Cloud-based Software-as-a-Service (SaaS) solutions are transforming the hospitality sector at a fast rate. They enable delivering services 30% faster and boosting guest satisfaction by 25% by means of real-time personalization and automation. This paper examines how SaaS impacts operational efficiency, guest experience, alignment with ESG, and strategic agility in the hospitality sector. With increasing customer demands and sustainability regulations, hotel brands are moving away from legacy systems to new, subscription-based solutions that are simple to scale and less environmentally unfriendly. This paper demonstrates, via a literature review, comparisons, and actual cases such as HotelKey, CitizenM, and Marriott, how SaaS in hospitality enables agility, digital inclusion, and ethical conduct in a post-pandemic world.

This article presents a comprehensive analysis of artificial intelligence and its impact on various aspects of sustainable development. AI is actively utilized to enhance decision-making, automate processes, and optimize numerous fields of activity. However, its integration into critical domains such as sustainable development, public administration, and cultural innovation raises concerns regarding accessibility, inclusivity, and resource redistribution. The study examines key aspects of artificial intelligence, its classification—including narrow and general AI—and the technologies employed, such as machine learning, natural language processing, and computer vision. Special attention is given to AI’s relationship with sustainable development goals and its role in advancing innovative solutions in social and environmental spheres. The article also explores the ethical, social, and cultural consequences of AI implementation, emphasizing the necessity of developing responsible, transparent, and sustainable systems that align with international standards and ensure long-term societal well-being. This research may be of interest to a broad audience of specialists and researchers engaged in fields related to AI development, its applications, and its influence on societal processes.

Accurate prediction of stock market crashes is a longstanding challenge in financial analysis, with significant implications for investors, regulators, and policymakers. This paper presents a comprehensive review of existing machine learning (ML) approaches used in the prediction of financial crises and market crashes. It evaluates a wide range of techniques—including supervised learning, unsupervised learning, ensemble models, and deep learning—highlighting their strengths, limitations, and performance in various market conditions. Key challenges such as data imbalance, feature selection, temporal dependencies, and the interpretability of predictions are discussed. Building on the insights from existing literature, the paper proposes a novel hybrid framework that integrates multi-source financial indicators, sentiment analysis, and time-series modeling to enhance crash prediction accuracy and reliability. The study contributes to the growing field of AI-driven financial forecasting and provides a foundation for future research on robust early warning systems.

This article examines the integration of autonomous vertical delivery systems (AVDS) for high-rise buildings in cities, while discussing the last-mile challenges that plague traditional elevator systems, which increase operational costs and CO₂ emissions. It is precisely due to this e-commerce and online food delivery boom that the study becomes relevant; modern times see up to 85% of a courier’s delivery cycle time as vertical movement inside a building—skyscraper situation kills all benefits brought by digitally optimized routing; enormous economic and reputational losses happen to both developers and delivery services. The novelty of this research lies in proposing full automation of vertical delivery based on a patented gateway for receiving couriers’ carts, a modular silent conveyor, and integrated microlockers on each floor, eliminating elevator frustration and ensuring end-to-end digital parcel tracking. The solution is easily scalable to any high-rise parameters and integrates with the IT infrastructure of major delivery operators. Following AVDS deployment, the courier time inside the building is reduced to less than one minute, empty elevator trips decrease by one-third, and the building’s carbon footprint is reduced by 3–4%. Econometric analysis reveals an 8.6% increase in tenants’ lease-renewal willingness and a 2.43 percentage point reduction in vacancy, while apartment values rise by USD 4,000–6,000 due to improved Environmental, Social, and Governance (ESG) metrics. This article will help developers, property managers, and logistics operators who want to cut costs and boost service quality in tall building projects.

The integration of Artificial Intelligence in Business Intelligence systems has fundamentally transformed enterprise analytics capabilities, enabling sophisticated pattern recognition, predictive modeling, and automated decision-making processes. However, the opaque nature of many AI algorithms presents significant challenges in business contexts where transparency, accountability, and regulatory compliance remain paramount concerns. This comprehensive technical review examines the role of Explainable AI in addressing these critical challenges, providing detailed insights into current methodologies, implementation frameworks, and practical applications across enterprise analytics environments. The content explores theoretical foundations distinguishing interpretability from explainability, emphasizing their crucial roles for different stakeholder groups within organizations. Technical frameworks encompass model-agnostic and model-specific methods, including LIME, SHAP, and attention mechanisms, alongside implementation tools ranging from open-source libraries to enterprise platforms. Real-world applications demonstrate XAI effectiveness across financial services, healthcare, retail, manufacturing, and human resources sectors, highlighting regulatory compliance benefits and stakeholder trust improvements. Current challenges include computational complexity, explanation fidelity, multi-modal data integration, and scalability issues, while emerging trends focus on automated explanation generation, interactive interfaces, and causal reasoning methods. Regulatory and ethical considerations address compliance evolution, bias detection, and fairness metrics, while technical advancements explore foundation model interpretability and privacy-preserving techniques.