Roman Reznikov

An energy sector is one of the pivotal industries of an economy. Access to electricity is a basic need for citizens, as well as for all spheres of economic activity and it is a necessity for the successful functioning of many types of enterprises. Companies, which are a part of a national energy infrastructure, have strategical importance at a governmental level. In this paper it was offered the definition of non-optimal/suboptimal costs for the companies of the Ukrainian energy sector. There were reviewed models for assessing the efficiency of the functioning of companies and their applicability for non-optimal costs identification, considering the Ukrainian energy sector context. Also, in was conducted an analysis of approaches to the financial diagnostics of enterprises. It was defined that high inflation does not allow full use of some of the approaches to find suboptimal costs. It is proved that without auxiliary analysis, none of the models considered corresponds to the busine...

In the context of ongoing global crises, such as geopolitical tensions, economic disruptions, and public health emergencies, businesses are compelled to devise robust strategies to ensure their survival and growth. This article delves into the adaptive measures and strategies businesses employ in the face of crises like the Russian invasion of Ukraine, the global COVID-19 pandemic, and the persistent conflicts experienced by Israel. By integrating insights from empirical research and diverse case studies, the article underscores the critical role of agility, technological innovation, and customer-centricity in navigating complex crisis scenarios. The discussion is structured around the three key phases of crisis management-survival, recovery, and renewal. During the survival phase, businesses focus on immediate cost-cutting and resource preservation to withstand the initial shock. As they move into the recovery phase, strategic adjustments to business models and market approaches are essential, leveraging new technologies and tapping into emerging markets. The renewal phase is characterized by a focus on long-term sustainability, driven by strategic innovation and refined business processes. The findings highlight that effective crisis management requires a multidisciplinary approach, involving elements of strategic management, organizational behavior, and crisis communication. This approach not only aids businesses in maintaining operational continuity during disruptions but also enhances their capability to thrive in a post-crisis landscape. The article contributes significantly to the literature by providing a comprehensive framework for understanding how businesses can leverage crisis-induced challenges as opportunities for innovation and growth. This extended annotation provides a deeper insight into the article's content, highlighting its relevance to both academic research and practical application in the field of business strategy and crisis management. It makes clear the article's contribution to developing a nuanced understanding of how businesses can not only survive but also emerge stronger from crises.

Despite extensive research on crisis management, significant gaps still need to be discovered, particularly in the context of IT service companies. This study addresses two primary deficiencies: the need for industry-specific frameworks and a comprehensive crisis typology and taxonomy. This research aims to develop a detailed crisis typology and taxonomy specific to IT service companies, enhancing their preparedness, response, and recovery capabilities. The study identifies five primary crisis types: financial, operational, reputational, geopolitical, and natural disasters. Each crisis type is defined, with examples and recommendations for effective management. The developed taxonomy uses criteria such as nature, causes, impact, duration, frequency, and manageability to classify crises. Through literature review and case study analysis, the research reveals diverse crises faced by IT service companies and offers practical strategies for crisis management. Theoretical contributions include a sector-specific crisis taxonomy and integration of practical insights, bridging the gap between theory and practice. This research emphasizes proactive risk management and robust systems, contributing to the resilience of IT service companies.

The transition to Industry 4.0 signifies a transformative shift in manufacturing and industrial operations, driven by the convergence of advanced digital technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), digital twins, and smart automation. This fourth industrial revolution promises unprecedented improvements in operational efficiency, product quality, and overall productivity. However, the implementation of Industry 4.0 technologies is inherently complex, requiring significant expertise and coordination that many organizations lack internally. This article delves into the essential role of IT-service companies in enabling successful Industry 4.0 transformations. Through a comprehensive analysis of the service offerings and frameworks of seven leading IT-service providers-LTTS, Wipro, TCS, Cognizant, HCL Technologies, Accenture, and Capgemini-it is illustrated how these companies leverage their extensive experience and structured methodologies to support businesses in navigating the intricacies of digital integration. Each of these companies has developed robust frameworks and specialized solutions that encompass various aspects of Industry 4.0, including smart manufacturing, real-time data analytics, IoT integration, digital twins, and autonomous systems. The detailed comparison reveals that these IT-service providers bring critical advantages to the table: their ability to anticipate and mitigate potential pitfalls, their partnerships with technology giants like SAP and AWS, and their comprehensive approach to integrating digital and physical systems. By analyzing successful case studies, it is demonstrated how these companies have consistently delivered high-quality outcomes, optimized production processes, and achieved significant economic benefits for their clients. Conversely, the article also examines the challenges faced by organizations that attempt to undertake Industry 4.0 transformations without external support. These challenges include the high costs and extended timelines associated with building in-house expertise, the risk of implementation failures due to a lack of experience, and the difficulty in managing the complex integration of diverse technologies. In conclusion, this article underscores the critical importance of engaging experienced IT-service providers for Industry 4.0 initiatives. Their structured frameworks, extensive knowledge, and proven methodologies not only streamline the implementation process but also ensure that organizations can fully realize the transformative potential of Industry 4.0 technologies. By leveraging the expertise of these top IT-service companies, businesses can navigate the complexities of digital transformation with greater confidence and achieve substantial, sustainable economic benefits.

Generative Artificial Intelligence (GenAI) is transforming the economic landscape by introducing innovative solutions that significantly enhance business efficiency, drive innovation, and create competitive advantages. This article delves into the economic implications and practical applications of GenAI across various business domains, including marketing, customer support, product design, and data analysis. By leveraging advanced AI models, companies can automate routine tasks, generate personalized content, and optimize operations, leading to substantial economic benefits. The implementation of GenAI necessitates a systematic approach, starting with business concept validation and progressing through stages of technical solution identification, proof of technology, and project planning. These stages ensure that AI solutions are economically viable, effective, and aligned with business objectives. Businesses can adopt different strategies to integrate GenAI, ranging from the rapid deployment of third-party applications to the development of customized in-house models. Each approach offers unique economic benefits, balancing customization and control with implementation time and value. This article provides practical recommendations for strategy of implementing Generative AI, emphasizing the importance of careful economic analysis, stakeholder engagement, and continuous improvement. By adopting a structured approach and selecting the appropriate integration strategy, businesses can harness the transformative power of GenAI. This enables them to innovate and thrive in an increasingly competitive economic environment, positioning themselves as leaders in the digital age.

Intelligence (AI) is a rapidly evolving subset of AI technologies that involves creating new content, such as text, images, and audio, using algorithms trained on large datasets. Well-known examples of generative AI technologies include Generative Adversarial Networks (GANs) and Generative Pre-trained Transformers (GPT). These innovations are increasingly being integrated into various business applications, from automating content creation and enhancing customer interactions to driving product development and innovation. The importance of generative AI in a business context lies in its potential to augment human creativity, improve operational efficiency, and unlock new business opportunities, making it a critical tool for enterprises aiming to maintain a competitive edge in the digital age. Despite its transformational potential, the implementation of generative AI in businesses faces significant challenges. High implementation costs, the complexity of integrating AI systems into existing infrastructures, a shortage of skilled specialists, and ethical issues related to data privacy and AI-generated content are among the primary obstacles. Additionally, businesses often struggle to align AI initiatives with their strategic goals and ensure that AI outputs meet high standards of quality and compliance. These barriers complicate the large-scale and effective adoption of generative AI, limiting its potential to revolutionize business operations and innovation. The research encompasses a comprehensive review of existing literature, analysis of real-world cases, and synthesis of best practices in the strategic implementation of generative AI. It explores strategic models for the deployment of generative AI in enterprises, identifies key drivers and barriers to its adoption in business environments, and examines the strategic approaches businesses use to integrate generative AI into their operations. Insights and recommendations are provided for enterprises considering the adoption of generative AI technologies. The importance of the research lies in its potential to help businesses overcome implementation challenges and maximize the benefits of generative AI. Understanding strategic models and approaches to AI integration will enable businesses to better navigate the complexities of AI deployment, enhance their innovative capabilities, and ensure sustainable growth in an increasingly competitive market. The conclusions drawn from the research aim to bridge the gap between theoretical insights and practical applications of generative AI, providing a valuable resource for business leaders, technology strategists, and policymakers seeking to leverage AI for competitive advantage.

In contemporary economics, data science models play a crucial role in analyzing complex relationships, predicting
economic trends, and informing policy decisions. This article reviews the most commonly used data science models in
economics, including econometric models like linear and logistic regression, Probit and Tobit models, time series analysis
models such as ARIMA and Vector Autoregression (VAR), and panel data analysis methods like fixed and random effects models
and Difference-in-Differences (DiD). Additionally, it explores machine learning algorithms, clustering and classification
techniques, dimensionality reduction methods, Bayesian methods, and natural language processing (NLP) applications. The
article highlights their purposes, applications, and relevant works, emphasizing the strengths and limitations of each model.
It also discusses the impact of these models across various sectors, including finance, retail, energy, and healthcare. This
comprehensive overview underscores the importance of aligning data science models with business objectives, ensuring data
quality, investing in scalable technologies, fostering a data-driven culture, and addressing ethical considerations. The article
concludes with future research directions, such as advanced neural network architectures, large language models, generative
AI models, hybrid models, and the need for interpretable and ethical AI applications in economics. The importance of this topic
lies in the transformative potential of data science models to enhance economic analysis and decision-making. By leveraging
advanced data science techniques, economists can gain deeper insights into complex economic phenomena, improve forecasting
accuracy, and develop more effective policies. As data-driven approaches continue to evolve, they provide powerful tools for
addressing critical economic challenges, driving innovation, and fostering sustainable growth across various sectors.

The article explores the vital role of machine learning (ML) and data science in advancing business efficiency, especially under crisis conditions like the ongoing conflict in Ukraine. It discusses how digital transformation through these technologies is crucial for maintaining competitiveness and operational resilience. As part of the research, it was conducted deep analysis of existing works. It was identified gap of comprehensive studies on the strategic application of cloud-based ML and data science solutions during crises. This study Highlights the increasing accessibility of ML and data science tools due to technological advancements, fostering a competitive business landscape. The study emphasizes the democratization of advanced technologies facilitated by cloud platforms like Microsoft Azure, Google Cloud Platform (GCP), and Amazon Web Services (AWS), making sophisticated tools accessible to smaller companies. Article concludes that strategic use of ML and data science significantly bolsters business resilience and efficiency, especially in challenging environments like Ukraine. Article examines ML tools and services provided by AWS, Azure, and GCP. As an assessment criterion it was chosen features, integration capabilities, innovation, pricing structures, computing capabilities, and security measures. In scope of this research it was defined that each platform offers robust ML solutions with unique strengths tailored to different business needs. For example, AWS excels in specialized tools, Azure in integration within its ecosystem, and GCP in sustainability and advanced technologies. Article provides recommendations for selecting cloud-based ML and data science solutions that align with operational strategies and crisis management needs. It encourages ongoing research to explore the long-term impacts of these technologies on business innovation and market dynamics. Highlights the need for further studies into the socio-economic impacts of ML and data science, including addressing privacy, security, and ethical concerns. Article provides tailored advice on choosing appropriate ML and data science tools to support their specific needs during the ongoing crisis. Also, it suggests broader adoption of cloud-based ML and data science technologies for enhanced decision-making and operational efficiency.