AI Augments Musical Creativity: But Does the Music Swing?

Purpose This study examines how secondary school administrators can lead ethical artificial intelligence (AI) integration within environments demanding technological innovation and educational value preservation. Design/methodology/approach The study conducted a scoping review of literature (2018–2025) to analyze administrative functions across four established leadership dimensions: instructional, managerial, strategic, and relational. Sources were obtained from academic databases and grey literature, with 21 sources selected based on relevance to secondary education and administrative practice. Analysis is grounded in foundational leadership scholarship while examining contemporary AI integration challenges. Findings The analysis reveals a misalignment between AI's most frequent use (relational leadership functions) and where it may be most appropriately suited (managerial and strategic functions). AI integration creates distinct opportunities and risks across each leadership dimension, with equity concerns emerging consistently. Communication represents the primary AI use, despite being the most fundamentally human aspect of educational leadership. Cognitive offloading risks emerge when administrators delegate critical thinking tasks to AI systems, potentially attenuating leadership capabilities essential for educational effectiveness. Research limitations/implications This study relies on secondary data collection and English-language sources, creating Western-centric bias and limiting generalizability beyond North American contexts. The corpus of 21 sources reflects the nascent research state in this emerging field. The rapid evolution of AI capabilities means current findings may prove transitional as technology advances. Future empirical research should examine long-term cognitive effects of AI reliance on administrators, stakeholder trust implications when AI-mediated communications are detected, differential equity impacts across diverse school communities, cross-cultural implementation patterns, and effectiveness of hybrid governance approaches for AI integration in educational leadership. Practical implications Findings support implementing hybrid governance models that combine regulatory oversight with participatory decision-making between administrators and stakeholders. Professional development programs must balance AI literacy training with preserving human capabilities essential for authentic educational leadership. Administrator preparation programs require redesign to address cognitive offloading risks while maintaining relationship-building and cultural competence development. Educational leaders should prioritize AI applications in managerial and strategic functions while preserving human judgment in relational leadership contexts. Policy frameworks must address equity concerns and provide guidance for schools serving vulnerable populations who currently receive less AI implementation support. Social implications AI implementation without critical examination risks amplifying existing educational inequities, particularly affecting Indigenous, newcomer, and racialized communities. Democratic participation in AI boundary-setting becomes essential for maintaining institutional trust and stakeholder engagement. The misalignment between AI deployment and appropriate applications threatens the relational foundations of effective educational leadership. Originality/value The study provides the first systematic examination of AI integration across established educational leadership dimensions in secondary school contexts, addressing a critical research gap given that nearly 60% of K-12 principals use AI tools while fewer than 10% of schools have established AI policies.

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 article "Embracing Real AI: A Call to Action for Medical Physicists in Healthcare" urges medical physicists to prepare for the integration of artificial intelligence (AI) into healthcare practices, emphasizing their pivotal role in adapting to technological advancements. The authors advocate for embracing AI through advocacy, broadening perspectives, and enhancing coordination and communication. They propose an ABC strategy focusing on increasing educational initiatives, fostering interdisciplinary collaboration, and creating team collaboration to facilitate AI integration. The commentary highlights AI's potential in enhancing diagnostics, personalizing medicine, and automating routine tasks while addressing challenges such as data sharing and the role of federated learning. The article calls for medical physicists to lead in embracing AI, emphasizing continuous learning and collaboration to leverage its potential for improving healthcare and patient care. Medical physicists have consistently demonstrated strong interest in developing proficiency in the adoption of new technological advancements. The roots of the profession come from the radiation sciences, including radiation protection, radiation therapy, diagnostic imaging, and nuclear medicine.1 As science and technology continued to evolve, medical physicists' roles have extended into other non-radiation domains, such as non-ionizing-radiation-based imaging (ultrasound and magnetic resonance), molecular imaging, computer aided diagnosis (CAD), information technologies, and data science.2 In addition, medical physicists gradually have adopted increasingly more active roles in ensuring the professional education of other radiology/radiation oncology team members, maintaining high quality standards via quality assurance (QA) methods. They also play a major role in advising the hospital management on medical devices and software acquisition. The continuing expansion of these roles and responsibilities has put medical physicists on the forefront of embracing emerging technologies, making the profession one of the most technical and versatile in healthcare settings. Currently, as our field grows in importance, we medical physicists seek to continue to engage in significant ways to for increased contributions and roles in human health. This commentary/opinion urges medical physicists to prepare for their expanding roles in the field of AI and its implementation and oversight in clinical practice. Medical physicists must embrace "Real AI" to help integrate AI into healthcare practices. Conceptually we advocate for a strategy that involves Real AI through advocacy, broadening, and enhancing coordination/communication (an ABC strategy). In our current and future work medical physicists will use AI to automate routine tasks, allowing medical physicists to focus on more complex tasks. Furthermore, Medical Physics will use AI to enhance efficiency, safety, diagnostic and therapeutic applications, and for personalized medicine. However, as we have done in the past with other complex concepts (such as radiation), medical physicists need to be prepared for the potential risks and ethical dilemmas associated with AI, such as bias and lack of transparency. It will be important that Medical Physicists prepare for the rapidly changing AI landscape, and continue learning, gain hands-on experience, and collaborate with other AI experts in the healthcare environment. This paper aligns with the already approved guidance document developed by the AAPM in conjunction with International Atomic Energy Agency (IAEA)3 that discusses how medical physicists can ensure the effective implementation and management of AI systems. It is crucial for the Clinical Quality Management Program (CQMP) personnel to receive regular training and updates on relevant guidelines and legislation. Clear communication channels should be established with IT experts, vendors, and other stakeholders for smooth coordination.4 Comprehensive documentation should be developed to ensure compliance with contractual obligations and guidelines. The clinical team should be involved in acceptance testing and discussions, depending on the clinical purpose of the AI system.4 Protocols for data collection and curation should be established, along with the development of standardized validation datasets for performance evaluation.4 A system for monitoring updates to AI systems and models should be implemented, with the CQMP leading new acceptance/commissioning rounds for any updates. Lastly, mechanisms for continuous evaluation and improvement of the CQMP processes should be established, which could involve regular audits, feedback mechanisms from end-users, and incorporating lessons learned from previous rounds.4 Nowadays, major healthcare systems in the US consider their data as immensely valuable assets that require rigorous protection to ensure Health Insurance Portability and Accountability Act (HIPAA) compliance, as well as intellectual property considerations. It can be very difficult for researchers to share clinical data with vendors for development purposes without a significant return being specified to the institution, such as joint intellectual property or substantial grant funding. Instead, these healthcare systems encourage their researchers to commercialize their findings independently, allowing the institution to retain full rights to intellectual property. That said, the realization of federated learning would be a significant advancement. To achieve this, a powerful pre-trained model that would be adaptable to operation on different scales and in various clinical scenarios is necessary. It is plausible that local adaptation may not require substantial computing power or AI expertise. This concept is particularly intriguing and could be beneficial to smaller centers and clinics in underserved areas. However, the primary challenge is the cost. As we become more reliant on AI systems like OpenAI's ChatGPT or Google Gemini, we often overlook the fact that these conveniences come with a hefty price tag, costing billions of dollars to develop and maintain.5 As medical physicists we and other healthcare professionals can anticipate that AI will significantly transform healthcare, improving efficiency, accuracy, and the level of detail that can be extracted from imaging, and methods of therapy. These technological advancements are expected to bring immense value to the field, offering a new horizon in diagnostic and therapeutic capabilities. Yet, we also must recognize that it also introduces potential significant risks and ethical dilemmas. One of the primary concerns is the possibility of bias in AI, which can stem from the training data, the algorithms, or their application, leading to potentially detrimental effects on patient care. As medical physicists, we should acknowledge that the complexity and lack of transparency in AI decision-making processes present obstacles in terms of accountability and rectifying errors and requires greater oversight and responsibility. The integration of AI also has great capacity in redefining the role of medical physicists, impacting education and employment within the field. Addressing these issues necessitates the creation of ethical standards for AI in healthcare, emphasizing transparency, responsibility, and equity, with contributions from diverse stakeholders, including patients, medical professionals, and ethicists.6 Such measures are crucial to ensure the responsible utilization of AI in healthcare, and ultimately serve the best interests of patients and society. We anticipate that continued guidance from our professional societies will be helpful as our collective communities develop methods and approaches that help us learn, adopt, and employ AI responsibly. Advocacy: increase educational initiative, public awareness, and recommending processes at all levels of the clinical workforce, as well as patient engagement. Broadening Perspectives: encourage Interdisciplinary Collaborations that allow medical physicists to work with professionals from other disciplines such as computer science, data science, and biomedical engineering, to gain insights into different perspectives on AI applications in healthcare. This enables medical physicists to provide continuing education and connect the community with research opportunities. Improving Coordination and Communication through creating team collaboration: enhance communication with healthcare professionals, administrators, and patients by clearly defining and articulating the role of medical physicists in AI applications. Promote the sharing of knowledge, as exemplified by creating data repositories through contributions, to further creating the foundation of our understanding and application of AI in the field. We consider the concept of Real AI in our context to be aimed at providing and/or qualifying a ready AI product that has undergone a rigorous QA process, that is free of false additives and biases, with data carefully curated to represent the demographics and be attuned to the needs of the clinic, sourced with proper ingredients, and abiding by laws and regulations that can ensure the product serves the common health needs of patients and benefits the public's interest. What AI 'is' and what it 'is not' is a complex topic that warrants further exploration and understanding, but one vital for comprehension of what utility AI can fulfill in the clinical process, what its advantages and limitations are, and how it can be curated to perform in the clinical scenarios relevant to a particular radiology/radiation oncology practice. Multiple data-analysis algorithms have been created over the course of years, and not all of them qualify as AI.7 What distinction(s) lie in what constitutes AI? One possible interpretation is that AI is a system that can adapt to new data, or a system that generates insights driven by data. AI systems are designed to "learn" and adapt to new data and be stable over the course of introducing data perturbations or employ model adaptation mechanisms. AI systems can adjust the underlying data-processing mechanisms based on the input they receive, which allows them to improve their performance and make more accurate predictions or decisions over time. This is often achieved through techniques such as machine learning, where algorithms are trained on a dataset and then used to make predictions or decisions without being explicitly programed to perform the task.8 Understanding how such datasets are selected, what data needs to be fed into AI model to achieve desired results, and how to prevent common pitfalls and ethical conundrums associated with the use of AI models requires additional training that might yet be lacking in the traditional training of the radiology/radiation oncology adjacent specialists. The scope of involvement of each member of the team when it comes to AI integration into the clinic continues to be determined as the field rapidly evolves. When it comes to the role of medical physicists in conjunction with AI, an open discussion of the exact responsibilities is still ongoing, and feedback is encouraged from all the members of the community. So, what can medical physicists do? They can use AI to enhance quality improvement and safety by analyzing medical data to identify trends, patterns, and outliers.9 This can lead to the identification of areas for improvement or potential safety hazards and help them enter the realm of Responsible AI. AI can also improve diagnostic and therapeutic techniques by enhancing the quality of medical imaging and automating image interpretation.10 Furthermore, AI can help in integrating diagnostics, personalized medicine, and theragnostics by analyzing large datasets to tailor treatment plans to individual patients.11 This can lead to more effective and personalized care. AI can also automate routine tasks in medical physics, such as treatment planning and QA processes, leading to increased efficiency.12 Lastly, AI techniques like machine learning and deep learning can be leveraged for research and development to analyze complex datasets, discover patterns, and develop innovative techniques for disease detection, treatment, and monitoring.13 Whether it involves developing AI-driven solutions like automated segmentation, dose calculations, addressing intricate problems in the clinic, or potentially even contributing to open-source AI initiatives, such activities will empower medical physicists to enhance their skills and make tangible contributions to the advancement of healthcare. Embracing AI not only fosters a sense of accomplishment but also opens doors to the world of `automation' and scaling that will pervade all technologies of the future. The AHAIBC committee is at the center of bringing the medical physicist forward by developing curriculum concepts, bootcamps, and engendering engagement for our society. Integration of AI into the realm of medical physics education is critical, especially considering the potential significance of incorrect AI usage or misapplication. The physicist is responsible for installing and commissioning the AI software, ensuring the modeling is not biased, performing continuing QA on the hospital data and processes, and establishing efficient resource management. Embracing education in AI offers new benefits for medical physicists as it is already revolutionizing various industries and professional practices and we need to be equally prepared. One way to engage and prepare healthcare professionals for the upcoming AI wave is to start with the roots of quality safety and assurance. To do this, we should enable a comprehensive QA program that encompasses all clinical operations related to medical fields including radiology, nuclear medicine, and radiation oncology. Ensuring the safe operation of hardware, software, clinical operation processes and machinery is of utmost importance and one of the most crucial responsibilities of a medical physicist. A Real AI approach can be highly beneficial in achieving the goal of safe clinical implementation. Understanding the potential and limitations of AI serves as a cornerstone for fostering engagement not only within our profession but with other healthcare providers. Continuous learning and participation in hands-on experience are essential components for navigating the complexities of AI applications within healthcare. Collaboration, networking, and exploring AI's purpose and impact are equally vital in this journey. Additionally, some physicists may choose personal projects, embracing challenges in small groups, and actively contributing to AI-focused teams to amplify the motivation and expertise of our field. Insights through personal and collaborative opportunities ultimately provide for and encourage professional growth and innovation within our medical physics field. Some medical physicists may be able to attend specialty meetings and conferences dedicated to AI which further enriches their knowledge base and provides them avenues for fruitful collaboration. There are successful educational programs such as the Radiological Society of North America Artificial Intelligence (RSNA AI)-certificate program.14 Interdisciplinary cooperation and inter-institutional collaboration for AI experts is of paramount importance for integrating AI into medical physicists' practice on a larger scale, and mechanisms enabling this collaboration should be provided to the community. In summary, the authors believe that being prepared for and embracing the changes that AI is already bringing at the current time will benefit our community, healthcare, patient care, and society at large immediately and for the future. We are at a critical juncture, which can be considered a fourth industrial revolution, where AI and automation are applied more broadly. Medical physicists have a pivotal role to play in this revolution. We need to position ourselves at the forefront of 'Real AI' and lead the charge in this exciting new era. It is time for action, and we can take the first steps with potentially just a few ABCs. All authors contributed their efforts in writing and editing this call for action. ChatGPT search engine has been utilized to provide additional background to the subject of matter for illustrative purposes. The authors appreciate members of the Ad. The authors declare no conflicts of interest. The content for this call for action has been edited with the help of large language models ChatGPT and Google NotebookLM.

In the realm of Artificial Intelligence (AI) integration and project management efficiency (PME), a comprehensive research study has been conducted, primarily focusing on various industries in Pakistan. The intricate interplay between AI integration, team proficiency in AI, organizational support for AI technologies, and PME forms the crux of this investigation. The theoretical underpinning of this research has been rooted in the Resource-Based View (RBV) theory. Data for this study have been collected through a structured questionnaire survey, targeting a diverse group comprising project managers, IT managers, senior executives, and other key personnel engaged in AI-driven decision support systems. The research has revealed significant positive correlations between the integration of AI, team proficiency in AI, organizational support for these technologies, and PME. These findings highlight the crucial role these elements play in enhancing project outcomes. This study, by uncovering these relationships, offers valuable insights for organizations aiming to optimize their project management practices, especially in emerging economies like Pakistan. It contributes to the existing body of knowledge by providing a nuanced understanding of how AI integration can be leveraged to enhance project management efficiency. Furthermore, the study discusses broader implications for policy and suggests directions for future research, emphasizing the strategic importance of nurturing AI competencies and fostering organizational support for AI technologies to realize enhanced project management outcomes.

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 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.

Purpose This paper aims to explore the paradigm shift in leadership and strategic management driven by the integration of responsible artificial intelligence (AI) in healthcare. It explores the evolving role of leadership in adapting to AI technologies while ensuring ethical governance, transparency and accountability in healthcare decision-making. Design/methodology/approach This study conducts a comprehensive review of current literature, case studies and industry reports to evaluate the implications of responsible AI adoption in healthcare leadership. It focuses on key areas such as AI-driven decision-making, resource optimisation, crisis management and patient care, while also addressing challenges in integrating AI technologies effectively. Findings The integration of AI in healthcare is transforming leadership from traditional, experience-based decision-making to data-driven, AI-enhanced strategies. Responsible leadership emphasises addressing ethical concerns such as bias, transparency and accountability. AI technologies improve resource allocation, crisis management and patient care, but challenges such as workforce resistance and the need for upskilling healthcare professionals remain. Practical implications Healthcare leaders must adopt a responsible leadership framework that balances AI’s potential with ethical and human-centred care principles. Recommendations include developing AI literacy programmes for healthcare professionals, ensuring inclusivity in AI algorithms and establishing governance policies that promote transparency and accountability in AI applications. Originality/value This paper provides a critical, forward-looking perspective on how responsible AI can drive a paradigm shift in healthcare leadership. It offers novel insights into the integration of AI within healthcare organisations, emphasising the need for leadership that prioritises ethical AI usage and promotes patient well-being in a rapidly evolving digital landscape.

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.

Purpose This study aims to investigate how artificial intelligence (AI) integration in service delivery influences sustainability and business performance in small- and medium-sized enterprises (SMEs) across diverse sectors. It further examines the moderating roles of stakeholder engagement and adoption barriers and the mediating role of sustainability performance in the AI–business performance relationship. Design/methodology/approach A mixed-methods approach was used, combining survey data from 428 firms across four sectors with qualitative insights from 20 semistructured interviews. Partial least squares structural equation modeling tested the hypothesized relationships, while thematic analysis provided contextual understanding of implementation challenges and success factors. Findings AI integration significantly improves both sustainability and business performance. Stakeholder engagement strengthens the positive effect of AI on sustainability outcomes, while adoption barriers weaken AI’s impact on business performance. Sustainability partially mediates the relationship between AI integration and business outcomes, underscoring its strategic role. Practical implications To maximize AI’s value, SMEs should adopt phased strategies, engage stakeholders proactively and address technological and organizational barriers. These actions enhance AI’s effectiveness in driving sustainable, competitive service delivery. Originality/value This study advances the AI literature by linking AI adoption to dual sustainability and business benefits while also incorporating the moderating effects of engagement and barriers – an area previously underexplored. It offers a sector-sensitive, empirically grounded model of AI-enabled transformation in SMEs.

In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios.

This paper explores the relationship between Artificial Intelligence (AI) integration in the workplace, cultural orientation, and its impact on job autonomy and creative self-efficacy. Our study employs a mixed-method experimental design across 480 individuals from different cultural backgrounds, specifically individualistic (United Kingdom) and collectivistic (Mexico) cultures. We evaluate how they perceive AI's role in their professional lives. We focus on two key aspects: job autonomy, the level of control and discretion employees have over their tasks, and creative self-efficacy, the confidence in one's ability to generate innovative ideas. Our findings revealed a significant increase in job autonomy following AI integration across all participants. Interestingly, this increase was more pronounced in the individualistic participants. Regarding creative self-efficacy, we found gender-specific impacts, with male participants experiencing a decrease, contrary to our expectations. Finally, our results supported the hypothesis that cultural orientation influences perceptions of AI, with collectivistic participants being more receptive to AI integration. These findings have significant implications for organizations integrating AI in multicultural environments. They highlight the importance of considering cultural differences in AI deployment strategies and suggest a need for culturally sensitive AI systems. The study also opens avenues for future research, particularly in exploring the role of other cultural dimensions, conducting longitudinal studies, and investigating ethical and bias-related aspects of AI in the workplace.

The integration of artificial intelligence into the logistics industry is a rapidly evolving field with the potential to revolutionize the way goods are transported and managed. Artificial intelligence can be used to optimize a wide range of logistics processes, from demand forecasting and route planning to warehouse management and customer service. However, the integration of artificial intelligence also raises a number of technical and ethical issues that need to be addressed to ensure its successful implementation. Choosing the right artificial intelligence algorithms for specific logistics tasks is crucial to ensure their efficiency and accuracy. This requires careful consideration of factors such as data type, task complexity, and desired performance metrics. The growing amount of data collected and processed by artificial intelligence systems raises concerns about data security and privacy. Companies need to implement robust security measures to protect sensitive data from unauthorized access, breaches, and misuse. The use of artificial intelligence in logistics raises ethical issues related to bias, transparency, and accountability. Artificial intelligence algorithms should be developed and used fairly, transparently, and with respect for the right to privacy and in compliance with all relevant laws and regulations. In order to eliminate or prevent these problems, recommendations for the effective implementation of artificial intelligence in the logistics sector have been developed and formulated. They include aspects that need to be addressed in the first place when developing mechanisms for automating logistics processes. The integration of artificial intelligence into logistics offers significant opportunities to increase efficiency, reduce costs and improve customer service. However, it is crucial to address the technical and ethical challenges associated with artificial intelligence integration to ensure that it is used responsibly and beneficially. By following the recommendations, logistics companies can successfully use artificial intelligence to transform their operations and achieve their strategic goals.

<div class="section abstract"><div class="htmlview paragraph">The integration of ergonomics and artificial intelligence (AI) in the automotive industry has the potential to revolutionize the way how vehicles are designed, manufactured and used. The aim of this article is to review the recent literature on the subject and discuss the opportunities and challenges presented by the integration of these two fields. The paper begins defining the ergonomics and the AI and providing an overview of their respective roles in the automotive industry. It then examines the benefits of the integration of ergonomics and AI in the automotive industry, including the optimization of vehicle design and manufacturing process. The enhancement of the driver experience, and improvement of safety accessibility, and customization, however, the integration of ergonomics and AI in the automotive industry also presents challenges, including ethical and legal considerations, data privacy, liability, and the impact on the employment in the automotive industry. The paper reviews research on these challenges and suggests that the development of international standards for the integration of AI in the vehicles may be necessary to ensure that AI systems in vehicle are secure, highlighting the need for future research to explore the integration of ergonomic and AI in the automotive industry. Future research should focus and addressing the ethical, legal, and societal implications of the AI in vehicles, as well as exploring new opportunities for the use of AI in design, manufacturing, and use of vehicles in overall, the integration of ergonomics and AI in the automotive industry has the potential to significantly improve the design and manufacturing of vehicles, as well as enhance the driving experience for users. However, the integration of these two fields also poses challenges that must be addressed, including ethical concerns, legal considerations, and the employment in the automotive industry. By working to overcome these challenges, we ensure that benefits of ergonomics and AI in the automotive industry are fully realized while minimizing their potential negative impacts.</div></div>

This issue brings together the authors’ research, which reveals contemporary topics in the context of studying CI, which resonate with contemporary scientific development trends and dynamics of change, and relate to recent discoveries in the field. Artificial intelligence (AI) and advanced data analytics are increasingly shaping how organizations generate intelligence and support strategic decision-making. In contemporary management research, strategic intelligence is no longer viewed solely as information gathering but as an integrative capability that combines analytical technologies, knowledge management, and strategic foresight. The integration of AI into strategic intelligence systems enhances the ability of organizations to process complex data and extract meaningful insights. Consequently, AI-supported intelligence systems facilitate knowledge-driven decision-making and contribute to improved organizational performance (e.g., Maitra et al., 2025; Seremeti & Anastasiadou, 2025).Several theoretical perspectives help explain how the integration of strategic intelligence and AI contributes to organizational effectiveness. Decision Theory provides an important foundation by explaining how organizations evaluate alternatives and make choices under uncertainty. Empirical evidence indicates that AI-enabled decision systems can significantly increase decision accuracy while reducing decision-making time, thereby improving the efficiency of strategic processes (e.g., Sikhakolli et al., 2025; Seremeti & Anastasiadou, 2025). The successful adoption of AI technologies can also be understood through the Technology Acceptance Model (TAM), which emphasizes that perceived usefulness and ease of use influence the adoption of new technologies within organizations. Consequently, organizational readiness, digital capabilities, and user acceptance become key determinants of effective AI integration (Abuzaid, 2024; Maitra et al., 2025).Another important theoretical lens is Information Processing Theory, which highlights the role of organizational capacity to process and interpret information in complex environments. Modern organizations face increasing information overload due to the exponential growth of digital data. AI technologies expand organizational information-processing capacity by automating data analysis, integrating diverse data sources, and delivering real-time insights that support more informed and timely decisions. These capabilities significantly enhance the analytical foundation of strategic intelligence systems.From a dynamic perspective, Organizational Learning Theory emphasizes the role of knowledge creation, knowledge sharing, and continuous adaptation in shaping organizational performance. AI contributes to organizational learning by extracting insights from large datasets and supporting evidence-based strategy development. By enabling organizations to identify patterns, evaluate outcomes, and refine strategies, AI strengthens adaptive learning processes and promotes innovation-driven growth (Alami & Al-Masaeid, 2025; Sposato, 2025).AI-driven analytics enables organizations to process large volumes of data, identify patterns, and generate predictive insights that improve decision quality and strategic responsiveness (Vincent, 2021; Weiser & von Krogh, 2023). In highly dynamic contexts, AI-based modeling and scenario simulations help organizations anticipate environmental changes and reduce uncertainty in strategic planning. At the same time, research emphasizes that the most effective outcomes emerge when AI-generated insights are combined with human judgment and intuition in decision processes (Vincent, 2021). Consequently, AI-supported intelligence systems enhance strategic agility and strengthen organizations’ ability to respond to emerging challenges and opportunities in omplex environments (Weiser & von Krogh, 2023; Asmar & Al-Rob, 2024). Beyond theoretical explanations, AI-enabled intelligence systems improve knowledge-driven decision making by transforming data into action-able insights that support evidence-based stra-tegic choices (Sikhakolli et al., 2025; Abdeljaber et al., 2025). In addition, AI contributes to operational efficiency by automating analytical processes and optimizing resource utilization, allowing managers to focus on strategic and innovative activities (Okafor & Murphy, 2025). Research increasingly emphasizes the impor-tance of human–AI collaboration, where AI systems augment rather than replace managerial expertise. Human oversight remains essential for interpreting analytical outputs, ensuring ethical accountability, and aligning technological insights with organizational values and strategic objectives (Štrukelj & Dankova, 2025; Alami & Al-Masaeid, 2025). Future research should continue to explore hybrid intelligence models that combine technological capabilities with human expertise to support sustainable and responsible decision making in increasingly complex organizational environments.