The functioning of modern urban environments relies heavily on the public transport system. Given spatial, economic, and sustainability criteria, public transport in larger urban areas is unrivaled. The system’s role depends on the quality of service it offers. Achieving the desired service quality requires a design that meets transport demands. This paper uses a data-driven approach to address headway deviations in public transport lines and explores ways to improve regularity during the design phase. Headway is a critical dynamic element for transport organization and passenger quality. Deviations between planned and actual headways represent disturbances. On lines with headways under 15 min, passengers typically do not consult schedules, making punctuality less crucial. Reduced headway regularity affects the average travel time, travel time uncertainty, and passenger comfort. Ideally, the public transport system operates with regular headways. However, disturbances can spread and affect subsequent departures, leading to vehicle bunching. While previous research focused on single primary disturbances, this study, with the help of AI (reinforcement learning), examines multiple primary disturbances in the cities of Belgrade, Novi Sad, and Niš. The goal is to model the cumulative impact of these disturbances on vehicle movement. By ranking parameter influences and using the automatic optimization of static line elements, this research aims to improve headway regularity and increase system resilience to disturbances. The results of this research could also be useful in developing adaptive public transport management systems that leverage AI and IoT technologies to continuously optimize headway regularity in response to real-time data, ultimately enhancing service quality and passenger satisfaction.

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.

This research paper analyzes low productivity of company workforce as the key risk factor to business sustainability of a company, with special attention to small and medium-sized companies. The scope of research involves for-profit, and small and medium-sized companies doing business in Serbia. This paper explores whether poor human resource management, and, more particularly, low productivity of employees represent one of the key reasons for poor business performance and eventually failure of the company. The quantitative part of this research was conducted during 2022, by analyzing 468 small and medium-sized companies with less than 250 employees, who ended their business during 2021. Current theory determines human resource management in companies, the influence of different sorts on overall business performance, and employee productivity and its causal effects on business survival. This study attempts to cover identified literature gaps by analyzing the risk influences of low productivity on the failure of small and medium-sized companies, with special diversification between manufacturing and service-oriented companies. Key findings from this research were defined with the help of statistical learning methods, suggesting direct correlation of low productivity in the case of small services-oriented businesses and medium-sized manufacturing companies. Future work on this research includes extension of the analysis to other countries in the West Balkans region and the comparation of all determined causalities.

The digitalization of Human Resources (HR) is rapidly transforming traditional workforce management practices, with artificial intelligence (AI) at the forefront of this evolution. This paper examines how AI-driven technologies are reshaping key HR functions, including talent acquisition, employee engagement, workforce planning, and performance management. AI-powered tools, such as predictive analytics, natural language processing, and machine learning algorithms, enable organizations to streamline processes, enhance decision-making, and improve employee experiences. For instance, AI simplifies candidate screening by analyzing resumes more efficiently, while chatbots provide real-time support to employees and candidates, fostering timely and personalized communication. Moreover, the integration of AI in workforce planning allows organizations to predict trends, optimize resource allocation, and align talent strategies with business objectives. The case study presented in this paper highlights the potential of AI to foster diversity and inclusion by mitigating biases in recruitment and evaluation processes. However, this paper addresses the ethical and practical challenges associated with AI implementation, such as data privacy, algorithmic transparency, and workforce upskilling. By bridging the gap between technological advancement and human-centric HR practices, this research underscores the transformative potential of AI in creating more agile, efficient, and inclusive workplaces. The findings aim to provide actionable insights for HR professionals navigating the digital transformation journey.