AI empowers enterprise agility and performance: Research trends and implications for future research

The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned

This article examines the theoretical and practical implications of artificial intelligence (AI) integration in supply chain management (SCM). AI has developed dramatically in recent years, embodied by the newest generation of large language models (LLMs) that exhibit human‐like capabilities in various domains. However, SCM as a discipline seems unprepared for this potential revolution, as existing perspectives do not capture the potential for disruption offered by AI tools. Moreover, AI integration in SCM is not only a technical but also a social process, influenced by human sensemaking and interpretation of AI systems. This article offers a novel theoretical lens called the AI Integration (AII) framework, which considers two key dimensions: the level of AI integration across the supply chain and the role of AI in decision‐making. It also incorporates human meaning‐making as an overlaying factor that shapes AI integration and disruption dynamics. The article demonstrates that different ways of integrating AI will lead to different kinds of disruptions, both in theory and in practice. It also discusses the implications of AI integration for SCM theorizing and practice, highlighting the need for cross‐disciplinary collaboration and sociotechnical perspectives.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

The study is devoted to the issue of digital transformation of business structures and focuses on examining the conceptual foundations of the digital transformation of these entities. Digital transformation is one of the key trends in modern business. A vision of a paradigm shift in development regulation, based on the consideration of digital methods of delivering value to the market has been outlined in the research. The essence of the digital transformation process lies in the creation of a new mode of production, characterized by the integration of human and artificial intelligence. It is substantiated that digital transformation is a strategic goal of the company, which requires a holistic approach and leads to a change in the very economic paradigm. It is noted that digital transformation is accompanied by the application of advanced technologies that serve as its foundation, and the study identifies a list of such technologies. It is revealed that technologies do not operate in isolation but create a powerful synergistic effect. Digital transformation fundamentally alters the way companies create value and generate profit. Instead of traditional models, digital technologies enable the development of new, innovative approaches that redefine market entry strategies. It is identified the key components of corporate digital transformation in the study, namely: the human factor, technological foundations, organizational change, and strategic development. The need for digital transformation is shaped by both external and internal factors that compel organizations to reconsider their strategy and operations. It is emphasized that the most challenging aspect of transformation is not technological but human and organizational. Cultural adaptation, employee engagement, and the enhancement of digital skills are critically important for overcoming resistance and minimizing risks. Transformational changes in the digital economy make businesses more flexible, innovative, and data-driven, yet they require a balance between technologies and the human factor. It is further noted that the next stages of digital transformation – including hyper-automation, cognitive robotics, and further integration of artificial intelligence – will demand continuous adaptation and innovative thinking from organizations to remain leaders in the era of the digital economy.

The goal of this research is to examine prior work on the subject of artificial intelligence (AI) integration in the banking sector of India using a systematic literature review (SLR). Using the PRISMA paradigm, this study examines the industry's and customers' perspectives on how artificial intelligence (AI) is transforming banking services. Chatbots, automated customer care, predictive analytics, and fraud detection are just a few examples of the AI applications that this study synthesizes from a wide variety of academic and business sources to highlight important trends, advantages, and disadvantages. The report emphasizes the increasing value of AI-driven ease, customization, and security, and how these technologies are transforming the user experience. But it does deal with important things like data privacy, ethical concerns, and the digital divide.. Through this analysis, the study aims to offer actionable insights into the successful integration of AI, fostering a more adaptive and customer-centric banking environment. Current research predominantly relies on descriptive analysis and secondary data sources. To advance the understanding of consumer perspectives and AI integration in banking, there is a pressing need for empirical research. This study would enrich the existing knowledge base by providing firsthand insights and how AI technologies are influencing consumer behavior and banking practices.

Purpose This systematic literature review (SLR) aims to critically analyze the current academic research on the adoption of artificial intelligence (AI) in supply chain management (SCM) and develop a theoretical framework and future research agenda. Design/methodology/approach Through a comprehensive review of 68 relevant papers, this study synthesizes the findings to identify key themes based on extended technology-organization-environment (TOE) theory. Findings This study analyzes AI integration in SCM based on the TOE framework, identifying drivers (technological, organizational, environmental and human), barriers (technical, organizational, economic and human) and outcomes (operational, environmental, social and economic) of AI adoption. It emphasizes AI's potential in improving SCM practices like resilience, process improvement and sustainable operations, contributing to better decision-making, efficiency and sustainable practices. The study also provided a novel framework that offers insights for strategic AI integration in SCM, aiding policymakers and managers in understanding and leveraging AI's multifaceted impact. Originality/value The originality of the study lies in the development of a theoretical framework that not only elucidates the drivers and barriers of AI in SCM but also maps the operational, financial, environmental and social outcomes of AI-enabled practices. This framework serves as a novel tool for policymakers and managers, offering specific, actionable insights for the strategic integration of AI in supply chains (SCs). Furthermore, the study's value is underscored by its potential to guide policy formulation and managerial decision-making, with a focus on optimizing SC efficiency, sustainability and resilience through AI adoption.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

This review draws on a systematic literature review and bibliometric analysis to examine how Digital Twins (DTs), Extended Reality (XR), and Artificial Intelligence (AI) support the reconfiguration of Cyber–Physical Systems (CPSs) in modern manufacturing. The review aims to provide an updated overview of these technologies’ roles in CPS reconfiguration, summarize best practices, and suggest future research directions. In a two-phase process, we first analyzed related work to assess the current state of assisted manufacturing reconfiguration and identify gaps in existing reviews. Based on these insights, an adapted PRISMA methodology was applied to screen 165 articles from the Scopus and Web of Science databases, focusing on those published between 2019 and 2025 addressing DT, XR, and AI integration in Reconfigurable Manufacturing Systems (RMSs). After applying the exclusion criteria, 38 articles were selected for final analysis. The findings highlight the individual and combined impact of DTs, XR, and AI on reconfiguration processes. DTs notably reduce reconfiguration time and improve system availability, AI enhances decision-making, and XR improves human–machine interactions. Despite these advancements, a research gap exists regarding the combined application of these technologies, indicating potential areas for future exploration. The reviewed studies recognized limitations, especially due to diverse study designs and methodologies that may introduce risks of bias, yet the review offers insight into the current DT, XR, and AI landscape in RMS and suggests areas for future research.

The rapid advancement of technology, particularly artificial intelligence (AI), is transforming the world of management. This chapter delves into the futuristic trends in management, focusing on the integration of AI into various managerial aspects, its potential benefits, challenges, and strategies for successful adoption. AI-driven decision-making aids managers in data-driven processes, identifying patterns, trends, and insights through AI algorithms. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment, enhanced employee experience through AI-driven content, and AI-driven operations management. AI-driven supply chain optimization, process automation, and customer relationship management (CRM) are also explored. However, ethical implications of AI in management include addressing biases and fairness concerns, ensuring transparency and accountability, and navigating privacy and data security challenges. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise, fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement and promoting AI-human synergy. The chapter emphasizes the importance of fostering a learning mindset, encouraging continuous skill development, and mitigating potential job displacement. AI's integration into management practices has the potential to revolutionize various aspects of organizations, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee productivity. AI-driven tools enable efficient data management, identification of trends, correlations, and actionable insights from complex datasets. They can optimize financial resources, human capital, or physical assets, enhance operational efficiency, and provide personalized customer experiences. AI-driven decision-making aids managers in making informed decisions by leveraging capabilities such as data processing, pattern recognition, real-time insights, and predictive analytics. These capabilities help managers segment customers, analyze market trends, and predict future demand. AI also enhances predictive and prescriptive analytics by providing recommendations for optimal performance. AI's impact on human resources management includes AI-enabled talent acquisition and recruitment. AI-powered tools can streamline the traditional recruitment process by scanning online platforms, screening resumes, and conducting assessments and skill evaluations. AI helps mitigate bias in the hiring process through blind hiring, objective evaluation, and data-driven decisions. However, challenges and ethical considerations include data privacy and security, transparency and explainability, algorithmic bias, and candidate experience. In conclusion, AI's integration into management practices has the potential to revolutionize various areas, including data management, resource allocation, personalization, risk assessment, supply chain management, and employee performance. However, HR professionals must address ethical concerns such as data privacy, transparency, algorithmic bias, and candidate experience to ensure the success and competitiveness of AI-based recruitment. AI can significantly enhance the employee experience by providing personalized learning and development programs, enhancing performance evaluations, building employee engagement strategies, and predicting potential attrition risks. AI-powered tools can assess employees' existing skills, knowledge gaps, and learning preferences, enabling the creation of personalized development plans. AI-driven learning platforms can adjust the difficulty and content of training materials based on individual progress, fostering a culture of continuous learning. AI-based performance evaluations can provide valuable insights to enhance the performance evaluation process, offering real-time tracking and 360-degree feedback analysis. AI-driven employee engagement strategies can bolster employee satisfaction, tailoring benefits, rewards, and recognition programs to individual needs. Predictive attrition analysis and chatbots for employee support can also help retain valuable employees. AI can also optimize supply chain operations by accurately predicting demand and managing inventory. AI-driven systems can dynamically adjust inventory levels based on real-time demand fluctuations and lead times, maintaining optimal resource utilization and minimizing excess inventory. AI-powered logistics and route optimization can revolutionize logistics and transportation management by optimizing routes and enhancing overall efficiency. AI-driven process automation can transform various business operations by identifying suitable processes, analyzing high-volume data processing, streamlining workflow and resource allocation, and addressing workforce concerns and upskilling needs amid automation. By embracing AI in operations management, businesses can achieve unprecedented levels of efficiency, resilience, and responsiveness to market demands. In conclusion, AI can transform the employee experience, drive productivity, and foster a dynamic workforce. However, careful consideration of ethical principles and continuous monitoring of AI systems are essential for responsible implementation. AI can significantly improve customer satisfaction and loyalty through real-time feedback analysis, chat sentiment analysis, and personalized loyalty programs. AI-powered customer support includes chatbots and virtual assistants, which provide instant and round-the-clock assistance. AI platforms, such as Natural Language Processing (NLP), enable chatbots to understand and respond to customer queries in a conversational manner. However, AI integration also presents challenges, such as addressing biases and fairness concerns in AI algorithms, ensuring transparency and accountability in AI systems, and managing privacy and data security challenges. Adhering to data protection regulations and implementing robust data governance frameworks are essential for safeguarding customer and organizational data. Managing the human-AI collaboration involves building a culture that embraces AI while valuing human expertise. This can be achieved through change management and training, promoting a collaborative environment, fostering a learning mindset, upskilling and reskilling initiatives, emphasizing creativity and critical thinking, and mitigating potential job displacement and promoting AI-human synergy. In conclusion, embracing AI in management offers numerous opportunities to enhance organizational efficiency, productivity, and competitiveness. It is crucial for management professionals and corporate leaders to understand and harness the power of AI responsibly. By being proactive in addressing challenges and aligning AI initiatives with organizational values, businesses can leverage futuristic trends in management to thrive in the dynamic and ever-evolving landscape.

The integration of Artificial Intelligence (AI) techniques into various domains has revolutionized numerous industries, and Supply Chain Management (SCM) is no exception. This paper addresses the challenges encountered in SCM and the development of AI solutions within this context. Specifically, we focus on the application of AI in optimizing supply chain planning tasks. This includes forecasting demand, availability and feasibility checks for customer orders, supply chain network design and information flow inside the supply chain planning processes. However, the successful implementation of AI in SCM requires a deep understanding of both the domain-specific challenges and the capabilities and limitations of AI technologies. Thus, this paper proposes an overarching approach that facilitates collaboration between domain experts in SCM and AI experts, enabling them to jointly develop effective solutions.The paper begins by outlining the key challenges faced by SCM professionals, including demand volatility, complexities in inventory management, and dynamic market conditions. Subsequently, it delves into the challenges associated with developing AI solutions for SCM, including data quality, interpretability, and model transparency. To address these challenges, the proposed approach promotes close collaboration and knowledge exchange between SCM and AI experts. By leveraging the domain knowledge and experience of SCM experts, AI experts can better understand the special issues of SCM processes and tailor AI techniques to suit specific needs. In turn, SCM experts can gain insights into the capabilities and limitations of AI, allowing them to make informed decisions regarding the adoption and integration of AI in their supply chain planning operations. Furthermore, the paper discusses the importance of establishing a multidisciplinary team comprising experts from the fields of SCM, AI, and IT. This team-based approach fosters a holistic understanding of SCM challenges and ensures the development of AI solutions that align with business goals and practical constraints.In conclusion, this paper highlights the challenges in combining SCM and AI and proposes a collaborative approach to address these challenges effectively. By leveraging the expertise of both domain and AI experts, organizations can develop tailored AI solutions that enhance supply chain planning, improve decision-making processes, and drive competitive advantage. The proposed approach contributes to the successful integration of AI in SCM, ultimately leading to more efficient and resilient supply chains in the era of artificial intelligence.

Indonesia's healthcare system continues to face significant challenges in delivering equitable services across diverse and remote regions. The digital transformation of healthcare—through the integration of Artificial Intelligence (AI), big data, and telemedicine—offers promising solutions to overcome disparities in access, infrastructure, and service delivery. This study aims to comprehensively analyze global and national research trends related to the digital transformation of healthcare using a Systematic Literature Review (SLR) approach, supported by bibliometric analysis through the VOSviewer software. Following the PRISMA protocol, a total of 30 relevant articles published between 2020 and 2025 were identified and analyzed. The network and overlay visualizations generated reveal four major thematic clusters: digital transformation and service quality, big data and pandemic response, AI and data privacy, and community engagement in digital health services. Overlay visualization also shows a clear shift in research focus—from early pandemic responses toward system optimization, ethical governance, and technological inclusivity in recent years. The findings highlight that digital healthcare transformation has increasingly evolved from emergency responses to COVID-19 into a strategic framework for long-term system improvement. Moreover, AI and big data have played pivotal roles in enhancing diagnostics, predicting outbreaks, and improving resource allocation. However, concerns related to privacy, digital literacy, and unequal technological access remain prominent. The study concludes that the integration of AI, big data, and telemedicine not only enhances healthcare efficiency but also requires strong regulatory frameworks, infrastructure readiness, and public engagement. Future research should incorporate co-citation and cross-country comparative analyses to enrich the understanding of digital health transformation in a global context, especially in low- and middle-income countries like Indonesia.

This study examines the integration of Artificial Intelligence (AI) in supply chain management (SCM) during the transition from Industry 4.0 to Industry 6.0. The focus is on improving operational efficiency, promoting human-centric collaboration, and advancing sustainability within supply chains. As industries progress, the need to incorporate AI technologies that improve decision-making and operational resilience while ensuring sustainable practices becomes increasingly critical. This systematic review aims to explore how AI is transforming SCM through these industrial transitions. Utilising the PRISMA framework, a systematic review was conducted to gather and analyse relevant literature published between 2010 and 2023. A comprehensive search of databases including Web of Science, Scopus, IEEE Xplore, Google Scholar, and ScienceDirect was performed. The review involved rigorous screening for eligibility and thematic analysis using Atlas-ti software to identify key themes and patterns related to AI integration in SCM. The findings indicate that AI integration significantly improves SCM by improving demand forecasting, inventory management, and overall decision-making capabilities. Industry 5.0 focuses on human-AI collaboration, improving customisation and problem-solving. AI technologies also contribute to sustainability by optimising resource utilisation and reducing environmental impacts. However, challenges such as cybersecurity risks and workforce skill gaps need to be addressed to fully leverage AI's potential. Integrating AI in SCM not only improves operational efficiency and sustainability but also promotes resilience against disruptions. The insights from this review offer valuable guidance for both academics and practitioners aiming to optimise supply chain operations through AI technologies from Industry 4.0 to Industry 6.0. The study underlines the importance of a balanced approach that integrates technological developments with human-centric and sustainable practices.

Context: Artificial intelligence (AI) has emerged as a promising tool for improving cancer diagnosis, treatment, and patient outcomes in recent years. Evidence Acquisition: This systematic literature review explores AI's potential role in cancer therapeutics. PubMed, Scopus, and Web of Science databases were searched using the keywords “artificial intelligence,” AI, “machine learning,” cancer, neoplasms, therapy, and therapeutic from October to December 2023. Original English articles that focused on the use of AI in cancer treatment and whose full texts were available were included in the present study. Finally, 24 articles met the inclusion criteria for the present study. Results: The analysis of the included articles showed that the role of AI in cancer treatment has been focused on three areas: Predicting treatment response, precision medicine, and predicting cancer treatment adverse events. The present review also identifies challenges and limitations in the implementation of AI in cancer therapeutics, such as issues related to data collection, data analysis, and the quality of data. Conclusions: The integration of AI in cancer care necessitates a strong health policy infrastructure. This infrastructure must prioritize ethical concerns such as confidentiality of information, neutrality of algorithms, and access, in addition to responsible AI development, transparency, and patient safety. Overall, this review provides critical insights into the current state of AI in the clinical settings of cancer treatment.

Digital transformation (or digitalization) is the process of continuous further development of digital technologies (such as smart devices, cloud services, and Big Data) that have a lasting impact on our economy and society. In this manner, digitalization is a huge driver for permanent change, even in the field of Sustainable Urban Development. In the wake of digitalization, expectations are changing, placing pressure at the societal level on the design and development of smart environments for everything that means Sustainable Urban Development. In this sense, the solution is the integration of Artificial Intelligence into Sustainable Urban Development, because technology can simplify people’s lives. The aim of this paper is to ascertain which Sustainable Urban Development dimensions are taken into account when integrating Artificial Intelligence and what results can be achieved. These questions formed the basic framework for this research article. In order to make the current state of Artificial Intelligence in Sustainable Urban Development as a snapshot visible, a systematic review of the current literature between 2012 and 2022 was conducted. The data were collected and analyzed using PRISMA. Based on the studies identified, we found a significant growth in studies, starting in 2018, and that Artificial Intelligence applications refer to the Sustainable Urban Development dimensions of environmental protection, economic development, social justice and equity, culture, and governance. The used Artificial Intelligence techniques in Sustainable Urban Development cover a broad field of Artificial Intelligence, such as Artificial Intelligence in general, Machine Learning, Deep Learning, Artificial Neuronal Networks, Operations Research, Predictive Analytics, and Data Mining. However, with the integration of Artificial Intelligence in Sustainable Urban Development, challenges are marked out. These include responsible municipal policies, awareness of data quality, privacy and data security, the formation of partnerships among stakeholders (e.g., local citizens, civil society, industry, and various levels of government), and transparency and traceability in the implementation and rollout of Artificial Intelligence. A first step was taken towards providing an overview of the possible applications of Artificial Intelligence in Sustainable Urban Development. It was clearly shown that Artificial Intelligence is also gaining ground in this sector.