Efficient traffic management in urban areas represents a key challenge for modern cities, particularly in the context of sustainable development and reducing negative environmental impacts. This paper explores the application of artificial intelligence (AI) in optimizing urban traffic through a combination of reinforcement learning (RL) and predictive analytics. The focus is on simulating the traffic network in Belgrade (Serbia, Europe), where RL algorithms, such as Deep Q-Learning and Proximal Policy Optimization, are used for dynamic traffic signal control. The model optimized traffic signal operations at intersections with high traffic volumes using real-time data from IoT sensors, computer vision-enabled cameras, third-party mobile usage data and connected vehicles. In addition, implemented predictive analytics leverage time series models (LSTM, ARIMA) and graph neural networks (GNNs) to anticipate traffic congestion and bottlenecks, enabling initiative-taking decision-making. Special attention is given to challenges such as data transmission delays, system scalability, and ethical implications, with proposed solutions including edge computing and distributed RL models. Results of the simulation demonstrate significant advantages of AI application in 370 traffic signal control devices installed in fixed timing systems and adaptive timing signal systems, including an average reduction in waiting times by 33%, resulting in a 16% decrease in greenhouse gas emissions and improved safety in intersections (measured by an average reduction in the number of traffic accidents). A limitation of this paper is that it does not offer a simulation of the system’s adaptability to temporary traffic surges during mass events or severe weather conditions. The key finding is that integrating AI into an urban traffic network that consists of fixed-timing traffic lights represents a sustainable approach to improving urban quality of life in large cities like Belgrade and achieving smart city objectives.

Serbian SMEs face mounting pressure to stay competitive, agile, and aligned with sustainability goals amid rapid digital change. This mixed-method study—12 qualitative case studies and a survey of 200 firms—examines how AI adoption supports flexible and adaptive strategic transformation. We examine how organizational context and AI readiness translate into the strategic application of AI and, in turn, sustainable development and strategic performance outcomes among Serbian SMEs. Through the AI-Driven Strategic Transformation Framework (AISTF-SME), three adoption types were identified —Traditionalists, Experimenters, and Strategic Adopters—distinguished by digital maturity, strategic integration, and sustainability orientation. While AI is primarily deployed for operational efficiency, firms with higher AI maturity and tighter strategic alignment report stronger gains in agility, innovation, and customer experience; sustainability-oriented use cases remain limited. Key barriers include shortages of technical talent, financial constraints, and insufficient institutional support. We recommend a multi-stakeholder policy approach emphasizing sector-specific AI readiness programs, better access to funding, and stronger university–industry collaboration. The findings enrich digital transformation and sustainability research and offer practical guidance for accelerating AI adoption in transitional economies.

AI technologies have significantly transformed various sectors, including travel and airport operations. This research aims to investigate the implications of implementing artificial intelligence (AI) in airport services on passenger satisfaction and overall travel experiences, as well as how AI technology shapes the way airports support passengers throughout their journey. The study conducted empirical research at Serbian airports with a sample size of 668 passengers. Participants provided their attitudes and satisfaction ratings on 10 statements regarding the impact of AI technology implementation in airport services. The findings from regression analysis reveal a high level of AI implementation in airport services, particularly concerning passenger experience and security. However, passengers express less satisfaction with the implementation of chatbots and virtual assistants, AI algorithms, and certain AI tools that do not meet their expectations. Specifically, passengers feel they do not always receive sufficient instant responses, real-time flight information in the case of delays, or effective issue resolution at the analyzed airports. These results contribute valuable insights to the AI literature and offer implications for future implementations of these technologies in industry, marketing, and technology development. Moreover, they can guide managerial practices and investments in future digital technologies aimed at enhancing passenger experience.

Digitization has led to the emergence of increasingly sophisticated forms of financial fraud, necessitating more advanced and integrated approaches for their rapid detection and prevention. This challenge prompted the authors to examine relevant literature and analyze current policies and measures for detecting financial fraud within the digital environments of organizations, with the aim of enhancing proactive prevention strategies. To this end, an online empirical survey was conducted with 118 executives and managers from Serbia during the first half of 2024, supported by the Association of Employers of Serbia and the Association of Managers. The research focused on the impact of new technologies, particularly AI, on the regulations and organizational policies related to financial fraud detection. Qualitative research, which utilized 12 predefined statements within each impact group using a five-point Likert scale, provided insights into the actual experiences and perspectives of participants concerning financial fraud as a distinct business, social, and economic issue. Multiple correlation approaches were employed to analyze the data. The outcomes suggest that all analyzed factors contribute to addressing financial fraud, with new technologies - especially those based on artificial intelligence - and corporate policies and strategies playing significant roles. Conversely, regulations have a lesser impact, attributed to their correctness, implementation, and enforcement. These findings enhance the understanding of the significance of taking a comprehensive approach to combating fraud, corruption, and financial crime, and highlight the roles of continuous technological advancements, employee digital education, and enhanced communication with the public and investors in building trust and maintaining a company's reputation.

This paper aims to explore the impact of digital transformation on human rights and security protection in the age of modern technologies, as well as to support policymakers in designing a rights-oriented and human-centric digital transformation. This challenge prompted the authors to examine relevant literature and analyze current policies and measures aimed at enhancing proactive strategies. To this end, an online empirical survey was conducted with 132 participants (ages 18-65+) from Serbia during the last quarter of 2024. The research findings support the hypothesis that the relationship between human rights and technological development is highly significant. The results emphasize privacy as the foundation of digital rights, focusing on the ethics of data usage and the protection of individuals' rights to freely express opinions and ideas online. Bridging the digital divide is crucial to ensure that technological advancements benefit all individuals equitably. Promoting access to digital literacy and education is essential for enabling individuals to effectively engage in discussions about these issues in the context of modern technologies. Furthermore, the effective protection of human rights requires coordinated efforts from policymakers, decision-makers, and institutional stakeholders to establish a framework that upholds justice, equality, and dignity in the digital era, as highlighted by the findings of this paper.