AI-Driven Transformation: Methodologies, Governance, and Strategic Imperatives

The integration of artificial intelligence (AI) in rural healthcare systems holds great promise for improving access to quality medical care and addressing healthcare disparities. However, numerous barriers must be overcome to successfully implement AI technologies in these settings. This paper explores the key challenges and potential solutions for implementing AI in rural healthcare systems, focusing on infrastructure, data privacy, internet connectivity, and workforce readiness. One of the primary barriers is the lack of infrastructure in rural areas. Many rural healthcare facilities are under-resourced, lacking the necessary hardware, software, and technical support to adopt AI solutions. Addressing this issue requires significant investment in healthcare infrastructure, including upgrading existing facilities and ensuring they are equipped with modern technology. Data privacy and security present another significant challenge. AI systems rely on vast amounts of patient data to function effectively, raising concerns about data confidentiality and protection. Implementing robust cybersecurity measures and developing clear data governance policies are essential to safeguard patient information and build trust in AI technologies. Internet connectivity is a crucial enabler for AI-driven healthcare, yet rural areas often suffer from unreliable or limited internet access. Solutions to this challenge include investing in broadband infrastructure, exploring alternative connectivity options such as satellite internet, and leveraging offline AI capabilities that can operate with minimal connectivity. Workforce readiness and acceptance of AI technology are also critical for successful implementation. Many rural healthcare providers may lack the training and expertise needed to utilize AI tools effectively. Comprehensive training programs, ongoing education, and support from technology providers can help bridge this knowledge gap and encourage the adoption of AI. In conclusion, while the integration of AI in rural healthcare systems faces significant barriers, targeted solutions can address these challenges. Investment in infrastructure, robust data privacy measures, improved internet connectivity, and comprehensive training programs are essential steps to enable the successful implementation of AI in rural healthcare. By overcoming these barriers, AI has the potential to transform rural healthcare delivery, improve patient outcomes, and reduce healthcare disparities. Collaborative efforts among government entities, healthcare providers, and technology companies will be crucial in realizing this potential and ensuring equitable access to advanced healthcare technologies in rural areas.

Artificial Intelligence (AI) is increasingly recognized as a transformative force in contemporary diplomacy, fulfilling a dual role as both an operational tool and a central subject of international discourse. On the one hand, AI serves as a powerful instrument within digital diplomacy, enabling states and international organizations to enhance the efficiency, scope, and agility of their diplomatic operations. On the other hand, as the focus of tech diplomacy, AI has become a key topic in international negotiations and norm-setting, shaping the global governance landscape. This study aims to develop a comprehensive conceptual framework for analyzing the dynamic intersection between diplomatic practice and AI, with a particular emphasis on how technological innovation is reconfiguring international policy, governance mechanisms, and patterns of global economic growth. Through a comparative theoretical analysis, this research investigates the multifaceted impact of AI on diplomatic communication, conflict prevention, and global coordination. It further explores how AI influences the evolution of international norms, the design of institutional structures, and the dynamics of geopolitical competition. In the context of digital diplomacy, AI-driven tools such as chatbots for consular assistance, predictive analytics for real-time crisis monitoring, and advanced disinformation detection systems—exemplified by Ukraine’s Osavul and NATO’s digital initiatives—significantly enhance the speed, reach, and responsiveness of diplomatic activities. These innovations not only streamline traditional diplomatic processes but also create new opportunities for creative statecraft and international engagement. Conversely, in the domain of tech diplomacy, AI has emerged as a central subject of multilateral negotiations and regulatory discussions. Conceptual frameworks presented in this study underscore AI’s dual function as both a geopolitical lever and a catalyst for international cooperation, necessitating robust regulatory alignment and the establishment of export controls on advanced AI technologies. The study identifies several critical challenges associated with the integration of AI into diplomacy, most notably the urgent need for global consensus on AI governance and the effective management of its strategic risks. The complexity of these challenges is compounded by the rapid pace of technological development and the divergent interests of state and non-state actors. In response, public-private partnerships (PPPs) are emerging as vital enablers, fostering collaboration across sectors, bridging knowledge gaps, and mobilizing resources for the responsible and inclusive development of AI on a global scale. PPPs play a crucial role in advancing standardization, policy harmonization, and the equitable distribution of AI benefits, thereby reinforcing the capacity of states and international organizations to address emerging challenges. Ultimately, AI is not only redefining the functions and practices of diplomacy but also transforming the broader landscape of international relations. By necessitating new frameworks for cooperation, regulation, and ethical oversight, AI is reshaping the foundations of global order and stability. The findings of this study highlight the strategic importance of aligning AI development with core values of global stability, equity, and shared prosperity. As AI continues to evolve, diplomatic efforts must adapt to ensure that technological progress serves the collective interests of the international community, fostering a future in which AI-driven diplomacy contributes to peace, security, and sustainable development.

As Artificial Intelligence (AI) systems increasingly permeate emotionally sensitive and caregiving domains—such as eldercare, education, therapy, and family life—there is a growing need to assess national readiness beyond infrastructure, research output, or innovation metrics. Existing AI-readiness indices often prioritize technical capacity while neglecting caregiving ethics, emotional integration, symbolic trust, and cultural adaptability. Simultaneously, many national AI policies and strategies articulate ethical aspirations without corresponding real-time implementation in relational contexts. These tensions expose two critical and underexplored gaps: (1) the disconnect between national AI policy and real-time practice in AI–Family Integration (AFI), and (2) the misalignment between conventional AI indices and relational readiness metrics based on AFI. To address these blind spots, this study introduces the AI–Family Integration Index (AFII)—a ten-dimensional benchmarking framework designed to evaluate countries on emotional, ethical, legal, symbolic, and caregiving preparedness. The AFII comprises dimensions such as emotional literacy, caregiving equity, symbolic legitimacy, youth-AI exposure, consent frameworks, and cultural receptivity. Each dimension was scored on a 0–10 scale using mixed-method analysis, including secondary qualitative and quantitative data, policy reviews, and narrative synthesis. Equal weighting was applied across dimensions to ensure conceptual balance and reflect the interconnected importance of each component. This methodological choice mirrors best practices in composite indices (e.g., the Human Development Index), while also maintaining interpretive equity across culturally diverse nations. To bring the framework to life, the study integrates real-world examples—such as Singapore’s emotionally intelligent robotics in eldercare and Japan’s symbolic AI design in companionship—to ground abstract dimensions in lived, culturally contextual practices. These narrative insights enhance both the interpretability and policy relevance of the scores. The AFII was applied to a sample of thirteen culturally and economically diverse countries, including all top 10 countries ranked in the Stanford AI Index (2024). The results reveal striking contrasts between innovation-driven AI adoption and human-centered readiness. While Singapore (9.6) leads globally, nations such as South Korea (8.8) and Japan (8.7) closely follow. The top five countries—Singapore, South Korea, Japan, Sweden (8.6), and the United Kingdom (8.4)—all score above 8.4, reflecting strong alignment between AI policy, emotional intelligence, cultural values, and caregiving integration. In contrast, middle performers like the United States (7.4) and China (7.6), though technologically dominant, exhibit shortfalls in symbolic trust, emotional safety, and caregiving ethics. At the lower end, India (6.0), Brazil (5.2), and South Africa (4.8) show emerging promise but require substantial investment in emotional-AI literacy, inclusive infrastructure, and ethical governance. A key finding is the gap between ethical policy intent and real-time relational deployment. Singapore, South Korea, and Japan exemplify strong alignment between governance vision and practical rollout. Conversely, France and Germany, despite policy sophistication, reveal slower execution in emotionally grounded domains. This disjunction reveals the need for a robust policy–practice alignment matrix, which the AFII introduces as a diagnostic typology for guiding national strategy. Another notable insight is the divergence between AFII rankings and the Stanford AI Index. Countries like the U.S. and China rank in the global top two for AI power, yet place 8th and 9th respectively on the AFII. Conversely, relationally advanced nations such as Singapore and Sweden achieve top AFII scores despite more modest technical power rankings. This contrast underscores that AI excellence must incorporate emotional and ethical dimensions—not merely computational strength or research volume. By embedding emotional and symbolic metrics into a comparative framework, the AFII offers a timely, multidimensional tool for policymakers, ethicists, and technologists. It reframes AI discourse from technocratic performance to relational intelligence, ethical resonance, and caregiving responsibility—defining a path for AI systems that not only think or learn, but relate, care, and belong.

Artificial intelligence (AI) has emerged as a disruptive technology in healthcare, with applications ranging from imaging diagnostics to clinical data management. This articlepresents a systematic literature review on the use of AI in clinical practice, focusing on global experiences and perspectives for Angola, particularly the Malanje region. The methodology included the analysis of scientific articles published between 2023 and 2025, obtained from platforms such as ScienceDirect, PubMed, SciELO, and Google Scholar, as well as technical reports from international organizations. The overall objective was to synthesize the available evidence on AI applications in healthcare, identifying lessons learned in countries such as the United States, Brazil, and South Africa, and proposing recommendations for its implementation in low-income settings. The results highlight that AI can improve diagnostic accuracy, optimize resource management, and expand access to healthcare services, especially in regions with shortages of professionals and infrastruct. However, its implementation requires investments in technological infrastructure, professional training, and ethical considerations, such as the protection of patient data. The conclusion is that AI can revolutionize clinical practice in Angola, provided it is implemented strategically, with investments in infrastructure, professional training, and ethical considerations. This study contributes to the advancement of scientific knowledge and the formulation of evidence-based public policies, aiming to promote equity and efficiency in the Angolan healthcare system.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing capabilities and business intelligence. AI-integrated enterprise resource planning (ERP) and customer relationship management (CRM) systems facilitate automated decision-making, improving supply chain management and personalized marketing campaigns. Despite its advantages, AI adoption in enterprise systems and predictive marketing presents challenges such as data privacy concerns, cybersecurity risks, and ethical considerations. The complexity of AI integration requires substantial investment in infrastructure and regulatory compliance to mitigate biases and ensure transparency in AI-driven decisions. Explainable AI (XAI) is increasingly necessary to build trust and accountability in enterprise applications. Future advancements in AI, including blockchain, augmented reality (AR), and quantum computing, will enhance predictive analytics and business intelligence, further transforming marketing automation and decision-making processes. The convergence of AI and blockchain is particularly promising in securing digital transactions and improving data transparency in enterprise operations. As AI continues to reshape enterprise systems and predictive marketing, businesses must adopt responsible AI practices, strengthen cybersecurity measures, and comply with global regulations to maximize its benefits. Companies that leverage AI-driven insights will gain a competitive edge by improving customer engagement, optimizing marketing strategies, and driving sustainable growth in the evolving digital economy.

PurposeThe purpose of this study is to critically investigate the effects of artificial intelligence (AI)-driven learning environments on learner autonomy, knowledge co-construction and digital equity in Thailand’s online education context. It aims to analyze how algorithmic learning tools and AI-based systems shape learners' educational experiences, particularly focusing on whether these technologies enhance or constrain learner autonomy and collaborative interactions. Additionally, it evaluates ethical and social implications, particularly the digital divide, algorithmic biases and the transparency of AI-mediated educational processes. The study seeks to provide actionable insights and recommendations for policymakers, educators and developers to optimize AI integration for inclusive lifelong learning.Design/methodology/approachThis research adopts a qualitative case study methodology involving in-depth, semi-structured interviews, participant observations and detailed document analysis from 30 diverse participants, including urban and rural learners, educators and instructional designers. Employing actor-network theory (ANT) and posthumanist theoretical frameworks, the study critically examines interactions between human and non-human actors within AI-driven educational environments. This approach allows for comprehensive exploration of participants' lived experiences, perceptions and interactions with AI technologies. Triangulation of multiple data sources ensures depth and reliability, providing nuanced insights into how AI systems influence learner autonomy, collaborative learning processes and educational equity.FindingsThe study identifies AI-driven learning platforms as having dual impacts on learner autonomy, simultaneously enabling greater self-directed learning through personalized guidance and real-time feedback while imposing algorithmic constraints that restrict intellectual exploration. AI-facilitated knowledge co-construction effectively organizes and structures peer interactions yet often results in depersonalized, transactional communication lacking deep human engagement. Furthermore, significant digital inequities are evident, with rural learners disproportionately disadvantaged due to limited technological infrastructure and algorithmic biases embedded within AI-driven platforms. These inequities highlight a critical need for inclusive, transparent and ethical design practices to ensure equitable access and meaningful learner agency.Research limitations/implicationsThe qualitative, context-specific nature of the case study limits the generalizability of findings beyond Thailand’s online educational environment. The rapidly evolving nature of AI technologies suggests that findings reflect a specific temporal context, necessitating continuous research updates. Participant selection bias may have influenced findings, with participants potentially holding strong pre-existing opinions on AI-driven education. Future studies should consider longitudinal analyses to understand long-term impacts, comparative cross-cultural research to validate findings and deeper exploration of algorithmic biases, fairness and ethical considerations in AI-driven educational tools, particularly how they affect diverse socio-economic groups.Practical implicationsPractically, this research underscores the necessity of designing AI-driven learning platforms that genuinely empower learner autonomy and critical thinking rather than restrict or overly dictate educational pathways. Educational practitioners and technology developers should implement transparent AI decision-making processes, enabling learners to understand and critically evaluate algorithmic recommendations. Policies must prioritize digital infrastructure expansion and digital literacy programs to reduce urban-rural digital disparities. Furthermore, pedagogical practices should integrate human-centric approaches to complement AI tools, ensuring that AI technologies serve to enhance, rather than replace, essential human interactions within collaborative learning environments.Social implicationsThe study emphasizes that AI-driven education holds significant potential for democratizing lifelong learning by offering personalized and adaptive learning experiences. However, it also warns of the risks associated with reinforcing existing socio-economic and digital inequalities, particularly between urban and rural learners. Addressing algorithmic biases and digital divides through targeted policy interventions, infrastructural investments and inclusive educational practices is critical for fostering equity and inclusivity in education. By ensuring that AI platforms are accessible and beneficial for all learner demographics, societies can leverage technology as a tool for broad-based educational empowerment and social development rather than a mechanism perpetuating exclusion.Originality/valueThis research contributes original insights into the nuanced impacts of AI-driven learning on learner autonomy, collaborative interactions and digital equity within the Thai educational context – areas relatively underexplored in existing literature. By uniquely integrating actor-network theory and posthumanist perspectives, it offers a sophisticated understanding of the interplay between humans and technologies in educational settings. The empirical findings deliver crucial evidence-based recommendations for educators, policymakers and developers aiming to ethically and inclusively leverage AI in education. Ultimately, the study enriches ongoing discussions on balancing technological advancement with human-centric, equitable and socially responsible educational practices.

he rapid progress in implementing Artificial Intelligence (AI) across various domains such as healthcare decision-making, medical diagnosis, and others has raised significant concerns regarding the fairness and bias embedded within AI systems. This is particularly crucial in sectors like healthcare, employment, criminal justice, credit scoring, and the emerging field of generative AI models (GenAI) producing synthetic media. Such systems can lead to unfair outcomes and perpetuate existing inequalities, including biases ingrained in the synthetic data representation of individuals.This survey paper provides a concise yet comprehensive examination of fairness and bias in AI, encompassing their origins, ramifications, and potential mitigation strategies. We scrutinize sources of bias, including data, algorithmic, and human decision biases, shedding light on the emergent issue of generative AI bias where models may replicate and amplify societal stereotypes. Assessing the societal impact of biased AI systems, we spotlight the perpetuation of inequalities and the reinforcement of harmful stereotypes, especially as generative AI gains traction in shaping public perception through generated content.Various proposed mitigation strategies are explored, with an emphasis on the ethical considerations surrounding their implementation. We stress the necessity of interdisciplinary collaboration to ensure the effectiveness of these strategies. Through a systematic literature review spanning multiple academic disciplines, we define AI bias and its various types, delving into the nuances of generative AI bias. We discuss the adverse effects of AI bias on individuals and society, providing an overview of current approaches to mitigate bias, including data preprocessing, model selection, and post-processing. Unique challenges posed by generative AI models are highlighted, underscoring the importance of tailored strategies to address them effectively.Addressing bias in AI necessitates a holistic approach, involving diverse and representative datasets, enhanced transparency, and accountability in AI systems, and exploration of alternative AI paradigms prioritizing fairness and ethical considerations. This survey contributes to the ongoing discourse on developing fair and unbiased AI systems by outlining the sources, impacts, and mitigation strategies related to AI bias, with a particular focus on the burgeoning field of generative AI.

Artificial intelligence (AI) and machine learning (ML) have emerged as disruptive technologies that are redefining cloud computing. The incorporation of AI and machine learning into cloud platforms improves productivity, scalability, and flexibility, allowing organizations to handle massive amounts of data and make intelligent choices in real time. This study investigates the symbiotic relationship between AI/ML and cloud computing, focusing on how cloud architecture provides the computational capacity needed for AI and ML models while AI improves cloud resource allocation. The article discusses key breakthroughs such as AI-driven automation in cloud operations, predictive analytics, and intelligent application deployment. The report emphasizes the democratization of AI capabilities via cloud services, which make them available to small and medium-sized organizations (SMEs) without requiring considerable infrastructure investment. It also investigates the security hurdles, ethical concerns, and compliance issues that come with data-intensive AI systems on the cloud. Real-world applications include AI-powered recommendation systems, fraud detection, and tailored consumer experiences, demonstrating the strength of this synergy. The report indicates that incorporating AI and ML into cloud computing is critical for expanding technological landscapes, driving creativity, and helping enterprises to remain competitive. However, addressing data privacy, ethical AI usage, and fair access to cloud-based AI resources are crucial for long-term progress. This study provides insights for researchers and practitioners on leveraging AI/ML in the cloud to meet evolving technological and business needs.

Artificial Intelligence (AI) is increasingly transforming higher education across the globe by reshaping teaching, learning, research, and institutional management. In developing countries such as Tanzania, the integration of AI into Higher Education Institutions (HEIs) presents both significant opportunities and complex challenges. Despite the presence of infrastructural and resource limitations, AI has the potential to revolutionize the educational landscape by improving access to quality learning materials, supporting data-driven decision-making, and enhancing administrative efficiency. This study explores the multifaceted impacts of AI on Tanzanian HEIs, with a specific focus on its current applications, benefits, challenges, and policy implications. The research draws upon recent empirical studies and secondary data to analyze how AI technologies such as intelligent tutoring systems, predictive analytics, and automated assessment tools contribute to personalized learning experiences and improved academic outcomes. Moreover, the study identifies the major barriers to AI adoption, including inadequate technological infrastructure, limited institutional capacity, insufficient AI-related policies, and persistent ethical concerns surrounding data privacy and algorithmic bias. Findings reveal that although AI adoption in Tanzanian HEIs is on the rise, the implementation remains fragmented and uneven across institutions. To fully harness AI’s transformative potential, the study recommends the development of comprehensive national AI policies, investments in digital infrastructure, and targeted capacity-building programs for educators and administrators. Additionally, the research emphasizes the need for ethical frameworks that promote fairness, inclusivity, and transparency in AI utilization. Overall, this paper underscores the necessity of a strategic and policy-driven approach to ensure that AI contributes effectively and equitably to the advancement of higher education in Tanzania.

The accelerating integration of Artificial Intelligence (AI) across the economic, social, and institutional domains of nations worldwide marks one of the most consequential technological shifts of the twenty-first century. This study critically examines the development trajectory of AI from its conceptual origins to its contemporary multi-sectoral applications, analysing both the transformative benefits it offers and the complex challenges that accompany its adoption. Drawing on comparative evidence from developed and developing economies—with particular focus on India, Kenya, Brazil, Rwanda, and Vietnam—the paper charts the divergent paths nations have taken in harnessing AI for growth and social equity. The research further evaluates the evolving global regulatory landscape, interrogating how legislative frameworks in the United States, European Union, and emerging markets attempt to govern AI deployment responsibly. Findings reveal that while AI holds significant promise for inclusive development, bridging the adoption gap requires coordinated investment in infrastructure, human capital, and governance. The study concludes by articulating a framework for ethical and inclusive AI integration that can inform policy across diverse national contexts. Keywords: Artificial Intelligence, Machine Learning, Global Development, International Policy, Ethics, Inclusive Growth, Regulatory Frameworks.

Integrating Artificial Intelligence (AI) into educational management systems is widely acknowledged as a transformative approach that can enhance operational efficiency, decision-making processes, and personalized learning experiences. However, significant challenges and ethical considerations accompany these opportunities, underscoring the importance of understanding AI’s impact on educational institutions. This study explores the advantages and disadvantages of AI implementation in academic management, with a focus on identifying the opportunities, challenges, and ethical concerns associated with its use. A mixed-methods research design combined quantitative surveys and institutional performance data analysis with qualitative interviews and focus groups. The study involved 12 educational institutions with integrated AI tools, collecting insights from 150 survey respondents and 30 interviewees. The findings reveal that AI significantly improves operational efficiency, streamlines administrative tasks, and optimizes resource allocation. Additionally, AI positively impacts learning outcomes, leading to notable enhancements in student performance and engagement. However, challenges such as financial limitations, a scarcity of technical expertise, and resistance to change were identified, along with ethical concerns related to data privacy and algorithmic bias. In conclusion, while AI holds immense potential for revolutionizing educational management, successful implementation requires careful consideration of challenges and ethical implications. The study’s findings have significant implications for policymakers and academic leaders, indicating the need for strategic planning, investments in infrastructure, and the establishment of clear ethical guidelines to ensure that AI is deployed effectively and equitably.

Artificial Intelligence (AI) is changing the manufacturing process speedily; however, there is little empirical data on the adoption rates, technology emphasis, and performance results achieved, especially in developing economies. The study of AI adoption trends in developing economies is critical because these countries have special infrastructural and ability limitations that determine technology adoption and performance. This study fills this gap by surveying 209 manufacturing companies in India and examines the relationships between AI technologies, their areas of application, and multidimensional performance outcomes using descriptive, inferential, and multivariate analysis. Results indicate that robotics, IoT, computer vision, and machine learning are the most commonly utilized technologies, with the majority of their applications being in production, predictive maintenance, supply chain, and design activities. Firms with large size are more likely to adopt, but the size of a firm is not the decisive factor that determines performance improvement. Instead, market dynamics in an industry are more influential, particularly time-to-market, innovation, and responsiveness to customers. The results are relevant because they provide empirical associations between adoption tendency and technology orientation with quantifiable performance gains, which support the objective of the study to advise AI-context-dependent strategies in manufacturing. The novelty of the paper is not only an integrated empirical framework of the tools-applications–multidimensional outcomes but also a change of debate on adoption to an industry-centered view of adoption as opposed to size-centered view. The paper contains delicate organizational guidelines and precise actions concerning the spread of inclusive AI in emerging economies.

Background: The prospect of Artificial Intelligence (AI) implementation in the healthcare field will revolutionize clinical decision-making and nursing practice. Nevertheless, senior nurses tend to have some reservations toward AI-based Clinical Decision Support Systems (AI-CDSS), especially on the issues of professional autonomy, workload, and even ethics. Purpose: it was to understand the issues and possibilities of introducing AI-based devices to experienced nursing personnel, their attitudes, obstacles, and possible ways of sustainable implementation. Methodology: A narrative review of PubMed, Scopus, CINAHL, and Web of Science (2018–2025) was conducted; eligible full texts were screened and thematically synthesized from 18 included studies. Results: The key obstacles to AI implementation were found to be more cultural and psychological than technical for decision makers. Nurses raised worries regarding the loss of expertise, risk to autonomy, and automation bias. A training co-pilot- a specialist mediating between advanced technology and clinical reality- was needed in the process of effective integration. Strategic imperatives like explainable AI, ethical sensitization, workload alignment, and institutional transparency became essential success factors. Conclusion: The paper highlights how effective AI implementation needs more than just technical implementation; it would need cultural adjustment, institutional openness, and training on specific skills designed to fit experienced personnel. To provide safe and sustainable integration of AI into nursing, it is necessary to create nurse informaticists as co-pilots and introduce ethical, transparent, and explainable practices.

Artificial Intelligence (AI) is emerging as a transformative force in both drug development and orthodontics. This paper provides an overview of key AI techniques employed in these fields. In drug development, AI is accelerating discovery, development, and delivery through generative models, predictive modeling, image analysis, and natural language processing. Orthodontics benefits from AI through image analysis, treatment planning, and patient experience enhancement. Common AI techniques in both domains include machine learning, deep learning, natural language processing, and computer vision. By leveraging these technologies, researchers and clinicians can achieve significant advancements in drug discovery, treatment efficacy, and patient care.