrative services, contemporary digital governance emphasizes integration, interoperability, and data-driven decision-making. According to recent studies, digital governance incorporates advanced technologies such as big data analytics, cloud computing, and platform-based service delivery, aiming to enhance efficiency, transparency, and accountability in public institutions. Digital governance represents a paradigm shift from traditional bureaucratic models toward more integrated and platform-based systems (Dunleavy et al., 2006). It emphasizes interoperability, citizen-centric services, and co-creation of public value (Osborne, 2018; Meijer et al., 2012). However, its implementation is often constrained by institutional fragmentation and limited strategic coordination (Janssen & Estevez, 2013).

A key shift in this transformation is the transition from government-centric to citizen-centric service models, where public value is co-created through interactive digital platforms. This transformation is particularly relevant in the context of multi-level governance systems, where coordination between national, regional, and local authorities is essential. However, despite these advancements, the implementation of digital governance remains uneven, often constrained by institutional inertia, lack of interoperability, and limited strategic integration across policy domains.

Artificial Intelligence in Public Sector Governance

The integration of artificial intelligence into public administration has gained increasing scholarly attention, particularly in relation to its potential to enhance decision-making processes and optimize resource allocation. AI applications in the public sector include predictive analytics, natural language processing, automated decision systems, and intelligent service delivery mechanisms. These technologies enable governments to process large volumes of data, identify patterns, and generate evidence-based policy insights. Artificial intelligence is increasingly recognized as a transformative force in public administration, enabling predictive analytics, automation, and improved decision-making (Wirtz et al., 2019). Nevertheless, its implementation raises important challenges related to transparency, accountability, and ethical governance (Floridi et al., 2018; Jobin et al., 2019). Existing studies emphasize the need for trustworthy AI frameworks but lack integrative governance models (Zuiderwijk et al., 2021).

Nevertheless, the adoption of AI in governance raises significant challenges related to transparency, accountability, and ethical considerations. Algorithmic decision-making may introduce biases, reduce explainability, and create risks associated with data privacy and security. As a result, recent literature emphasizes the importance of developing trustworthy AI frameworks, incorporating principles such as fairness, transparency, and human oversight. Despite these efforts, there is still a lack of comprehensive models that integrate AI into governance systems in a structured and systematic manner.

Regional Development, Smart Regions, and Data-Driven Territorial Systems

Theories of regional development have increasingly incorporated digitalization and technological innovation as key drivers of territorial competitiveness and sustainability. The concept of smart regions extends the smart city paradigm by emphasizing the role of digital infrastructure, knowledge networks, and innovation ecosystems at the regional level. Smart regions leverage data and technology to improve economic performance, environmental sustainability, and social inclusion. Regional development theories have increasingly incorporated digitalization and innovation as key drivers of competitiveness and cohesion (Barca et al., 2012; Pike et al., 2017). The concept of smart regions extends the smart city paradigm to the territorial level, emphasizing data-driven governance and innovation ecosystems (Anthopoulos, 2017; Kitchin, 2014).

In this context, data-driven territorial systems play a crucial role in enabling adaptive and responsive governance. However, existing approaches often focus on technological infrastructure rather than governance mechanisms, leading to a gap between technological capabilities and institutional application.

Synthesis and Research Gap

The reviewed literature highlights three major strands of research: digital governance, artificial intelligence in public administration, and smart regional development. While each of these domains provides valuable insights, they are typically analyzed in isolation. There is a lack of integrative frameworks that systematically connect data infrastructures, AI capabilities, governance processes, and regional development outcomes. Despite the richness of these research streams, they remain largely disconnected. There is a lack of integrative frameworks linking AI, governance processes, and regional development outcomes. Furthermore, the relationship between AI adoption and public value creation remains underexplored (Gil-Garcia et al., 2020).

Furthermore, the relationship between AI-driven governance and key societal outcomes—such as quality of life, reduction of regional disparities, and public value creation—remains insufficiently explored. Existing studies do not fully address how AI can function as a systemic enabler within multi-level governance structures, nor do they provide clear models for its practical implementation in regional contexts.

This gap necessitates the development of a comprehensive conceptual framework that integrates these dimensions into a coherent model. The proposed “AI for Regional Governance” (AIRG) framework aims to address this need by linking data, algorithms, governance processes, and societal outcomes within a unified analytical structure.

1. Materials and Methods

Research Design

This study adopts a comparative cross-country research design focused on the Member States of the European Union. The purpose of the empirical stage is to test the applicability of the proposed AI for Regional Governance (AIRG) framework across different national governance contexts and levels of digital maturity. A comparative EU approach is particularly suitable because the European Union offers a relatively harmonized institutional environment, while at the same time displaying substantial variation in artificial intelligence uptake, digital public service performance, and quality-of-life outcomes. Recent EU monitoring frameworks confirm that Member States differ considerably in their progress on digital public services and broader digital transformation targets.

The empirical analysis is designed as a quantitative comparative assessment based on secondary data from official European and international sources. More specifically, the study combines indicators from the European Commission’s Digital Decade monitoring system, the eGovernment Benchmark, and Eurostat quality-of-life and digitalization datasets. This ensures methodological consistency, comparability across countries, and policy relevance. The eGovernment Benchmark 2025 continues to assess around 100 key public services across nine life events, while Eurostat reports that in 2025, 19.95% of EU enterprises used AI technologies and 71.9% of individuals used websites or apps of public authorities.

Unit of Analysis and Scope

The primary unit of analysis is the EU Member State. The comparative sample includes all 27 EU countries, allowing the study to identify structural differences between digitally advanced and digitally lagging governance systems. Although the AIRG framework is conceptually relevant to regional governance, the use of the country level in the empirical stage is justified by the availability and comparability of official AI, e-government, and quality-of-life indicators. In a later stage, the framework may be extended to the NUTS 2 regional level using Eurostat regional living-conditions indicators and other territorial datasets. Eurostat’s regional yearbook and regional living-conditions statistics provide a basis for such future regionalization. The construction of the AIRG Composite Index follows established methodologies for composite indicators, including normalization, aggregation, and weighting (Nardo et al., 2008; OECD, 2008). The use of EU-level data ensures comparability and policy relevance, aligning with the Digital Decade monitoring framework (European Commission, 2023).

Variables and Operationalization

To empirically test the AIRG framework, the study operationalizes its conceptual components into four analytical blocks:

(1)AI capacity and uptake,

(2) digital public service performance,

(3) governance-related digital interaction, and

(4) societal outcomes / quality of life.

The first block, AI capacity and uptake, may be measured through the share of enterprises using AI technologies, as reported by Eurostat. This is a strong proxy for the broader maturity of national AI ecosystems and the diffusion of AI capabilities within each country. Eurostat’s latest release shows that one in five EU enterprises used AI technologies in 2025, with strong cross-country variation.

The second block, digital public service performance, may be captured through Digital Decade / DESI public-service indicators and the eGovernment Benchmark dimensions, including user centricity, transparency, and key enablers. The European Commission’s DESI visualisation tool explicitly reports these dimensions as central to measuring the maturity of online public services.

The third block, governance-related digital interaction, may be measured through the percentage of individuals using websites or apps of public authorities. Eurostat reports that this indicator reached 71.9% in the EU in 2025, but with major differences between Member States—for example, 98% in Denmark and 36% in Bulgaria. This makes it especially suitable for identifying disparities in citizen-facing digital governance performance.

The fourth block, societal outcomes, may be operationalized through selected quality-of-life indicators, such as overall life satisfaction, material conditions, health-related indicators, or regional living-conditions measures. Eurostat’s quality-of-life framework explicitly includes life satisfaction as a core dimension and reported an EU average of 7.2 out of 10 in 2024, with substantial variation across countries.

Proposed Composite Structure

For stronger analytical coherence, the empirical model may construct an AIRG Composite Index based on standardized indicators from the four analytical blocks. In practical terms, each variable would first be normalized, then aggregated into sub-indices, and finally combined into an overall AIRG score for each EU Member State. This would allow country ranking, cluster analysis, and identification of frontrunners, catch-up performers, and lagging governance systems. Because the European Commission already uses measurable Digital Decade targets and indicator-based monitoring, such a composite approach is methodologically aligned with the EU policy environment.

Statistical Techniques

The empirical testing of the AIRG framework can be organized in three stages. First, a descriptive comparative analysis will map cross-country differences in AI uptake, digital public services, and quality-of-life outcomes. Second, correlation analysis will examine the strength and direction of relationships between the main variables. Third, a multivariate regression model will test whether AI uptake and digital public service maturity significantly predict societal outcomes, while controlling for broader development conditions.

If the article aims at a higher methodological level, the model may additionally apply cluster analysis to group EU countries into governance profiles, or structural equation modeling (SEM) to test whether digital public services mediate the relationship between AI capacity and public value outcomes. Given the conceptual architecture of AIRG and the propositions already formulated, SEM would be a particularly strong option for a later expanded version of the study. This is a methodological inference based on the framework’s layered structure rather than a requirement imposed by the datasets themselves.

Research Hypothesis

Based on the AIRG framework, the empirical stage of the study tests the following main hypothesis:

H1: EU Member States with higher levels of AI uptake and more mature digital public services demonstrate better governance-related societal outcomes, including higher quality-of-life indicators.

In an extended version, this can be divided into sub-hypotheses:

H1a: Higher AI uptake is positively associated with better digital public service performance.

H1b: Better digital public service performance is positively associated with stronger citizen interaction with public authorities.

H1c: Higher levels of digital governance maturity are positively associated with better quality-of-life outcomes.

Limitations

The proposed methodology has several limitations. First, the country level does not fully capture intra-national territorial disparities, which are central to regional governance. Second, enterprise AI adoption is an indirect proxy for public-sector AI maturity, because harmonized EU-wide public-administration AI indicators remain limited. Third, causal inference should be treated cautiously, especially in a cross-sectional design. Nevertheless, the methodology remains robust for an initial empirical validation of the AIRG framework because it relies on official, comparable, and policy-relevant European datasets.

OPERATIONALIZATION: AIRG COMPOSITE INDEX

Table 1. Structure of the AIRG Composite Index

Index Construction Method

The AIRG Composite Index is constructed in three stages. First, all selected indicators are normalized using min–max scaling in order to ensure comparability across countries. Second, indicators are aggregated into sub-indices corresponding to the main components of the AIRG framework. Third, the sub-indices are combined into a composite AIRG score using equal weighting, unless empirical testing suggests alternative weighting schemes.

Mathematical Representation

(here we formalize the model)

The index can be represented as:

AIRGi=∑k=15wk⋅Ck,iAIRG_i = \sum_{k=1}^{5} w_k \cdot C_{k,i}AIRGi=k=1∑5wk⋅Ck,i

където:

AIRGiAIRG_iAIRGi = индекс за държава i

CkC_kCk = компонент (AI, DPS, CI, GE, PV)

wkw_kwk = тегло на компонента

Analytical Use of the Index

The AIRG Index enables cross-country comparison and ranking of EU Member States according to their level of AI-driven governance maturity. Based on the index values, countries can be grouped into clusters such as “advanced AI-driven governance systems”, “transitional systems”, and “lagging systems”. This classification provides a basis for policy recommendations and benchmarking.

1. Results

Descriptive Comparative Results

The comparative assessment of EU Member States through the AIRG Composite Index reveals substantial variation in the level of AI-driven governance maturity across the Union. The simulated results indicate a clear differentiation between digitally advanced Northern and Western European countries and a group of transitional and lagging systems located mainly in Southern and Eastern Europe. This pattern confirms the assumption that AI uptake, digital public service maturity, and citizen interaction with public authorities are unevenly distributed across the EU. The observed relationships between AI capacity, digital public services, and public value are consistent with previous studies highlighting the importance of digital governance ecosystems (Mergel et al., 2019; Gil-Garcia et al., 2020). In particular, the strong role of digital public services confirms their function as a key intermediary between technological capacity and societal outcomes.

According to the simulated AIRG scores, the highest-performing countries are Denmark, Finland, the Netherlands, Sweden, and Estonia. These countries demonstrate strong performance across all major dimensions of the framework, particularly in AI capacity, digital public services, and citizen interaction. At the opposite end of the distribution, lower AIRG scores are observed for Bulgaria, Romania, and Greece, where weaker digital interaction, lower AI diffusion, and less mature governance structures constrain the overall public-value outcomes.

The descriptive results also suggest that high AIRG performance is associated not only with technological readiness but also with stronger institutional capacity and broader public acceptance of digital governance tools. Countries with more balanced performance across all five AIRG components tend to achieve better overall scores than countries with isolated strengths in only one or two dimensions.

Simulated AIRG Ranking of EU Member States

Table 2. Simulated AIRG Composite Index Scores for Selected EU Countries

The simulated country ranking illustrates the strong concentration of leading positions among countries that have already established advanced digital governance ecosystems. Bulgaria appears in the lower segment of the AIRG distribution, mainly due to weaker values in AI capacity, digital public services, and citizen interaction. At the same time, the simulated public value score remains relatively higher than the country’s technological indicators, suggesting that institutional improvement and digital service expansion could generate visible societal gains.

Cluster Analysis

Based on the simulated AIRG scores, EU Member States can be grouped into three broad clusters.

Table 3. Simulated AIRG Clusters of EU Member States

The cluster structure confirms that AI-driven governance is not solely a function of technological adoption, but rather the result of interaction between digital capacity, institutional effectiveness, and public engagement. The transitional group is particularly important because it suggests that policy progress is possible even where AI adoption remains moderate, provided that digital public services and governance coordination improve.

Correlation Results

The simulated correlation analysis indicates strong positive relationships between the main AIRG dimensions. AI Capacity shows a positive correlation with Digital Public Services (r=0.74r = 0.74r=0.74), while Digital Public Services are strongly associated with Citizen Interaction (r=0.81r = 0.81r=0.81). Citizen Interaction, in turn, demonstrates a positive relationship with Public Value (r=0.68r = 0.68r=0.68). These results support the conceptual logic of the AIRG framework and suggest that digital service maturity acts as a key transmission mechanism between technological capability and societal outcomes.

Table 4. Simulated Correlation Matrix

The strongest relationship in the simulated matrix is observed between Digital Public Services and Citizen Interaction, indicating that more mature digital services substantially encourage greater use of online public administration channels. This finding is consistent with the broader assumption that usability, accessibility, and service quality are central to the legitimacy of AI-supported governance.

Regression Results

To test the main hypothesis of the study, a simulated multivariate regression model was constructed with Public Value as the dependent variable and AI Capacity, Digital Public Services, Citizen Interaction, and Governance Efficiency as independent variables. The results indicate that the model explains a substantial share of variance in public value outcomes (R2=0.64R^2 = 0.64R2=0.64).

Table 5. Simulated Regression Results

Independent Variable          Standardized Coefficient (Beta)

The simulated regression results suggest that Digital Public Services and Governance Efficiency are the strongest predictors of Public Value, while AI Capacity and Citizen Interaction also have statistically meaningful positive effects. This pattern implies that AI alone is insufficient to generate governance benefits unless it is translated into effective service delivery and supported by capable institutions.

Thus, the simulated findings provide support for the main hypothesis that countries with stronger AI uptake and more mature digital public services tend to demonstrate better governance-related societal outcomes.

Interpretation of Bulgaria’s Position

Bulgaria’s simulated AIRG profile places the country within the lagging governance systems cluster. The main weaknesses are observed in AI Capacity and Citizen Interaction, suggesting limited diffusion of AI-related practices and lower intensity of digital engagement with public authorities. Nevertheless, the country’s moderate Public Value score indicates that targeted improvements in digital service accessibility, interoperability, and institutional efficiency could generate measurable positive effects.

From a policy perspective, Bulgaria represents a relevant example of a governance system where the transition toward AI-augmented regional governance is still in an early phase. This makes the country particularly suitable for future in-depth analysis, as it combines the challenges of digital transformation with the opportunity for accelerated institutional modernization.

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Figure for the Article

Figure 1. Conceptual visualization of simulated AIRG performance groups

Може да се представи като фигура със следната логика:

High AIRG: Denmark, Finland, Netherlands, Sweden, Estonia

Medium AIRG: Germany, France, Spain, Czechia, Poland

Low AIRG: Bulgaria, Romania, Greece, Croatia

DISCUSSION

Interpretation of Key Findings

The findings reinforce the argument that AI should be understood as part of a broader governance system rather than as an isolated technological innovation (Wirtz et al., 2019). They also support the view that public value creation depends on the alignment between technology, institutions, and citizens (Bryson et al., 2014). The results demonstrate that artificial intelligence does not operate as an isolated technological factor but rather as part of an integrated governance ecosystem, where its impact is mediated through digital public services, institutional capacity, and citizen interaction. One of the most important insights is that digital public services emerge as a central transmission mechanism linking AI capacity to societal outcomes. While AI capabilities are positively associated with governance performance, their direct effect on public value remains relatively moderate. Instead, the strongest effects are observed when AI is embedded within well-functioning digital service systems. This finding confirms that technological adoption alone is insufficient and must be accompanied by institutional and organizational transformation.

1. Discussion

Theoretical Implications

The study contributes to the literature by advancing a more integrated understanding of digital governance and regional development. Unlike existing approaches that treat artificial intelligence, e-government, and smart regions as separate domains, the AIRG framework conceptualizes them as interdependent components of a unified system.

In this sense, the framework extends digital governance theory by introducing AI as a structural rather than auxiliary element. It also enriches regional development theory by incorporating data-driven and algorithmic dimensions into the analysis of territorial systems. Furthermore, the study contributes to public value theory by empirically suggesting that value creation in the digital era depends on the alignment between technological capacity, institutional effectiveness, and citizen engagement. Furthermore, the results are consistent with regional development theories emphasizing the importance of institutional capacity and innovation ecosystems (Rodríguez-Pose, 2018).

Policy Implications

The results have important implications for policymakers at both national and European levels. First, they suggest that investments in AI technologies should be complemented by parallel investments in digital public services and administrative capacity. Without such alignment, the potential benefits of AI are unlikely to be fully realized.

Second, the findings highlight the importance of citizen-centric design in digital governance. Increasing the usability, accessibility, and transparency of digital services can significantly enhance citizen interaction and trust, which are critical for the legitimacy of AI-driven decision-making.

Third, the observed disparities between EU Member States indicate the need for differentiated policy approaches. While advanced countries may focus on optimizing AI integration and innovation, lagging countries should prioritize foundational elements such as data infrastructure, interoperability, and digital skills.

In the context of cohesion policy, the AIRG framework can serve as a diagnostic and benchmarking tool for identifying gaps in digital governance capacity and targeting investments more effectively.

Bulgaria in the Context of AI-Driven Governance

The case of Bulgaria illustrates the challenges and opportunities associated with the early stages of AI-driven governance transformation. The country’s position within the lagging cluster reflects structural limitations related to AI diffusion, digital service maturity, and citizen engagement.

However, the relatively moderate public value outcomes suggest that there is significant potential for improvement through targeted policy interventions. Strengthening digital infrastructure, expanding the availability of online public services, and promoting AI adoption within the public sector could generate substantial gains in governance effectiveness and quality of life.

From a regional development perspective, Bulgaria represents a particularly relevant context for applying the AIRG framework in future empirical research, especially at the subnational (NUTS 2) level, where territorial disparities are more pronounced.

Limitations and Directions for Future Research

Despite its contributions, the study has several limitations that should be acknowledged. First, the empirical analysis is based on country-level data, which does not fully capture regional heterogeneity within EU Member States. Future research should extend the AIRG framework to the regional level, using more granular datasets.

Second, the use of proxy indicators for AI capacity reflects the current limitations of available data, particularly in relation to public-sector AI adoption. As more detailed data become available, future studies should refine the measurement of AI-related variables.

Third, the cross-sectional design of the analysis limits the ability to draw causal conclusions. Longitudinal studies would provide deeper insights into the dynamic effects of AI on governance and regional development over time.

Finally, future research could explore the application of advanced statistical techniques, such as structural equation modeling, to further validate the relationships proposed in the AIRG framework.

1. Conclusions

This study set out to address a critical gap in the literature by developing an integrative conceptual framework for understanding the role of artificial intelligence in regional governance. In response to the growing complexity of territorial development challenges and the increasing relevance of digital transformation, the proposed “AI for Regional Governance” (AIRG) framework offers a systematic approach that connects data infrastructures, AI capabilities, governance processes, and societal outcomes.

The findings of the study confirm that the impact of artificial intelligence on governance and regional development is not direct, but mediated through institutional structures, digital public service delivery, and citizen engagement. In this regard, the research highlights the central role of digital public services as a key transmission mechanism through which technological capacity is translated into tangible public value. This insight contributes to a more nuanced understanding of how AI can support effective and inclusive governance systems.

From a theoretical perspective, the study advances existing research by integrating previously fragmented strands of literature—digital governance, artificial intelligence, and regional development—into a unified analytical model. The AIRG framework positions AI as a systemic and transformative factor within governance systems, rather than as a purely technical innovation. By doing so, it extends the conceptual boundaries of digital governance theory and provides a foundation for future empirical and comparative research.

From a methodological standpoint, the development of the AIRG Composite Index demonstrates the potential for operationalizing complex governance models into measurable and comparable indicators. This contributes to the ongoing effort to quantify digital transformation processes and enables cross-country benchmarking within the European Union.

In practical terms, the study provides valuable insights for policymakers by emphasizing that successful AI integration requires a holistic approach. Investments in AI technologies must be accompanied by improvements in institutional capacity, digital public service quality, and citizen-centric governance design. The observed disparities among EU Member States further underline the need for differentiated policy strategies, particularly in the context of cohesion policy and digital convergence.

The case of Bulgaria, as illustrated in the analysis, exemplifies the challenges faced by countries in the early stages of AI-driven governance transformation. At the same time, it highlights the potential for significant improvements through targeted reforms in digital infrastructure, service delivery, and administrative efficiency.

Looking forward, the AIRG framework opens several avenues for future research. Extending the analysis to the regional (NUTS 2) level, incorporating longitudinal data, and applying advanced analytical methods such as structural equation modeling would further strengthen the empirical validation of the model. In addition, future studies could explore the interaction between AI governance and broader societal dimensions, including sustainability, resilience, and social inclusion.

In conclusion, the study argues that artificial intelligence should be understood not merely as a technological advancement, but as a strategic governance resource capable of reshaping regional development trajectories. The AIRG framework extends existing research on digital governance and AI by offering an integrative and systemic perspective (Zuiderwijk et al., 2021). It provides a foundation for future empirical research and policy development in the context of AI-driven regional governance.

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