Artificial intelligence-powered image recognition retail checkout systems

The article comprehensively examines the profound impact of digital transformation on the processes of strategic decision-making within contemporary organizations, with a specific emphasis on the integration of artificial intelligence (AI) solutions and advanced big data analytics into managerial frameworks. The analysis delineates the principal directions of transformation in strategic management paradigms, substantiating the pivotal role played by intelligent digital instruments in strengthening organizational competitiveness, adaptability, and innovation capacity. A structured classification of essential data analytics tools employed in strategic decision support is presented, accompanied by meticulously selected practical case studies that demonstrate the successful application and tangible benefits of AI technologies across diverse sectors, including manufacturing, services, and public administration. Furthermore, the research critically assesses key obstacles to digital adaptation, such as data integrity and quality challenges, inadequate digital literacy among personnel, resistance to organizational change, and ethical dilemmas associated with algorithmic and automated decision-making processes. The proposed conceptual framework, termed the Digital Analytical Strategic Management Architecture (DASMA), encompasses multiple layers of analytics: descriptive, diagnostic, predictive, prescriptive, and cognitive. This model advocates decisionmaking logics that prioritize human-centricity, adaptability, and organizational transparency, particularly through the integration of explainable artificial intelligence and proactive strategic foresight. Notably, the DASMA model supports the iterative evolution of managerial approaches, enabling organizations to responsively navigate volatility and uncertainty in their operational environments.The results of this study make a significant contribution to the methodological foundation for the design and implementation of data-driven management strategies. The findings are intended to enhance executive-level decision-making, facilitate the operationalization of digital transformation frameworks, and foster sustainable organizational resilience and competitiveness in the face of ongoing digital disruption and complex, unpredictable market dynamics.

The integration of Artificial Intelligence (AI) in the food industry is revolutionizing customer experience by enabling personalized services, real-time interactions, and operational efficiency. This chapter explores how AI-driven technologies such as machine learning, natural language processing, computer vision, and recommender systems are enhancing the customer journey across food delivery, restaurant services, and backend operations. The chapter explores applications like chatbots, voice assistants, visual recognition, and sentiment analysis, while addressing key challenges including data privacy, algorithmic bias, and regulatory compliance. The chapter also covers the topic of ethical issues and sustainability and such topics as generative AI, emotion recognition, and federated learning. Thus, this chapter aims to give a brief on how AI can be employed in the food industry to develop smart solutions that enhance customers' experiences in the changing industry environment.

In the era of rapid technological advancement, the integration of artificial intelligence (AI) and digital consumerism has created new trends in business. Consumers are able to communicate with brands in new ways owing to developments in digital media. AI-powered recommendation systems, chatbots and virtual assistants are the main drivers of this change. It allows businesses to offer product recommendations, customer support, and personalized content to increase user engagement and satisfaction. This chapter provides real-world examples of AI applications across industries, highlighting success stories of companies using AI to create better value and customer satisfaction. Finally, the integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers.

The augmented reality (AR) field has experienced substantial progress in recent years, driven by breakthroughs in hardware, software, and computer vision techniques. Artificial intelligence (AI) integration has significantly enhanced AR, making it more accessible and expanding its practical applications across various industries, notably in automotive manufacturing. In this context, AR aids assembly processes by improving the efficiency and accuracy of assembly line workers. AR systems provide real-time guidance and feedback by incorporating object detection, tracking, and digital content overlay, increasing productivity and superior quality in automobile production. This chapter delves into the transformative role of AR in the automotive industry, highlighting its impact on the design process, manufacturing, and customer experience. Drawing on Machine Learning (ML) methodologies discussed in previous chapters, the chapter explores how AR technologies are employed to streamline complex assembly tasks, reduce human error, and enhance overall operational efficiency. The design process benefits from AR through enhanced visualization and prototyping, allowing for more precise and creative developments. In manufacturing, AR supports workers by overlaying critical information and instructions directly onto their field of view, facilitating faster and more accurate assembly operations. This real-time assistance boosts productivity and ensures that higher quality standards are met consistently. The chapter addresses the use of AR in enhancing the customer experience, from virtual showrooms to personalized, interactive user manuals, creating a more engaging and informative interaction with the product. By providing a comprehensive overview of AR's applications in the automotive sector, this chapter underscores the technology's potential to revolutionize industry practices. The integration of AI and AR not only enhances current manufacturing processes but also paves the way for innovative advancements in automotive design and customer engagement.

Artificial intelligence (AI) integration into education presents challenging ethical and practical issues as well as transforming possibilities for individualized learning, operational efficiency, and improved outcomes. This report investigates trust, transparency, algorithmic bias, and responsibility in AI-driven educational systems from the points of view of three hundred teachers, educators, and researchers around India. The results highlight a notable knowledge gap: Just 28.33% of respondents are highly familiar with artificial intelligence in education, while 32.67% are completely unfamiliar. Though 35% of respondents are unsure about data privacy policies, 41.67% of respondents believe AI can greatly improve educational outcomes, so they are cautiously optimistic. Only 27.67% of respondents said they were completely aware of how artificial intelligence systems use data. Thus, algorithmic bias became a major issue for 34% of respondents, along with general calls for transparency. The results highlight the need for human supervision since 35.67% of respondents support artificial intelligence use just under such circumstances. This paper comes to the conclusion that tackling these issues calls for strong ethical rules, professional growth to raise AI literacy, and inclusive models for transparency and responsibility. These steps are essential to guarantee fairness and confidence in AI integration, so opening the path for responsible application in learning environments.

Advancements in Artificial Intelligence (AI) and edge computing are reshaping what is possible in the world of hyper-personalization, currently powering customer experience in e-commerce and retail. In this study we explore when AI algorithms and edge computing frameworks enable real time, individualized customer interactions resulting in deeper engagement and higher loyalty. This study uses a quantitative research design to explore data driven personalization techniques with datasets from leading retail and ecommerce platforms. To evaluate the scalability and responsiveness of hyper-personalization technologies, a combination of machine learning models and terms edge computing infrastructures is methodologically analyzed. Through case studies of top tier e-commerce platforms, results show that integration of AI and edge computing can reduce latency in customer interactions by up to 70%, while upgrading conversion rates by roughly 30%. These findings emphasize the added value of creating real time customer experience driven sales and customer loyalty leveraging real time customer insights enabled by AI at the edge. This work presents a novel framework that enables scalable, secure hyper personalization in e-commerce to overcome existing limitations in data processing speed and privacy compliance. It offers actionable recommendations for retailers that look to adopt AI driven, edge enabled hyper personalization techniques for enhancing customer engagement and operational efficiency.

Data analytics is reshaping the landscape of banking in Africa, presenting both significant opportunities and notable challenges. This manuscript provides a comprehensive review of the role of data analytics in enhancing financial services across the continent. The analysis begins by exploring the various applications of data analytics, including credit scoring, fraud detection, customer segmentation, and personalized marketing. These applications have demonstrated substantial potential in driving financial inclusion, improving operational efficiency, and enhancing customer experiences. Despite the promising benefits, the implementation of data analytics in African banking is impeded by several challenges. These include limited data quality, regulatory constraints, a shortage of skilled professionals, and infrastructural limitations. The manuscript examines these obstacles in detail and discusses potential strategies for overcoming them, such as investing in technology, fostering collaborative ecosystems, and developing robust regulatory frameworks. Case studies of leading African banks, such as Equity Bank, First National Bank, and Standard Bank, illustrate successful applications of data analytics and highlight the transformative impact on financial services. The review also considers future prospects, including the expansion of mobile and digital banking, the integration of artificial intelligence, and increased collaboration between banks and fintech companies. Overall, while data analytics offers considerable promise for enhancing financial services in Africa, its successful implementation requires addressing existing challenges and leveraging emerging opportunities. The findings underscore the need for a strategic approach to harness data analytics effectively, aiming to drive financial inclusion and foster socio-economic development across the continent. Keywords: Data Analytics, African Banking, Financial Inclusion, Credit Scoring, Fraud Detection, Machine Learning.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies into Enterprise Resource Planning (ERP) systems represents a paradigmatic shift in organizational digital transformation strategies. This research investigates the revolutionary impact of AI/ML integration within SAP ecosystems, examining performance improvements, operational efficiency gains, and strategic advantages. Through comprehensive analysis of implementation patterns across diverse industry sectors, this study presents empirical evidence demonstrating significant enhancements in predictive analytics, automated decision-making, and resource optimization. The findings reveal that organizations implementing AI-enabled SAP solutions experience an average 35% improvement in operational efficiency, 42% reduction in manual processing time, and 28% increase in predictive accuracy for demand forecasting. This research contributes to the growing body of knowledge on intelligent ERP systems and provides practical insights for enterprise digital transformation initiatives.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

The U.S. retail sector, a $7 trillion economic engine, is undergoing rapid transformation fueled by digital commerce, shifting consumer expectations, and intensified competition. At the heart of this shift lies the imperative to modernize customer service infrastructure. This paper explores how Salesforce Service Cloud—a leading Customer Relationship Management (CRM) platform—can be strategically deployed to optimize service delivery, reduce churn, and support omnichannel responsiveness across the retail value chain. We begin by contextualizing the urgency of service innovation, drawing on recent data showing that U.S. companies lose over $75 billion annually due to poor customer experiences. We then examine Salesforce Service Cloud’s capabilities, including AI-driven case management, real-time analytics, automation, and integration with e-commerce platforms. Through use-case illustrations and performance metrics, we demonstrate how these features enable faster resolution times, improved customer satisfaction, and more efficient use of the retail workforce. Importantly, we highlight the platform’s role in enhancing employment efficiency for over 2.7 million Americans working in customer-facing roles, enabling frontline staff to shift from routine inquiries to high-value engagement. Finally, we position CRM modernization within the broader context of U.S. economic priorities, referencing Executive Order 14058 and other federal mandates that call for customer experience transformation. Our findings support the conclusion that Service Cloud is more than a software solution—it is critical infrastructure for ensuring that the U.S. retail sector remains competitive, resilient, and aligned with the digital economy of the future. Keywords: Customer Experience, Salesforce, Retail Sector, Digital Commerce.

Purpose Artificial intelligence (AI) services are vital in enhancing customer experience and purchase intentions in the international online fashion retail sector. This study explores customers’ intentions to use AI-enabled services, focusing on transaction utility, trust and product uniqueness across the customer journey in the context of international online fashion stores. This study also assesses how privacy moderates customer intentions. Design/methodology/approach This study adopted a longitudinal research design and purposive sampling technique to collect a total of 566 participants. The final data were analyzed using IBM SPSS Amos version 21 software. Findings The study highlights the significance of transaction utility, trust and product uniqueness in AI integration across the customer journey (pre-purchase, during purchase and post-purchase stages). Most of the direct relationships are significant, except the relationship between the during purchase and post-purchase stages. With a few exceptions, AI integration commonly does not mediate the relationship between antecedents and intention to use AI-enabled services. Privacy moderates AI integration in post-purchase, during purchase and intention to use AI-enabled services, except in the pre-purchase stage. Originality/value This study bridges important gaps in the literature by integrating AI-enabled services and customer behavior, contributing to a broader knowledge of customer interactions in global e-commerce fashion stores. The study examines multiple attributes that impact intention, such as transaction utility, trust, product uniqueness, AI integration in three stages of purchases (pre-purchase, during purchase and post-purchase) and privacy, using three major theories: mental accounting theory, trust commitment theory and commodity theory.

The article explores the role of digitalization in the development of public finance, highlighting its key advantages, challenges, and directions for implementing digital technologies in financial resource management. It is determined that digitalization is a key factor in enhancing the transparency, efficiency, and accountability of budgetary processes. The integration of big data analytics, artificial intelligence, and blockchain technologies contributes to process automation, improves revenue and expenditure forecasting, and enables the timely detection of deviations or violations.The study examines the implementation of digital platforms such as ProZorro and “Diia,” which have significantly increased the transparency and accountability of budget expenditures while strengthening public trust in government authorities. It has been established that expanding their functionality through the integration of artificial intelligence and automation of financial operations opens new opportunities for improving public finance management efficiency.At the same time, the digitalization process faces challenges, including the insufficient level of digital literacy among civil servants, uneven access to digital services across regions, high modernization costs for infrastructure, and the complexity of integrating new technologies with existing systems. Overcoming these barriers requires a systematic approach that includes developing digital infrastructure, reforming legislation, fostering financial technologies, and strengthening cybersecurity.The study analyzes international experience in the digitalization of public finance across various countries. Using China as an example, which demonstrates a high level of integration of information and communication technologies into financial management, it is shown that their application enhances transparency, reduces costs, detects deviations, improves imperative planning, and increases the accountability of public officials. Moreover, digitalization contributes to strengthening a country’s financial sovereignty. In particular, the use of automated control and monitoring systems significantly enhances the accountability of government agencies. China’s successful experience demonstrates the potential for effectively integrating digital technologies with public financial policy. For Ukraine, this opens up prospects for introducing a mixed economic system that combines market mechanisms with effective state regulation. The implementation of such approaches, coupled with profound social transformations, will contribute not only to the digitalization of public finance but also to strengthening social security and ensuring the long-term development of the country.The research findings have both theoretical and practical significance for the development of a national strategy for the digitalization of public finance. A set of measures is proposed to enhance public trust in government institutions, reduce financial losses, optimize expenditures, and improve Ukraine’s international competitiveness. The integration of digital technologies will not only enhance the transparency of budgetary processes but also ensure the long-term stability of the country’s financial system. Further research should focus on improving digital ecosystems, adapting international experience, and assessing the socio-economic impact of digitalization.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

Background: In today's digital age, consumers interact with brands through various channels, including websites, social media, and mobile apps. This has led to the rise of "digital consumers" with distinct cognitive skills and expectations. Businesses are constantly seeking ways to improve customer experience (CX) and stay competitive. Objective: This article explores how artificial intelligence (AI) is transforming the digital consumer experience. Methods: A qualitative research approach was employed, utilizing literature reviews, expert interviews, and case studies to gain insights into the application of AI in customer interactions and its impact on the digital customer journey. Findings: The research found that the integration of AI in digital consumerism has created new trends in business, enabling personalized and dynamic interactions between consumers and brands. AI-powered tools like recommendation systems, chatbots, and virtual assistants blur the lines between pre-purchase, purchase, and post-purchase stages, offering a more seamless customer experience. Conclusions: The integration of artificial intelligence and digital customer experience has the potential to transform the future of e-commerce, marketing, and customer service, opening new horizons for both businesses and consumers. However, challenges related to data privacy, ethics, and algorithmic biases need to be addressed. Originality/Significance: This article presents an original contribution to the understanding of the evolving digital consumer experience and the impact of AI on this landscape. While the use of AI in various aspects of business has been explored

Abstract. Introduction. Today, characterized by the rapid digitalization and intellectualization of the global economy, the integration of artificial intelligence (AI) in transportation services is gaining ground. Consequently, maritime logistics, as part of international trade, places new demands on the training of highly qualified personnel capable of continuously acquiring professional competencies to optimize processes, improve safety, and enhance economic sustainability. This article examines the challenge of training future transport industry specialists capable of adapting to the continuous integration of innovative digital technologies and artificial intelligence in the maritime economic sector. The aim of this article is to develop a scientifically sound concept for training personnel for the transport industry that takes into account the specifics of integrating artificial intelligence into the maritime economic sphere. The methodological foundations of the study include comparative pedagogical and personal-activity approaches, systems analysis, and modeling methods. Advanced concepts and theories in the fields of artificial intelligence, pedagogy, and transport economics are used as a methodological basis. Research Results. The author attempts to present conceptual aspects of training specialists for the transport industry, taking into account the specifics of AI implementation in the maritime economic sector. Based on an analysis of the current state of AI integration in the maritime economic sector, a core set of competencies and skills necessary for working with AI in transport is identified and the structure of a training model for transport specialists is presented. The conclusion describes the potential impact of this model on the development of the maritime industry as a whole.