Use of AI Technologies in Improving the Business Processes of Companies

Innovations Management JEL C45, O33 I. A. Kokorev, V. M. Kiselev, V. V. Velikorossov, A. L. Poltarykhin, G. S. Ukubassova Use of AI Technologies in Improving the Business Processes of Companies KEYWORDS ABSTRACT artificial intelligence, business process, business process modeling, automation Introduction. With the rapid development of digital technologies, artificial intelligence is becoming an essential tool for enhancing efficiency, automating processes, improving customer service, and adapting the business to dynamic markets. The topic is particularly relevant due to rising competition and the need to introduce innovations for sustainable development. Research in this area is crucial for optimizing operations, preventing errors, developing new strategies, and fostering innovation. The article aims to analyze the main areas of AI application in business, assess their impact on processes, and identify promising areas of development. Materials and methods. The study draws on sources from scientific journals such as the Journal of Business Economics and Management, Studies in Big Data, Procedia Computer Science, and others. Relevant literature was analyzed using the VOSviewer program, which allows visualization of bibliometric networks based on citations, co-authorships, and research connections. Results. Currently, over half of organizations employ AI to optimize email communications (61%), enhance customer interactions (56%), streamline production processes (51%), strengthen cybersecurity (51%), and detect fraudulent activities (51%). The prospects for AI in business are promising, encompassing expanded automation, improved personalization, enhanced analytics, and development of new business models. Responsible and integrated use of AI alongside other technologies is expected to drive high efficiency and sustainable growth. Conclusion. Despite challenges such as data quality and ethical implementation, AI offers significant opportunities for business transformation in the digital age. Integrating AI effectively is a critical strategic factor for achieving competitive advantage. FOR CITATION Received: Jun 6, 2025 Accepted: Sep 3, 2025 Published: Dec 1, 2025 Kokorev, I. A., Kiselev, V. M., Velikorossov, V. V., Poltarykhin, A. L., & Ukubassova, G. S. (2025). Use of AI Technologies in Improving the Business Processes of Companies. Economic Consultant, (4), 4–16. https://doi.org/10.46224/ecoc.2025.4.1 This is an open access article distributed under a Creative Commons Attribution-ShareAlike International License (CC-BY-SA 4.0) that allows others to share the work with an acknowledgement of the work’s authorship and initial publication in this journal eiSSN 2686-9012 statecounsellor.wordpress.com ECONOMIC CONSULTANT. 2025. 4 5 INTRODUCTION In today’s rapidly evolving technological landscape, AI has become a central tool for improving business processes. Its implementation allows companies to increase operational efficiency, automate routine tasks, enhance customer service, and adapt to changing market conditions. The relevance of the topic is driven by growing competition and the need for innovative solutions that ensure sustainable business development. According to IT Desk, 78% of organizations worldwide use AI in at least one business function, and 92% of companies plan to increase investment in AI over the next three years. The AI software market is projected to be worth USD 174.1 billion by 2025, with annual growth of about 25% through 2030 [1]. A bibliometric map of relevant studies was constructed using VOSviewer (see the section Materials and Methods), which presents citation-based relationships among publications (see Figure 1). Scientific community pays significant attention to integration of artificial intelligence into business processes. In particular, N. Lutfiani et al. analyze this relationship in three key aspects: adoption and implementation of AI in organizations, impact of AI on various aspects of business performance, and potential problems. Several factors influence AI adoption and implementation, including data availability, organizational culture, leadership support, technical expertise, and ethical considerations [2]. eiSSN 2686-9012 statecounsellor.wordpress.com ECONOMIC CONSULTANT. 2025. 4 6 J. L. Ruiz-Real et al. provide an analysis of modern research on artificial intelligence methods in business. The authors conducted a bibliometric analysis, identifying eleven thematic areas and the most common terms used in AI research [3]. Thomas Davenport describes the technologies available and how companies can use them to gain business and competitive advantages, emphasizing that AI technologies do not replace humans but augment their capabilities, enabling intelligent machines to work alongside skilled professionals [4]. O. Brown et al. highlight that generative AI, including ChatGPT’s real-time access to internet information, enhances dynamic interaction with clients. However, it raises concerns about accuracy and reliability, given the sometimes unverified nature of online content [5]. Recent research focuses on more narrowly defined aspects of AI use in business, such as retail [6], personnel management [7], and similar fields. Thus, scientific research on the use of AI in business is relevant and essential for enhancing efficiency, preventing errors, designing new models and strategies, and ensuring sustainable, innovative company development in the digital age. The article aims to analyze the main areas of AI application in business, assess their impact on processes, and identify promising areas of development. MATERIALS AND METHODS The study utilized articles from journals such as Journal of Business Economics and Management, Studies in Big Data, Studies in Systems, Decision and Control, Procedia Computer Science, Journal of Intelligent Manufacturing, Future Business Journal, Journal of the Knowledge Economy, Journal of Technology Transfer, Sustainability, Review of Managerial Science, and Journal of Marketing Analytics. A map of relevant studies was generated using VOSviewer, a software tool for constructing and visualizing bibliometric networks (journals, researchers, and publications) based on citations, bibliographic links, co-citations, and co-authorship. Publications from the OpenAlex database were used as the empirical base. The search included the terms artificial intelligence and business for 2020-2025, yielding 324 publications. Data were collected in spring 2025. Citation analysis and normalization of citation data were performed. eiSSN 2686-9012 statecounsellor.wordpress.com ECONOMIC CONSULTANT. 2025. 4 7 STUDY RESULTS AI enables automation of routine processes, improves decision quality, enhances operational efficiency, personalizes services, and supports rapid adaptation to market changes and trend forecasting. Currently, more than half of organizations (Figure 2) use AI to optimize email (61%), enhance customer interactions (56%), streamline production processes (51%), and strengthen cybersecurity and fraud detection (51%). Figure 2 Percentage of companies using AI [8] M. T. Nuseir et al. note that AI is widely applied across industries including technology, business, healthcare, automotive, and academia. Tasks such as sales, fraud detection, customer service, and product recommendations are increasingly optimized using AI [9]. D. Parwani et al. highlight AI’s role in transforming industry practices, fostering innovation, and optimizing processes in manufacturing, healthcare, finance, and retail [10]. eiSSN 2686-9012 statecounsellor.wordpress.com ECONOMIC CONSULTANT. 2025. 4 8 S. R. Sandeep et al. describe AI’s contribution to manufacturing and service delivery, including applications like machine translation, chatbots, and selflearning algorithms that can help people better understand the environment and respond accordingly [11]. S. Nosova et al. examine the challenges of transforming business processes using AI in a highly competitive global market. According to the authors, AI can boost business profitability by: a) creating a virtual workforce, b) enhancing physical capital while developing and refining the skills of existing employees, and c) driving innovation, enabling both developers and companies to bring new products to market [12]. T. Chin et al. observe that a growing number of enterprises are employing AI to develop ecosystem business models (EBMs) which require more effective coordination among multiple stakeholders to achieve a dynamic and sustainable balance between people, production, and profit [13]. G. Pisoni and M. Moloney discuss how AI can be used to manage and optimize business processes, especially in finance, while emphasizing the importance of responsible AI implementation. They identify critical issues and tasks that organizations must address to ensure AI is applied ethically and effectively in a corporate context [14]. A. Haldorai et al. explore the main applications of AI in business modeling, demonstrating how both advanced and contemporary AI tools and technologies can support decision-making and process optimization [15]. The primary challenges in integrating AI into business include the complexity of implementation, high costs, insufficient staff training, and the risk of errors or misuse, all of which necessitate significant adjustments to existing business processes. V. Varriale et al. emphasize that integrating AI with other advanced technologies is a rapidly expanding field that can significantly influence business performance. They show that AI is deeply interconnected with emerging technologies, indicating promising avenues for research in which AI integration can provide substantial benefits within specific production systems [16]. S. Khaneja and T. Arora argue that incorporating AI-based neurobiological methods and software into business operations is essential for companies to maintain competitiveness in today’s rapidly evolving environment [17]. eiSSN 2686-9012 statecounsellor.wordpress.com ECONOMIC CONSULTANT. 2025. 4 9 F. Ji et al. examine the interplay between technological readiness, innovation potential, automation and control, and privacy and security considerations within the context of Industry 4.0. Their findings indicate that technological readiness acts as a catalyst for innovation, highlighting the critical role of a reliable technological infrastructure. Furthermore, innovation capacity serves as a significant mediator between technological readiness and privacy and security dynamics, emphasizing the interdependent relationship between innovation and security in digital business [18]. B. Zhang and B. Peng identify differences between leader and follower companies by employing text analysis and machine learning methods to develop AI indicators that empirically assess how leading companies influence AI development in their followers. According to the researchers, leading companies exert influence primarily through demonstration, competitive pressure, and network effects. Their study also shows that leading companies in regions with advanced digital infrastructure, operating in nationally supported key industries, or with state ownership have a stronger stimulative effect on the AI capabilities of follower companies [19]. S. D. Jankovic and D. M. Curovic emphasize the importance of AI-driven data analytics for enhancing decision-making, optimizing resource allocation, and improving overall operational efficiency to support sustainable practices. Their research identifies three distinct company profiles (low, medium, and high) differing in AI implementation levels and other critical parameters [20]. M. Shahin et al. argue that combining lean manufacturing tools with AI represents a transformative approach to optimizing production, reducing waste, and increasing efficiency. The authors stress that successful implementation of lean manufacturing practices alongside AI requires careful attention to data quality and algorithmic accuracy [21]. J. Åström et al. contend that the capabilities of AI technologies alone are insufficient; companies must also understand how to commercialize these technologies through appropriate innovation within AI-driven business models. Their study outlines the essential actions organizations must undertake to generate value from AI, which includes three key stages: identifying prerequisites for AI value creation, selecting mechanisms to generate value, and developing AI-based business models [22]. In sales, AI enables automating customer interactions, personalizing offers, forecasting customer demand and behavior, and improving both sales efficiency and the overall customer experience. eiSSN 2686-9012 statecounsellor.wordpress.com ECONOMIC CONSULTANT. 2025. 4 10 H. A. Lari et al. identify key applications of AI in e-commerce through a review of research from multiple sources. The authors highlight that AI significantly enhances the operational efficiency of e-commerce companies. In recent years, these companies have increasingly invested in AI technologies to foster business growth and development [23]. M. I. Campante et al. propose a conceptual framework for examining how sales professionals utilize AI. They emphasize the urgent need for more comprehensive research into the role of AI in B2B sales [24]. Q. Zhang and Y. Xiong note that one of the most promising applications of AI is personalized product recommendations. They explore the development of AI-based systems for product design, sales, and customer experience in e-commerce. The authors underscore AI’s crucial role in enhancing productivity, sales performance, and the overall consumer experience through personalized recommendations [25]. L. Sharakhina et al. examine methodologies for studying the visual components of advertising and advocate for revising traditional approaches to analyzing advertising messages. They propose combining biometric tracking with AI-driven techniques to capture viewers’ emotional responses to video content [26]. Despite its potential, adoption of AI in business encounters several challenges, including high implementation costs, insufficiently trained personnel, poor-quality data, and difficulties integrating AI into existing processes. P. Jafarzadeh et al. report that implementing AI in traditional industrial and service enterprises remains challenging. Two primary obstacles are accurately harnessing AI’s capabilities within business processes and ensuring transparency of business logic and data sources for AI experts. The authors discuss strategies to help SMEs understand and leverage AI’s potential [27]. A. Fenwick et al. observe that introducing AI systems in organizations faces obstacles ranging from technical issues to human-related barriers, often resulting in failed implementation or outcomes below expectations. They highlight the critical role of human resource management (HRM) in ensuring AI integration aligns with organizational values and goals [28]. M. Grebe et al. note that, despite high expectations across industries and corporate functions, many companies struggle to realize AI’s full potential and remain limited to pilot projects [29]. eiSSN 2686-9012 statecounsellor.wordpress.com ECONOMIC CONSULTANT. 2025. 4 11 H. F. Hansen et al. emphasize that only a small number of organizations have successfully implemented AI technologies at scale. Their research indicates that early AI adoption involves foundational tasks that evolve as projects mature. The authors provide tools for assessing current maturity levels and offer practical guidance for the continued deployment of AI technologies within organizations [30]. The transition of organizations toward intelligent solutions requires adapting business processes, investing in technology and employee training, and fostering a corporate culture that supports the successful implementation and use of AI. Y. Chen et al. examine digital servitization, describing it as the transformation of manufacturing companies from providing standard products and services to delivering intelligent solutions. They highlight the case of the Chinese air conditioner manufacturer Gree, which has become a global leader through innovative offerings. AI-enabled air conditioners at Gree automatically adjust to environmental changes and allow for remote monitoring and maintenance via the Internet of Things (IoT) [31]. M. Roux et al. point out the scarcity of empirical evidence regarding the role of intangible organizational capabilities in facilitating AI adoption within SMEs. Their findings suggest that an organization’s ability to implement structural and cultural changes significantly supports AI integration, ultimately enhancing productivity [32]. C. R. Sauer and P. Burggräf emphasize the persistent challenge of determining the optimal level of human-AI interaction in decision-making. They propose a structured framework for assessing and establishing this optimal interaction across various production scenarios [33]. S. Kalogiannidis et al. investigate the effectiveness of AI technologies in predictive risk assessment and their contribution to ensuring business continuity. Their study demonstrates that AI significantly enhances both the accuracy and speed of risk assessment procedures, with natural language processing (NLP) playing a key role [34]. S. Nosova et al. focus on the application of cybersecurity measures to intelligent, data-driven decision-making systems in order to safeguard against cyberattacks. Their research aims to develop models of cybersecurity incidents and design an appropriate cybersecurity framework for organizations [35]. Overall, AI emerges as a pivotal tool for addressing a wide range of business challenges (sales optimization, fraud prevention, and customer service) while eiSSN 2686-9012 statecounsellor.wordpress.com ECONOMIC CONSULTANT. 2025. 4 12 simultaneously promoting innovation and improving efficiency across sectors such as manufacturing, healthcare, and finance. DISCUSSION The prospects for AI adoption in business are highly promising, encompassing further expansion of process automation [36], greater personalization of services, and enhanced analytics and forecasting capabilities [37]. AI technologies are poised to play an increasingly central role in new business models development, optimization of operations, and creation of competitive advantages. Moreover, responsible use of AI, combined with its integration alongside other innovative technologies, is expected to deliver high efficiency and sustainable growth in the future [38] The potential applications of AI span a wide range of economic sectors. Future developments are likely to include further algorithmic improvements, expanded automation and analytics capabilities, and the consideration of ethical and social implications of technology use. In key industries such as healthcare, finance, manufacturing, transportation, and logistics [39], AI is expected to enhance diagnostic accuracy, support risk management, optimize production, and facilitate new business models development [40; 41]. Additionally, AI adoption is anticipated to extend to education, agriculture [42], and the service sector, boosting efficiency, reducing costs, and driving innovation across these areas. CONCLUSION Implementing AI in business processes offers companies new opportunities to improve efficiency, enhance competitiveness, and foster innovation. AI adoption automates repetitive tasks, strengthens analytics and decision-making, and enables more personalized customer experiences. Despite ongoing challenges related to data quality and the responsible deployment of AI technologies, their application promises substantial business growth and transformation in the era of rapid digital development. Consequently, AI integration has become a critical factor for organizational success and a strategic instrument for gaining competitive advantage.