КОМПЬЮТЕРНОЕ ЗРЕНИЕ КАК СПОСОБ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ БИЗНЕС-ПРОЦЕССОВ В ОРГАНИЗАЦИЯХ

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

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, reports, conference proceedings, experimental studies, literature reviews, scientometric analyses, books, and multi-case studies, dating from the years of 2018 to 2024. This literature review defines machine learning (ML), artificial intelligence (AI), computer vision, and convolutional neural networks (CNNs). It also compares machine learning to traditional programming and reveals the types of learning in ML models’ training. ML and its correlation with AI are also discussed and details about theory of mind, self-aware AI, reactive machines, and limited memory AI are shared. The literature expounds computer vision, particularly convolutional neural network (CNN) and CNN layers. Recent cutting-edge applications of artificial intelligence including generative AI models and autonomous systems are also incorporated. Finally, the literature briefly addresses the alleged threat of AI taking over the job market. The findings of this literature review reveal that AI is becoming the new way of operating. The conclusion shows that AI models require significant computation to allow computers to learn autonomously. Thus, understanding mathematical models of data and perfecting the process of writing software could be the key to remaining employable as more jobs are expected to be shifted due to AI and tasks automation. Keywords: Cognitive technology, artificial intelligence, machine learning, computer vision, convolutional neural networks

Highlights AI can enhance agricultural safety through predictive models, LLMs, computer vision, and wearables. Machine learning is a tool that has the potential to predict hazards using historical data on injury incidents, weather, and worker behavior. Computer vision and remote sensing applications could be used to detect unsafe conditions for real-time risk mitigation. Wearable AI devices present opportunities to monitor worker health and prevent injuries in agricultural environments. ABSTRACT. Artificial intelligence (AI) has emerged as a transformative tool in various industries, including agriculture, where it has the potential to enhance safety and reduce injury risks. This review explores the application of AI techniques in occupational safety with a special focus on agricultural safety, focusing on predictive modeling, large language models (LLMs), computer vision, and wearable technologies. Due to the limited number of studies that address AI in agricultural safety, research from related fields, such as construction safety, was also considered for its potential applicability. The findings indicate that (1) predictive models that leverage machine learning (ML) algorithms can assess historical data and forecast hazards, enabling proactive safety measures. (2) LLMs can improve injury report analysis by extracting key terms and patterns to identify recurring risks. (3) Computer vision and remote sensing technologies enhance environmental monitoring by detecting real-time unsafe conditions. At the same time, (4) AI-powered wearable devices can track worker health indicators such as heart rate, potentially preventing injuries. A total of 85 studies were analyzed, providing insights into the diverse applications of AI in mitigating occupational hazards, specifically agricultural hazards. This review highlights the current advancements and future research opportunities for AI-driven safety interventions in high-risk occupational environments. Keywords: Agricultural safety, Artificial intelligence, Computer vision, Health, Injury prevention, Large language models, Machine learning, Occupational safety, Predictive modeling, Wearable technology.

Introduction. Modern organizations operate with large volumes of data from various sources, in particular: intelligent sensors; tools for monitoring, controlling and diagnostics; syslogs and human-generated content, etc. others Artificial intelligence technologies increase the capabilities of data analysis and use them for effective business, can create original images and texts for marketing purposes and give intelligent recommendations for analytics. The above indicates that artificial intelligence is gaining more and more importance as a tool for increasing efficiency, optimizing resources and providing competitive advantages to the enterprise's business strategies. Purpose. Identification of the key advantages of using artificial intelligence technology for enterprise management and analysis of its impact on productivity, quality and efficiency of business processes, as well as prospects for the development of this technology in the modern business environment. Methodology. Systematic analysis of scientific publications on this topic. The summarization of materials on the research topic was carried out in accordance with the established requirements for a comprehensive search of scientific publications in open access in order to identify general trends and assess the prospects for the development of this technology in the modern business environment. The results. In the materials of the article, the justification of the key advantages of using artificial intelligence for the management of enterprises is provided and it is established that artificial intelligence has a significant potential for automating routine tasks, in particular, such as document processing, database management or creating reports, etc. It is noted that artificial intelligence contributes to the rational justification of management decisions based on the analysis of large volumes of data in the company's activities, taking into account the behavior of consumers of products (services), relevant financial and economic indicators and market trends. It is advisable to use artificial intelligence in the optimization of supply chains taking into account the risk and justification of alternative logistics solutions. In the field of quality control, artificial intelligence provides automatic detection of defects with the help of computer vision, which reduces the amount of defects and increases compliance with standards. In addition, it is an effective tool for personnel management, assisting in recruitment, performance evaluation and forecasting of training needs. Based on the results of the research, it was concluded that artificial intelligence is becoming an integral part of modern enterprise management, providing opportunities to optimize business processes by reducing costs and introducing innovations. It has been established that in order for enterprises to make the most of the advantages of artificial intelligence technology, it is necessary to invest in the adaptation of internal processes and personnel training, which contributes to the creation of prospects for long-term progressive development, ensuring flexibility and competitiveness in conditions of rapid changes in the market environment.

ABSTRACTThis account of practice details how an action learning methodology was successfully implemented within a corporate business environment in order to aid its emergence from bankruptcy. The need to quickly develop and adopt new knowledge and business practices was critically important to the survival of the business. The business had been underperforming for several years and was in the process of emerging from bankruptcy. An action learning methodology was implemented to enable an accelerated process of organizational learning and business process transformation. Ultimately, action learning proved to be critically important for the adoption of the new approach to the business. The action learning process was responsible for tailoring the new approach to fit within the already established operating framework of the business. The new knowledge and processes that resulted from action learning were also central to an improvement in the performance of the business and a successful emergence from bankruptcy.

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

In recent years, professional education in computer vision and artificial intelligence (AI) has witnessed transformative changes, a process significantly accelerated by the COVID-19 pandemic, which catalyzed the shift towards online learning formats. In Ukraine, as in many other countries, universities have rapidly adapted their curricula to align with these new realities. The onset of martial law in Ukraine further entrenched online learning as the dominant mode of education. Additionally, there has been a noticeable shift in research activities towards projects tailored to the defense sector, unfolding against the backdrop of significant global advancements in AI for computer vision. This article explores the impact of these shifts and the “Artificial Intelligence Development Concept” introduced by the Ministry of Digital Transformation of Ukraine on the quality and methodologies of computer vision education. It scrutinizes specific training courses in this domain, along with the literature used in the educational process. The exploration includes how deep neural network technologies such as TensorFlow, PyTorch, OpenCV, and Python libraries are being integrated into computer vision curricula, reflecting the latest advancements in the field. It also examines the needs of Ukrainian startups and companies specializing in computer vision projects, analyzing how curricula are evolving to meet the specific requirements of modern software developers. Furthermore, the article proposes directions for the further development of online education in computer vision teaching in Ukraine.

This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and a speculative AI 4.0 (Conscious AI). Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 is built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, the proposed AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly 70 years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data have stimulated each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI’s transformative potential benefits society.

Distributed Ledger Technologies (DLTs), including but not limited to Blockchain, have become key enablers of digital transformation across sectors. Their core attributes — immutability, traceability, decentralization, and tamper resistance — make them well-suited for secure and transparent data management. While initially linked to cryptocurrencies, DLTs are now applied in diverse domains such as identity verification, supply chain management, secure voting, and digital asset certification.In parallel, rapid advances in artificial intelligence (AI), particularly in Generative AI, are reshaping how data is generated, interpreted and applied. AI enables new forms of automation and decision-making, but it also introduces risks related to misinformation, digital fraud and security vulnerabilities. The convergence of DLT and AI offers a promising framework to address these challenges. As decentralized trust infrastructures, DLTs can enhance the transparency and integrity of AI systems by validating identities, certifying data provenance and securing sensitive assets such as contracts, medical records and intellectual property.This integration fits within the broader BIBI paradigm – Blockchain, AI, Big Data and the Internet of Things (IoT), where DLTs provide the foundation for secure, distributed coordination and auditability. Coupled with AI, they enable automated decision-making over large-scale, heterogeneous data, particularly from IoT devices, with applications in manufacturing, logistics and health care. This synergy also unlocks advanced functionalities, including decentralized identity, verifiable credentials and trusted automation, facilitating use cases such as tokenized assets and secure digital certification.Nevertheless, several technical and ethical challenges remain. The increasing computational demands of AI raise concerns about energy consumption, an issue already encountered in early DLT deployments. Energy-efficient consensus mechanisms, such as Proof-of-Stake and Byzantine Fault Tolerance, offer valuable insights for developing more sustainable AI infrastructures. Another key challenge is interoperability, as most DLT platforms operate in isolation. Enhancing interoperability would enable seamless data and asset exchange across heterogeneous ledgers, contributing to a more integrated and user-centric digital ecosystem.Looking forward, the convergence of DLT and AI holds significant potential for intelligent smart contracts, advanced traceability in critical sectors such as food safety and health care, and the development of digital twins for real-time simulation and optimization of organizational processes. This cross-disciplinary innovation can drive not only technological and economic growth but also democratic access to trusted, privacy-preserving, and sustainable digital systems.The Special Issue “Distributed Ledger Technologies and Artificial Intelligence” aims to offer a dedicated forum for researchers exploring the intersection of these two rapidly evolving fields. A total of 13 submissions were received, and following a rigorous double-blind peer review process, with at least three reviews per manuscript, three articles were selected for publication.The first article, “Energy: Reducing Latency in IoT DLTs for AI-Driven Real-Time Solutions” (Moya Perez et al., 2025), addresses the challenges of integrating IoT networks with DLT and AI, focusing on latency and energy efficiency. The authors propose Energy, a novel consensus algorithm designed for public DAG-based DLTs, which reduces or eliminates Proof-of-Work requirements. Experimental results demonstrate substantial improvements in low-payload scenarios common in IoT, enabling real-time AI model updates and efficient, secure data flow in large-scale environments.The second paper, “eFLEET: A Framework in Federated Learning for Enhanced Electric Transportation” (Ruiz de Gauna et al., 2025), presents a federated learning approach for optimizing autonomous electric vehicle routing in urban settings. Using traffic image analysis through computer vision and a DAG-based privacy layer, the framework selects optimal routes based on real-time and predictive data. The experiments show how the system improves traffic planning, enhances scalability and contributes to energy efficiency and pollution reduction in smart cities.The third contribution, “Enhancing the Viewing, Browsing and Searching of Knowledge Graphs with Virtual Properties” (Dibowski, 2024), introduces virtual properties as a user-interface enhancement for knowledge graph exploration. Defined using SHACL and evaluated via SPARQL queries, virtual properties enrich class scopes beyond direct neighbors. Successfully implemented at Bosch for over 100,000 users, this novel approach improves usability and expressiveness in knowledge-driven applications and sets a new direction for SHACL-based UI development.The authors would like to express their sincere gratitude to all the authors who submitted their contributions and to the anonymous reviewers for their thoughtful and rigorous evaluations. The authors also extend their appreciation to Professor Honghao Gao, Editor-in-Chief of IJWIS (Emerald), for his support in the publication of this special issue. The authors believe the selected papers represent cutting-edge research in this area and will be of great interest to the DLT and AI research communities.

In the context of escalating global polycrises, residential high-rises face unprecedented challenges that threaten infrastructure and occupant safety. This literature review examines the role of Artificial Intelligence (AI) in managing these crises within vertical communities. Covering studies from 2014 to 2024, the review utilized a comprehensive search strategy across Web of Science, Scopus, IEEE Xplore, Google Scholar, and PubMed, focusing on AI, polycrisis, and high-rise buildings. The analysis identified 87 relevant studies showcasing the potential of AI technologies — such as machine learning, natural language processing, and computer vision — to enhance crisis management. Key findings indicate that AI can significantly improve emergency preparedness, real-time information dissemination, and resource optimization. Computer vision advances hazard detection accuracy, facilitating faster evacuations and better safety. The integration of IoT sensor networks with predictive analytics reduces false alarms and enhances early threat detection. However, the review also highlights challenges, including data privacy, cybersecurity, scalability, and the balance between AI autonomy and human oversight. Future research should address these limitations by focusing on real-world case studies to assess AI performance across diverse crisis scenarios. Development of more robust AI models and their integration with existing crisis management frameworks is essential. Ethical considerations, particularly regarding privacy and bias, must also be scrutinized to ensure that AI solutions are both effective and equitable. This review provides valuable insights into using AI to bolster urban resilience and support Sustainable Development Goals in high-rise residential settings, emphasizing the need for strategic AI integration and ongoing research to address current gaps and challenges.

Artificial Intelligence (AI) is revolutionising project management by offering a suite of advanced tools and techniques that optimise planning, execution, and monitoring processes.This study explores the integration of cutting-edge AI technologies, including Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), and Reinforcement Learning (RL), across traditional and hybrid project management methodologies, such as Agile, Waterfall, and Lean.Empirical studies demonstrate AI's transformative impact, highlighting its ability to address inefficiencies inherent in conventional methodologies while equipping organisations with the agility to meet evolving project demands.AI-driven solutions provide actionable insights through predictive analytics, enabling organisations to pre-emptively identify bottlenecks, allocate resources dynamically, and mitigate risks.Sentiment analysis tools facilitate enhanced stakeholder engagement by monitoring team dynamics and communication patterns and fostering timely interventions.CV applications in resource-intensive industries, such as construction and manufacturing, improve the precision of quality control, safety compliance, and progress tracking.RL offers a strategic advantage by simulating various scenarios to enhance risk management and optimise decision-making processes.Despite its transformative capabilities, AI adoption in project management faces challenges such as fragmented data infrastructure, algorithmic biases, and the opacity of advanced models.Addressing these challenges requires robust data governance frameworks, ethical guidelines, and cross disciplinary collaboration to ensure transparency, accountability, and fairness.Innovative technologies, such as federated learning and generative AI, offer significant potential to propel the field forward by ensuring data privacy, streamlining workflow automation, and fostering adaptive project environments.Integrating AI's analytical precision with human creativity enables organisations to develop scalable and adaptive solutions, effectively addressing the complexities of modern project landscapes.This synergy establishes AI as a fundamental pillar of project management excellence, empowering organisations to drive sustainable innovation and efficiency in an ever evolving global environment.Offering actionable insights and recommendations, this study highlights the importance of strategic and ethical AI integration to fully realise its transformative potential in project management. Computer Vision (CV)In industries such as construction and manufacturing, CV technologies monitor physical resources, ensure safety compliance, and track asset utilisation through real-time image and video analyses 10 . Reinforcement Learning (RL)RL algorithms dynamically adjust project schedules and resource allocations based on ongoing performance metrics and are especially useful in highly variable environments, such as software sprints or large-scale engineering projects 3 . 2.

The advent of Artificial Intelligence (AI) has revolutionized various domains, and Content Management Systems (CMS) are no exception. Traditional CMS workflows, often reliant on manual processes, have struggled to keep up with the growing demands for scalability, speed, and personalization in managing digital content. AI - powered CMS address these challenges by introducing automation, enhancing efficiency, and delivering personalized user experiences. Through the integration of AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision, modern CMS have evolved into intelligent platforms capable of automating workflows, optimizing content delivery, and driving data - driven decision - making. This paper explores the transformative potential of AI in CMS, focusing on its applications in workflow automation and user experience enhancement. AI enables the automation of repetitive tasks like content tagging, categorization, and scheduling, significantly reducing manual effort and human error. At the same time, it enhances user engagement by offering personalized content recommendations, optimizing search functionalities, and dynamically adapting content to user contexts. These advancements allow businesses to manage content more effectively and cater to diverse audience needs in real time. However, the integration of AI into CMS is not without challenges. Issues such as data privacy concerns, bias in AI models, and the complexity of integrating AI into legacy systems remain significant barriers. This paper addresses these challenges and proposes actionable recommendations, including the development of explainable AI systems, robust data security frameworks, and scalable architectures that accommodate diverse business needs. As businesses increasingly adopt AI - powered CMS, their potential to transform content management is undeniable. These systems not only streamline workflows and improve operational efficiency but also empower organizations to deliver meaningful and personalized experiences to users. By addressing current challenges and advancing AI technologies, the future of content management will be defined by intelligent, adaptive, and user - centric platforms that bridge the gap between technology and human creativity. This comprehensive review highlights the opportunities and challenges of leveraging AI in CMS and underscores the importance of continued research and innovation to fully realize its potential. AI - powered CMS are not merely tools for managing content but catalysts for digital transformation, enabling organizations to thrive in an increasingly dynamic digital landscape.

PurposeMany authors have recognised multiple research gaps at empirical and theoretical levels regarding the interplay between the management of intellectual capital (IC) and the development of innovation capabilities from a strategic approach to technological opportunities, especially in knowledge-intensive business services (KIBS). This paper endeavours to provide answers to some of these research questions by relying on empirical observations in the context of an emergent economy.Design/methodology/approachAfter reviewing the literature through the lenses of the IC and knowledge-based view, the paper operationalises IC as a formative construct and explores its relationships with Technological Orientation, Innovativeness and Innovative Performance. This investigation employs structural equation modelling to analyse data collected through a questionnaire from a sample of KIBS members in a cross-sectional study.FindingsThe research confirms the positive influence of human capital on a company’s Technological Orientation, which in turn impacts Innovative Performance in KIBS. Structural and relational capital positively affect Innovativeness and the latter directly impact performance. All other IC components were found to determine the dependent variables through a mediated effect.Research limitations/implicationsThis research has several limitations. Firstly, it has to be stressed that the significance of the statistical findings is dependent on the sample size, and the results’ explanatory power and effect size cannot be extrapolated to other demographics in different regions or sectors. Secondly, it should be observed that the results of the empirical research can be affected by the respondents’ subjectivity in assessing the items and their perception of reality when providing the answers. Thirdly, this is a cross-sectional study, and results concerning, e.g. the Innovative Performance may be subject to change over a longer period of time, though items in this construct have inquired about results obtained over time. Finally, given the purpose of the research, the outcome may be limited to the research model design. Future research could attempt to replicate the results by investigating other demographics and other regions and by employing a longitudinal study.Practical implicationsThe study highlights the value added by adequate management of all IC elements, as they affect the output of organisational business strategies and processes in an integrated manner. An organisational technological strategy that is based on up-to-date technologies, the embeddedness of technology in the organisational processes and the staff’s continuous technical/digital upskilling are conducive to competitive advantage and business success in KIBS. High competencies and skills that are continuously renewed through training as well as the acquisition of digital competencies, play an influential role in generating innovative capabilities that lead to business performance. Flexible and efficient business processes, the company’s support for informal knowledge sharing between employees and its ability to exploit new knowledge to sustain growth are prerequisites for KIBS’ success. Promoting an organisational culture of trust and collaboration, making the staff feel valued and satisfied and effectively using digital technologies for communication are all significant factors in supporting the development of innovation capabilities.Social implicationsThis research proves that IC management, viewed as an integrated construct comprising human, structural and relational capital management, significantly affects Innovativeness through the development of technology-based innovative capabilities. Furthermore, in a context where KIBS (and other companies) nowadays compete in a digital environment, technology and technical knowledge are paramount for a company’s success. The study validates in an aggregated model the role played in innovation by the management of knowledge and technical knowledge embodied in the employees’ competencies and know-how, the management of tacit and explicit knowledge developed by the structural capital and the management of stakeholder knowledge accumulated by the relational capital.Originality/valueThis research takes a fresh and comprehensive perspective to investigate how IC management can impact Innovativeness and Innovative Performance in KIBS by focusing on the nexus with the organisations’ Technological Orientation. The paper makes an original contribution to research concerning IC, KIBS, technology and innovation based on empirical evidence and a new approach to assessing IC as an aggregate rather than by looking at individual components.

As AI-driven intelligent automation platforms, RPA is transforming business processes across sectors. This study analyses how rule-based automation becomes hyperautomation, improving scalability, cognition, and process optimization. Integrating “artificial intelligence (AI), machine learning (ML)” and cognitive technologies with RPA systems concerns data quality, scalability, workforce reluctance, security, and real-time processing complexity. After careful examination, the study suggests cloud-based orchestration, AIpowered data preparation, workforce upskilling, and edge AI deployment. It analyses 'how intelligent automation influences banking, healthcare, manufacturing, retail, telecom, and e-commerce business process efficiency, decision intelligence, cost optimization, worker productivity, and customer experience. Decision-making, digital transformation, and operational agility are enhanced with intelligent automation. This review highlights self-learning automation systems, AI model explainability, and scalable architectures for sustainable corporate development and encourages further research.