AI in Hyper Automation: From RPA to Intelligent Automation Ecosystems

This has changed in the digital age, where social media is no longer seen as a tool of communication, but rather it has become a transformation factor that is transforming how organisations innovate and redesign their business processes. The review paper discusses the complex influence of social media on digital innovation and transformation of business processes based on the strategic importance of social media in facilitating agile, data-driven and customer-centric organisational ecosystems. Based on an integrative review of the existing research, the research paper summarises the ways social media platforms enable open innovation, promote collaboration, spread knowledge faster and democratise the generation of ideas via digital communities. The paper explains how social media analytics, artificial intelligence, and big data help in enhancing innovation potentials through providing real-time insights, predictive intelligence, and evidence-based decision-making. The review further examines how the business processes, especially marketing, customer relationship management, human resources and supply chain operations, are undergoing reengineering with the integration of social media, which is facilitating the firms to respond with personalised digital experiences. Although these opportunities exist, the review marks various challenges, such as privacy concerns, overload of information, skills gaps, organisational resistance and reliance on a particular platform that could lead to barriers to beneficial transformation. The paper ends with future research directions highlighting the use of social media and emerging technologies, including AI, IoT, blockchain, and the metaverse, to provide future directions for further digital transformation. On balance, the study offers a detailed insight into the role of social media as an agent of digital innovation and the transformation of the business process that allows organisations to be competitive in a constantly evolving technological environment. Keywords: Social Media, Digital Innovation, Business Process Transformation, Social Media Analytics, Open Innovation, Digital Transformation, Emerging Technologies

The rapid advancement of artificial intelligence (AI) fundamentally transformed the business environment by optimising management processes, enhancing operational efficiency, and generating new challenges for enterprises. This necessitated a comprehensive analysis of its impact on the transformation of business processes. The objective of the study was to examine the influence of AI on the transformation of business processes in enterprises of various sizes, identify primary directions of its application, and assess development prospects. The study was grounded in the use of systemic analysis, comparative analysis, case study method, and statistical data analysis. Scientific publications, corporate reports, analytical materials, and practical implementation cases were processed. The findings revealed that the adoption of AI significantly increased the efficiency of business processes in large enterprises through operational automation, customer interaction personalisation, and enhanced market forecasting. In the financial sector, machine learning algorithms reduced fraud rates and expedited document processing, consistent with global practices in banks such as JPMorgan Chase, Mizuho, and SMFG. In retail, improvements in recommendation system accuracy were observed, as reported by global companies like Amazon and Netflix, where personalisation contributed to increased conversion rates. Manufacturing enterprises reported optimised logistics and reduced operational costs due to AI implementation. Furthermore, enterprises actively explored the potential of generative AI in marketing, product development, and employee productivity, yielding results aligned with international experience, including the deployment of GitHub Copilot, which improved the speed and quality of software development. The practical value of the study lay in the applicability of its results for executives, analysts, and consultants to enhance digital transformation strategies and improve enterprise competitiveness

The subject of the study is the managerial, organizational, economic, and strategic aspects of the digital transformation of oil and gas companies in the context of the development and implementation of artificial intelligence technologies. The focus is on the processes of forming and implementing key pathways of digital transformation, including technological solutions, organizational and managerial changes, business process transformation, and the integration of digital initiatives into the strategic management system. The research aims to analyze the mechanisms of utilizing artificial intelligence technologies to support managerial decisions, as well as to identify the relationships between the level of digital maturity of companies, the quality of management, and the sustainability of their development. Special attention is given to the systematic nature of digital transformation and to the conditions under which the implementation of intelligent technologies ensures a sustainable economic and managerial effect in the oil and gas industry. The methodological framework of the research consists of systemic and structural-logical approaches, methods of analysis and synthesis, as well as comparative analysis of practices in digitization and implementation of artificial intelligence technologies in oil and gas companies. The scientific novelty of the research lies in the development and justification of a comprehensive approach to analyze the digital transformation of oil and gas companies under the conditions of developing artificial intelligence technologies. Unlike existing studies, which predominantly emphasize the technological aspects of implementing digital solutions, this work considers digital transformation as a multi-level managerial process. A systematization of the pathways of digital transformation is proposed, including technological, organizational-managerial, business process, and strategic levels, as well as revealing their interconnections and complementary nature. Special attention is given to the role of artificial intelligence as a tool for supporting managerial decisions and as a factor in transforming strategic management mechanisms. The research concludes that the effectiveness of digital transformation in oil and gas companies is determined not by the scale of the implementation of individual digital technologies but by the degree of their integration into the management system and strategic development. It has been established that the application of artificial intelligence technologies requires simultaneous changes in organizational structure, managerial processes, and business models. It has been proven that only the comprehensive and coordinated implementation of all pathways of digital transformation allows for increased manageability, adaptability, and sustainability of oil and gas companies in conditions of high technological and market uncertainty.

This comprehensive technical article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) technologies on enterprise digital transformation initiatives. It examines the current state of AI/ML adoption across industries, highlighting key applications including intelligent process automation, advanced analytics, and customer experience enhancement. The article provides detailed analysis of how AI/ML redefines enterprise capabilities through predictive analytics, natural language processing, and computer vision applications. It further investigates how these technologies drive business value through data-driven insights, personalized customer experiences, and process optimization. Implementation considerations including data infrastructure, MLOps, and organizational factors are discussed in depth. The article concludes with an exploration of emerging trends such as federated learning, generative AI, edge computing, and AI-augmented decision making. Throughout, the article presents research-backed evidence of the quantifiable business impact achieved by organizations successfully implementing these technologies.

The article examines the influence of entropy on the digital transformation of the enterprise's business processes, which is an important topic in today's conditions of globalization and rapid technological development. The introduction of new digital technologies leads to an increase in uncertainty and chaos in organizational structures, which complicates the management of business processes. The authors noted that digitalization completely changes the way tasks are performed and the structure of processes at the enterprise, which in turn can lead to a change in the roles and responsibilities of employees. It is justified that any changes in the enterprise automatically create uncertainty and form the basis for resistance to changes. It has been proven that when an enterprise implements new technologies, such as: automation, artificial intelligence, or the Internet of Things (IoT), it faces the need to integrate relevant technologies into the already existing business processes of the enterprise, which can become de stimulators of the development of economic entities. The main focus is on the analysis of factors that contribute to the increase of entropy, such as the increase in the volume of information, inefficient data flows and the growth of cyber threats. The article also considers possible ways to reduce entropy, including reengineering business processes and using modern data management technologies. The authors emphasize the need for further research for a deeper understanding of the specific risks and opportunities associated with digital transformation, which will contribute to the successful development of enterprises in turbulent market conditions. Special attention is paid to the importance of entropy management to ensure the stability and competitiveness of enterprises. The authors emphasize the need for further research for a deeper understanding of the specific risks and opportunities associated with digital transformation, which will contribute to the successful development of enterprises in turbulent market conditions. The increase in entropy in the process of digitization is a natural consequence of the complexity and uncertainty associated with the introduction of new technologies and processes, so in order to successfully manage these challenges, enterprises must actively work on reducing the level of entropy through standardization, integration and clear communication of changes in the organizational structure.

Abstract: The chapter "NextGen Realities: AI, AR, and Data Science Integration" explores the transformative convergence of Artificial Intelligence (AI), Augmented Reality (AR), and Data Science, highlighting their combined potential to revolutionize various industries. It delves into the core technologies, their synergistic integration, and practical applications in fields such as finance, engineering, healthcare, and manufacturing. The chapter examines the mathematical foundations underlying these technologies, discusses the development of intelligent conversational agents, and investigates the unified frontier of AI and VR. By addressing technical challenges, ethical considerations, and future trends, this chapter provides a comprehensive overview of how the integration of AI, AR, and Data Science is shaping the future of innovation and digital transformation. Keywords: Artificial Intelligence (AI),Augmented Reality (AR),Data Science,Big Data,Virtual Reality (VR),Machine Learning (ML),Predictive Analytics,Generative Design,Conversational Agents,Intelligent Automation,Financial Technology (FinTech),Engineering Design,Healthcare Technology,Manufacturing Processes,Mathematical Foundations,Real-Time Data Processing,Smart Systems, Digital Transformation,Future Trends and Ethical AI.

The article is devoted to the study of approaches to managing the digital transformation of business entities. It presents differences in the interpretation of the essence of the concepts of digitalization, digitization, and digital transformation. Three main areas of the enterprise affected by digital transformation have been identified: work with clients, operational business processes, and business models of the enterprise. Digitalization of enterprise business processes will influence changes in value formation and structural changes, and will also depend on them. It is substantiated that the level of use of digital technologies will be greatly influenced by the financial capabilities of the enterprise. An algorithm for the digital transformation of an enterprise has been developed, which makes it possible to choose priority areas for optimizing business processes through varying degrees of digitalization. This algorithm provides a preparatory stage at which it is recommended to carry out a detailed diagnosis of the external environment and assess the degree of penetration of digital technologies into the industry in which the activity is carried out. It provides for diagnostics of the enterprise taking into account available resources and identification of business processes that require innovative changes and solutions. The developed algorithm provides for partial optimization of individual business processes (installation of ERP-, CRM-, HRM- and other resource management systems, document management, logistics, etc.) and significant changes in several business processes using AI, BigData, IoT, cloud technologies, etc. The algorithm ends with the implementation stage, at which the necessary software and technological solutions are implemented. Transformation of operational processes through automation will provide economies of scale and increase productivity. The elements of digitalization of business processes of the enterprise are characterized. Three variable blocks in the transformation of business models are considered: digital modifications of individual processes, the introduction of new digital business models, and digital globalization. Conclusions about the main advantages of enterprises that have carried out digital transformation are drawn.

Artificial intelligence (AI) and smart automation are revolutionizing the global supply chain ecosystem at an accelerated pace, providing tremendous potential for resilience, innovation, efficacy, and profitability. This paper examines how AI, machine learning (ML), and robotic process automation (RPA) influence supply chain operations to adjust to the risks and vulnerabilities. It focuses on how AI and other relevant technologies will enhance forecasting to predict actual demand, expedite logistics, increase warehouse efficiency, and promote instantaneously making decisions. This study utilizes thematic analysis to find AI-driven supply chain applications, including logistics optimization, forecasting demand, and risk mitigation, among 383 peer-reviewed articles (2017–2024). It provides a strategic framework for dealing with vulnerabilities, operational excellence, and resilient solutions. Additionally, the research investigates how AI contributes to supply chain resilience by predicting disruptions and automating risk mitigation strategies. This paper identifies critical success factors and challenges in adopting intelligent automation by analyzing real-world industry implementations. The findings will propose a strategic framework for organizations aiming to leverage AI to achieve operational excellence, agility, and real-time information flow for effective decision-making.

The integration of Artificial Intelligence (AI) in supply chain management has emerged as a transformative strategy to enhance efficiency, resilience, and adaptability in complex global logistics networks. This proposes a comprehensive model for AI integration in supply chain optimization, drawing comparative insights from the United States and Nigeria two economies with contrasting technological infrastructures, regulatory frameworks, and economic dynamics. The model emphasizes a multi-tiered approach incorporating predictive analytics, real-time decision-making, and intelligent automation. In the U.S., AI adoption is characterized by advanced infrastructure, data availability, and investment in digital transformation, enabling real-time inventory management, demand forecasting, and autonomous logistics. Conversely, Nigeria presents a context of infrastructural challenges and data limitations, yet exhibits increasing potential for AI-driven solutions through mobile technology, cloud computing, and emerging policy support. The proposed model comprises four core pillars: (1) AI-readiness assessment focusing on digital infrastructure and data maturity; (2) phased AI adoption strategy tailored to sector-specific needs; (3) stakeholder collaboration for technological transfer and capacity building; and (4) policy alignment to foster ethical AI use and cross-border interoperability. Through comparative analysis, the model identifies key enablers and barriers within each country, highlighting opportunities for technology leapfrogging in Nigeria and continuous innovation in the U.S. This argues that while the scale and mode of AI integration may differ, the strategic alignment of technology, talent, and governance is essential for effective supply chain optimization in both regions. This research contributes to the discourse on inclusive AI deployment by illustrating how adaptable frameworks can bridge technological disparities and drive supply chain resilience. Ultimately, it underscores the importance of localized strategies within a global model, fostering both economic growth and digital equity. The findings serve as a guide for policymakers, industry leaders, and technology developers seeking to harmonize AI integration across diverse socio-economic contexts.

In today’s digitally driven market, Software as a Service (SaaS) providers face increasing pressure to deliver seamless, efficient, and personalized customer experiences (CX). Artificial Intelligence (AI) has emerged as a transformative tool in achieving these goals, enabling companies to anticipate customer needs, automate support services, and drive user engagement. This study explores the application of AI technologies in enhancing CX within SaaS environments, focusing on how tools such as chatbots, recommendation engines, predictive analytics, and natural language processing (NLP) contribute to improved customer satisfaction, retention, and loyalty. Drawing upon relevant literature, industry practices, and empirical data collected from selected SaaS firms, the study investigates the extent to which AI adoption has reshaped customer interaction models and decision-making processes. The research applies the Technology Acceptance Model (TAM) and Service-Dominant Logic as theoretical frameworks to understand both user adoption and service delivery dynamics. Data was gathered through structured questionnaires and interviews, and analyzed using both descriptive and inferential statistics. Findings reveal that AI significantly enhances CX by reducing response time, providing proactive support, and enabling real-time personalization. However, challenges such as data privacy concerns, integration complexity, and user trust remain barriers to full adoption. The study concludes by recommending strategic investment in explainable AI, improved customer training, and ethical data handling practices to maximize the potential of AI in CX management. This research contributes to the growing body of knowledge on digital transformation in SaaS and provides practical insights for technology leaders seeking to leverage AI for competitive advantage.

Background: It is widely accepted that the digital transformation of business is increasingly attracting the attention of researchers from the academic circles as well as professionals from the business community. The main consequence of this lies in the daily development of new and improvement of existing digital technologies. The outcomes of such events on the market are reflected in all aspects of companies' operations. For this reason, they are constantly looking for various improvements to their business, which most often include the implementation of new technology. Mere implementation of a new technology without any other changes very often leads to failure. The core of this failure can be found and attributed to inadequately identified, analysed, documented and established business processes. Business process management (BPM) and redesign as its integral part are actually an indispensable segment of a successful process of digital business transformation. Therefore, the digital transformation of business should not be viewed exclusively from a technological perspective, but also from a process viewpoint. Purpose: With the aim of shedding additional light on the connection between business process management and digital business transformation, the paper aims to identify and explain the importance of business process redesign. Study design/methodology/approach: For the purposes of this paper, a systematic literature review was conducted. Findings/conclusions: The result of the conducted research indicates that a process approach to the digital transformation of business can contribute to significantly different, more successful results. Limitations/future research: Limitations refer to the number of databases searched during this systematic literature review. Subsequent research could include additional sources that would include additional works that can contribute to a better research result.

Intelligent automation has become a key part of using artificial intelligence (AI) in managing businesses, changing the way companies operate. By combining machine learning, robotic process automation (RPA), and advanced analytics, intelligent automation helps automate both simple tasks and more complex processes. This improves efficiency, encourages innovation, and adds flexibility, helping businesses stay competitive in fast-changing markets. For example, companies in fields like manufacturing, healthcare, and retail have seen major improvements by using intelligent automation in their operations, showing how adaptable this technology is. AI also plays a key role in decision-making. By automating the processing and analysis of large datasets, AI provides managers with reliable information to solve problems and find new opportunities. It helps analyze market trends, improve supply chains, and evaluate risks, making sure decisions are fast, data-driven, and match the company’s goals. AI tools also help businesses predict the outcomes of different strategies, reducing risks and improving decision-making. AI-driven data analysis helps companies understand consumer behavior, predict demand, and analyze finances more accurately, leading to better decisions and long-term growth. While AI brings many benefits, the research also looks at challenges companies face. These include concerns about data security, ethical issues, high costs of implementation, and the need for skilled workers to manage and optimize AI systems. Solving these challenges is essential to make sure AI adoption is effective, ethical, and long-lasting. To fully take advantage of AI, businesses should focus on being transparent, invest in training their employees, and encourage teamwork to reduce risks and get the most out of AI

In the context of growing global pressure for tax transparency and digital transformation, multinational corporations face increasing challenges in managing documentation and ensuring compliance with transfer pricing regulations. This paper explores how Artificial Intelligence (AI) technologies—specifically Natural Language Processing (NLP), Robotic Process Automation (RPA), and machine learning—can enhance the efficiency, accuracy, and traceability of documentation processes related to intercompany transactions. Based on a qualitative analysis and a case study of OMEGA Group, a large European multinational, we identify critical opportunities and limitations in the implementation of AI tools for fiscal reporting and documentation management. Our findings reveal that the integration of AI-driven systems significantly reduces human error, accelerates data processing, and improves alignment with OECD and EU regulatory standards. Moreover, we highlight how strategic investment in digital compliance infrastructures contributes to broader organizational sustainability by reducing operational risk and enhancing institutional credibility. In addition, this study considers the legal and regulatory implications of using AI for fiscal documentation, emphasizing the importance of algorithmic transparency and explainability (XAI) in jurisdictions with high formalism. Furthermore, the paper addresses emerging ESG requirements, particularly the EU’s Corporate Sustainability Reporting Directive (CSRD), and how AI supports sustainability-related disclosure by reducing paper-based processes and improving traceability. We also propose the inclusion of fiscal benchmarking mechanisms into AI systems to improve proactive risk assessment and cross-border compliance harmonization. The study concludes with practical recommendations for corporate decision-makers and public authorities aiming to improve tax governance through intelligent automation. It contributes to the growing body of literature on digital sustainability and offers a timely and original perspective on the intersection of technology, fiscal compliance, and responsible international management.

In retail, strategies in increasing sales through innovation and customer experience can be a critical factor in business success. In this case, artificial intelligence can be used to analyze customer data and market trends to develop innovations and customer experiences that can increase sales. To achieve this goal, it is necessary to implement an effective data management strategy and the use of appropriate technology to collect and analyze customer data in real-time. Thus, retail businesses can understand customer behavior, and design innovations that suit their needs. In addition, artificial intelligence can help retail businesses in the development of effective and relevant marketing strategies, leading to increased customer traction and experience. Through data analysis, retail businesses can identify market trends and customer needs, and develop innovative solutions that can improve customer experience. In addition, artificial intelligence can help retail businesses in developing effective and relevant marketing strategies, leading to increased customer attraction and experience.

Event-based automation systems are a revolutionary paradigm in the healthcare information technology activities that respond to the critical issue of operational resiliency in the growing and increasingly complex digital health ecosystems. The SupportPlus model proves how intelligent automation may radically rethink the validation process, moving away from a manual process of reactivity towards validation to a system-driven mechanism of assurance. Using microservices architecture, serverless computing, and container orchestration on the Azure Kubernetes service, the framework integrates validation, monitoring, and compliance functions in a metadata-based ecosystem. Application to mission-critical healthcare services also demonstrates significant operational benefits such as radical decreases in manual validation cost, faster incident response settings, and system availability that is approaching five-nines reliability. The architecture uses event-driven orchestration to instigate automated validation gates built into continuous delivery pipelines, modifying compliance with periodic manual audits into continuous compliance processes. The self-healing functionality is defined as the capability of the system to automatically recover most of the validation failures, and this aspect is fundamentally changing the dynamic between the operations of IT and the system reliability. In addition to the short-term efficiency, the framework harmonizes the validation practices throughout organizational teams, eradicates tribal knowledge dependence whatsoever, and develops ongoing compliance evidence that can be examined during a regulatory audit. The measurable effect proves that intelligent healthcare IT automation is not only cost optimization but also meets the main demands of patient safety, care continuity, and trust of people in digital health infrastructure.