The emergence of digital transformation has changed the business landscape for the foreseeable future. As scholars advance their understanding and digital transformation begins to gain maturity, it becomes necessary to develop a synthesis to create solid foundations. To do so, significant steps need to be taken to critically, rigorously, and transparently examine the existing literature. Therefore, this article uses a meta-review with the support of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocol. As a result, we identified six dimensions and seventeen categories related to digital transformation. The organizational, technological, and social dimensions are still pivotal in digital transformation, while two new dimensions (sustainability and smart cities) still need to be explored in the existing literature. The need to deepen knowledge in digital transformation and refine the dimensions found is of paramount importance, as it involves some complexity due to organizational dynamics and the development of new technologies. It was also possible to identify opportunities, challenges, and future directions.

Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success.

The transition to digital business systems has revolutionized organizational operations, driven by the integration of advanced technologies such as artificial intelligence (AI). This integration indicates a shift, redefining traditional practices and enhancing efficiency across diverse sectors such as finance, healthcare, and manufacturing. This study explores the impact of AI on auditing through a systematic literature review to develop a conceptual framework for auditing practices. The theoretical implications show the transformative role of AI in redefining auditors’ roles, shifting from retrospective examination to proactive real-time monitoring. Moreover, managerial contributions stress the benefits of AI integration, enabling informed decision-making in risk analysis, financial management, and regulatory compliance. Future research should explore AI’s influence on auditing efficiency, performance, regulatory challenges, and auditor adaptation. Overall, this study underlines the importance for organizations to embrace AI integration in auditing practices, fostering innovation, competitiveness, and resilience.