A Review on Artificial Intelligence in Pharmaceutical Sciences: Opportunities & Challenges

While big firms in developed countries have embraced Artificial Intelligence (AI) for Supply Chain Management (SCM), the same cannot be said for firms in emerging markets, hence necessitating this study to examine AI adoption in plastic manufacturing firms in emerging markets, as a broad objective. The study relied on secondary qualitative data from peer-reviewed journals published between 2020-2025. Data collection followed a structured literature review protocol, and findings were analyzed thematically. The thematic analysis for the first objective, which sought to identify the types of AI applicable in SCM, indicated a clear set of AI technologies applicable to SCM in plastic firms, including machine learning, robotics, computer vision, and natural language processing. The result for objective two, which sought to determine the prospects of adopting AI in SCM plastic manufacturing firms in emerging markets, showed that plastic firms that adopt AI for their SCM stand to gain from more accurate forecasting, improved quality, lower costs, and stronger competitiveness. Findings for objective three, which assessed challenges of AI adoption in SCM in plastic manufacturing firms in emerging markets, revealed a set of interrelated barriers, including economic (costs), infrastructural (power/connectivity), technical (data availability/quality), human (skills and resistance), and institutional (security/privacy and policy). The study concluded that indeed, there are several areas AI can be adopted in SCM in manufacturing firms in emerging markets, and that when deployed, they stand to gain massively, notwithstanding the challenges they could face while attempting to adopt it. The study, therefore, among others, recommended that plastic manufacturing firms in emerging markets need to adopt practical AI tools for demand forecasting, warehouse automation, and quality control to improve efficiency, reduce waste, and enhance responsiveness.

Purpose This study investigates the interplay between artificial intelligence (AI) adoption, ethical capitalistic orientation and the moderating role of human–AI synergy, a new construct developed via grounded theory. This study aims to understand how these variables influence organizational decision-making in the AI era, specifically promoting ethical practices and mitigating negative outcomes such as employee displacement. Design/methodology/approach Using both qualitative and quantitative regression analysis, this study examines the relationships among AI adoption, ethical capitalistic orientation and human–AI synergy. Statistical tools were used to test hypotheses, identify AI adoption’s impact on organizational ethics and assess human–AI synergy’s moderating role. This research also explores how combining AI and human collaboration can foster a more ethical, socially responsible business model. Findings Findings show AI adoption positively impacts ethical capitalistic orientation (β1 = 0.45, p = 0.0001). Human–AI synergy significantly enhances this effect (β2 = 0.30, p = 0.0005) and moderates the relationship between AI adoption and ethical practices (β4 = 0.15, p = 0.0030). The study emphasizes focusing on human–AI collaboration over workforce displacement to maintain ethical practices and achieve efficiency. These results highlight the importance of ethical AI adoption and social responsibility. Originality/value This study introduces human–AI synergy as a novel construct, demonstrating its critical moderating role in the relationship between AI adoption and ethical capitalistic orientation. It offers new insights into how businesses can leverage AI without workforce reduction or unethical practices. This research emphasizes building ethical frameworks for AI adoption, presenting a novel perspective integrating technology with human-centered decision-making.

Background/Objectives: The integration of Artificial Intelligence (AI) and Machine Learning (ML) in pharmaceutical research and development is transforming the industry by improving efficiency and effectiveness across drug discovery, development, and healthcare delivery. This review explores the diverse applications of AI and ML, emphasizing their role in predictive modeling, drug repurposing, lead optimization, and clinical trials. Additionally, the review highlights AI’s contributions to regulatory compliance, pharmacovigilance, and personalized medicine while addressing ethical and regulatory considerations. Methods: A comprehensive literature review was conducted to assess the impact of AI and ML in various pharmaceutical domains. Research articles, case studies, and industry reports were analyzed to examine AI-driven advancements in predictive modeling, computational chemistry, clinical trials, drug safety, and supply chain management. Results: AI and ML have demonstrated significant advancements in pharmaceutical research, including improved target identification, accelerated drug discovery through generative models, and enhanced structure-based drug design via molecular docking and QSAR modeling. In clinical trials, AI streamlines patient recruitment, predicts trial outcomes, and enables real-time monitoring. AI-driven predictive maintenance, process optimization, and inventory management have enhanced efficiency in pharmaceutical manufacturing and supply chains. Furthermore, AI has revolutionized personalized medicine by enabling precise treatment strategies through genomic data analysis, biomarker discovery, and AI-driven diagnostics. Conclusions: AI and ML are reshaping pharmaceutical research, offering innovative solutions across drug discovery, regulatory compliance, and patient care. The integration of AI enhances treatment outcomes and operational efficiencies while raising ethical and regulatory challenges that require transparent, accountable applications. Future advancements in AI will rely on collaborative efforts to ensure its responsible implementation, ultimately driving the continued transformation of the pharmaceutical sector.

The AI-environment paradox: Unraveling the impact of artificial intelligence (AI) adoption on pro-environmental behavior through work overload and self-efficacy in AI learning.

Assessing perspectives on artificial intelligence applications to gastroenterology

Purpose The purpose of this paper is to identify and explain the organizational conditions under which artificial intelligence adoption in universities leads to structural change rather than incremental adaptation. By integrating Luhmann’s theory of decision premises with Argyris and Schön’s concept of organizational learning loops, the study conceptualizes artificial intelligence (AI) adoption as a process mediated by institutional structures and mechanisms of invisibilization and proposes strategies to foster double-loop learning that enable universities to surface and address organizational paradoxes, thereby creating the conditions for meaningful transformation in teaching, research and governance. Design/methodology/approach This conceptual study develops an analytical framework combining Luhmann’s theory of decision premises (programs, communication channels and personnel) with Argyris and Schön’s distinction between single-loop and double-loop learning to examine how universities process AI adoption. The approach synthesizes literature from organizational sociology, higher education studies and paradox theory to explain how contradictions are mediated by institutional structures and managed through mechanisms of invisibilization. The framework is applied analytically to the context of AI in teaching, research and governance, identifying conditions under which contradictions escalate into paradoxes that destabilize decision premises and create opportunities for structural change. Findings The study shows that universities often integrate AI within existing decision premises, containing contradictions through mechanisms of invisibilization, reframing contradictions as technical adjustments, recasting innovation as continuity and suspending role redefinitions, sustaining single-loop learning and organizational stability. Structural change through double-loop learning occurs when external pressures, such as regulatory mandates and funding constraints, converge with internal tensions in academic culture, governance and faculty roles, escalating contradictions into paradoxes that destabilize decision premises. The analysis posits that transformation depends on reconfiguring program premises toward reflexivity, redesigning communication channels for deliberative governance and redefining personnel premises to integrate AI-related expertise into formal authority structures. Research limitations/implications As a conceptual analysis, the study does not include empirical testing, which limits the ability to generalize findings across institutional contexts. Future research should apply and refine the proposed framework through comparative and longitudinal studies of AI adoption in universities, examining variations across governance models, regulatory environments and disciplinary cultures. The framework offers a basis for analyzing how decision premises mediate technological change, highlighting the need for research that investigates the interaction between external pressures, internal tensions and invisibilization mechanisms. Such work can inform both theory development in organizational change and the design of policies that foster reflexive, transformative AI integration. Practical implications The framework offers university leaders and policymakers strategies to foster transformative AI adoption by making organizational contradictions visible and actionable. Institutions can reconfigure program premises to align AI initiatives with mission and values, redesign communication channels to integrate AI within participatory governance and redefine personnel premises to incorporate AI-related expertise into formal authority structures. These interventions can help balance efficiency gains with academic autonomy, transparency and epistemic diversity. Policymakers can use the framework to design regulatory and funding mechanisms that incentivize reflexive adaptation rather than superficial compliance, thereby creating conditions for sustainable organizational change in teaching, research and governance. Social implications By framing AI adoption in universities as an organizational learning challenge, the study highlights its potential societal impact beyond technical efficiency. Universities play a central role in shaping knowledge production, professional formation, and public trust in expertise. AI integration that prioritizes reflexivity, inclusivity and participatory governance can strengthen these societal functions, fostering equitable access to high-quality education and preserving epistemic diversity. Conversely, uncritical adoption risks reinforcing managerial logics that marginalize academic voices and narrow the social purposes of higher education. The framework encourages institutions to engage with AI in ways that support democratic accountability and socially responsive knowledge systems. Originality/value This paper offers a novel conceptual framework linking Luhmann’s theory of decision premises with Argyris and Schön’s organizational learning loops to explain how AI adoption in universities is mediated by institutional structures. By introducing the concept of invisibilization mechanisms, reframing contradictions as technical adjustments, recasting innovation as continuity and suspending role redefinitions, the study advances understanding of why AI often reinforces stability rather than triggering structural change. It also extends organizational change theory in higher education by specifying conditions under which contradictions escalate into paradoxes and by proposing targeted strategies to foster double-loop learning that enable transformative, reflexive integration of AI technologies.

Purpose The purpose of this study is to examine how AI adoption enhances operational performance in manufacturing firms and to investigate how variations in firms’ strategic focus moderate this relationship. Specifically, this study explores how AI adoption functions as a dynamic capability that enhances operational performance in manufacturing firms and investigates how different strategic orientations—exploration, exploitation, or ambidexterity—moderate this effect from the perspective of the Attention-Based View (ABV) of the firm. Design/methodology/approach This study employs a quantitative research method, using a sample of 426 Chinese manufacturing firms to examine the impact of artificial intelligence (AI) adoption on operational performance and to analyze the moderating role of firms’ strategic focus (exploration, exploitation, and ambidexterity). Data were collected through a structured questionnaire survey. To ensure the robustness of the findings and address potential endogeneity issues, hierarchical regression analysis and the two-stage least squares (2SLS) method were employed. Findings This study reveals that AI adoption significantly enhances the operational performance of manufacturing enterprises, but this effect is moderated by firms’ strategic focus. A high exploration tendency weakens the performance-enhancing effect of AI adoption due to implementation instability caused by excessive experimentation. A high exploitation tendency also reduces the positive impact of AI adoption, as over-reliance on existing processes constrains AI’s transformative potential. Furthermore, an ambidextrous strategy (coexistence of high exploration and high exploitation) further diminishes the positive effect of AI adoption, indicating that resource dispersion and increased coordination costs may offset its benefits. Originality/value From the perspective of Dynamic Capabilities Theory, this study empirically examines the impact of AI adoption—as a dynamic capability—on operational performance. Additionally, drawing on the Attention-Based View (ABV) of the firm, it uncovers the moderating role of firms’ strategic focus, addressing an existing research gap concerning strategic-level decision-making in AI adoption. The findings offer theoretical insights that guide enterprise managers in optimizing AI adoption strategies, helping them strike a balance between innovation and efficiency to maximize the benefits of digital transformation.

This paper investigates drivers of Artificial Intelligence (AI) adoption, specifically in rural small and medium-sized enterprises (SMEs). Research on AI has gained traction in recent times, however, remains an area in need of further investigation, notably the adoption of AI by SMEs, and particularly among rural SMEs. The focus on SMEs is important as they account for the majority of businesses worldwide, playing an important role in job creation and economic development. The research uses secondary data from the Longitudinal Survey for Small Business (LSBS), a large UK panel survey of SMEs, which provides a broad range of variables on a range of SMEs. Probit regression, using a series of environment, firm and network engagement factors as predictive variables, identifies drivers of AI adoption for rural SMEs. Among the numerous drivers of AI adoption in rural SMEs, networking, is identified as a key variable associated with adoption. This research contributes to the limited knowledge on this subject and more broadly to technology adoption in organisations. This leads to policy recommendations in promoting AI adoption among rural SMEs through better communication of the advantages of adoption among SMEs and network development. • A rural location is not a barrier to AI adoption for SMEs, with numerous rural SMEs adopting AI technology in their operations. • There are numerous antecedents to AI adoption for rural SMEs, including networking, prior innovative activity influence, and strategic planning. • Rural SMEs that adopt AI technology are more likely to have employees, tend to be larger, have more sites, and have a greater turnover than non-adopters. • Engagement in networks can facilitate the adoption of AI technology among rural SMEs, as well as enhance understanding of the benefits of AI technology to SMEs' operations. • Policy should seek to encourage increased levels of AI adoption among rural SMEs to support growth, and ensure that appropriate infrastructure exists to support the use of AI technology.

The integration of Artificial Intelligence (AI) into personal finance and wealth management has fundamentally reshaped financial behaviors and decision-making processes. The primary objective of this study is to evaluate the role of AI in influencing personal financial behaviors and wealth management outcomes. Specifically, it aims to determine how AI adoption, investment, and usage impact personal savings and net worth. This study adopts a quantitative approach, utilizing secondary data from trusted sources such as Our World in Data and the Federal Reserve Bank of St. Louis. The dataset spans from 2010 to 2022, capturing trends over a significant period of AI development and adoption. A multivariate regression model is employed to examine the relationships between the dependent variables, Personal Savings Rate and Change in Net Worth, and independent variables such as AI adoption rate, AI investment, and household debt-to-income ratio. Descriptive statistics, correlation analysis, and stationarity tests are conducted to ensure data reliability and model validity. Diagnostic checks, including heteroskedasticity tests and Durbin-Watson statistics, further validate the robustness of the results. The study reveals that AI adoption positively influences personal savings by encouraging disciplined financial behaviors, consistent with the findings of prior research. However, its impact on wealth accumulation is less direct, with AI investment showing a surprising negative association with changes in net worth. This indicates inefficiencies in resource allocation or lag effects in the benefits of large-scale AI investments. Traditional economic factors, such as household debt and spending habits, continue to play significant roles in shaping financial outcomes, highlighting the enduring influence of non-technological determinants. The study also underscores the role of macroeconomic variables, such as unemployment, in moderating AI’s impact, with precautionary savings behaviors emerging during periods of economic uncertainty. Based on the findings, several actionable recommendations emerge. For individuals, the adoption of AI-driven tools that promote financial literacy and track spending can enhance savings and improve overall financial health. Financial institutions should prioritize user-centric designs in AI platforms, ensuring accessibility and functionality for diverse demographics. Policymakers are encouraged to support initiatives that bridge disparities in AI adoption, such as digital literacy programs and affordable access to financial technologies. Moreover, strategic investment in AI tools that address wealth management complexities, such as portfolio optimization and risk assessment, is critical for improving long-term financial outcomes. Originality This study contributes to the growing body of literature on AI in finance by offering a dual focus on personal savings and wealth management. Unlike previous studies that often treat these domains independently, this research provides an integrated perspective, highlighting both the synergies and divergences in AI’s impact. The findings on the nuanced relationship between AI investment and financial outcomes offer a fresh lens for evaluating the effectiveness of technological advancements. Furthermore, the study’s emphasis on traditional economic factors alongside AI-related variables underscores its originality in bridging the gap between technological innovation and foundational economic principles. This approach provides a robust framework for future research and practical applications in finance.

Purpose The purpose of this study was to construct a mediated moderating model, based on transactional theory of stress and coping, examining how hotel artificial intelligence (AI) adoption affects employee service performance (in-role and extra-role service performance), with AI crafting as the mediator and appraisals toward AI (challenge appraisals and hindrance appraisals) as the moderators Design/methodology/approach This study used a sample of 334 frontline hotel service employees collected in a three-wave time-lagged design, using path analyses to test the conceptual model. Findings Results indicated that when challenge appraisals toward AI is high, hotel AI adoption and challenge appraisals toward AI interaction have a positive impact on AI crafting. However, when hindrance appraisals toward AI is high, hotel AI adoption and hindrance appraisals toward AI interaction have a negative impact on AI crafting; the interaction between hotel AI adoption and appraisals toward AI on employee service performance (in-role and extra-role service performance) was mediated by employee AI crafting. Practical implications Organizations should develop comprehensive AI adoption strategies that consider both opportunities and risks. Encouraging employee AI job crafting through training programs and knowledge sharing can enhance service performance. Managers should assess and actively shape employees’ cognitive appraisals of AI, promoting challenge rather than hindrance perceptions. Creating an inclusive organizational culture and open communication channels is crucial for fostering positive employee attitudes toward AI adoption. Originality/value This study contributes to the literature on AI adoption and employee service performance by examining when and how hotel AI adoption influences employee service performance.

This study investigates the relationship between artificial intelligence (AI) adoption, knowledge sharing, and employee performance in Malaysian small and medium-sized enterprises (SMEs), with knowledge sharing examined as a mediating mechanism. SMEs represent a vital component of Malaysia’s economy, yet many face resource limitations that affect their readiness to fully harness AI technologies. While AI is recognized for its potential to enhance efficiency and innovation, its impact on employee performance is not always straightforward. This research therefore, explores whether knowledge sharing acts as the bridge through which AI adoption translates into performance outcomes. Data were collected through a survey of SME employees across service and manufacturing sectors, and the responses were analysed using partial least squares structural equation modelling (PLS-SEM). Measurement model results confirmed strong reliability and validity for the constructs of AI adoption, knowledge sharing, and employee performance. Structural model assessment revealed that AI adoption significantly and positively influences knowledge sharing but does not directly affect employee performance. Meanwhile, knowledge sharing revealed a strong and significant relationship with employee performance and was also found to partially mediate the relationship between AI adoption and performance. The study findings highlight that AI’s value in SMEs lies not in the technology itself but in its ability to foster knowledge exchange, learning, and collaboration. In addition, employee performance improves when AI is embedded into organizational practices that encourage knowledge sharing, thereby complementing human creativity and expertise. Theoretically, this study integrated the Knowledge-Based View (KBV) and the Technology–Organization–Environment (TOE) framework to explain how AI adoption and knowledge sharing practices together influence employee performance. Practically, the results underscore the need for SME leaders to move beyond technology acquisition and focus on building collaborative cultures that enable knowledge sharing. Overall, this research contributes both theoretical and practical insights into how SMEs can strategically leverage AI adoption to enhance employee performance through the mediating mechanism of knowledge sharing.

This research explores how social capital supports the adoption of artificial intelligence (AI)in developing countries, focusing on the role of "social brokers."A social broker is a trusted individual who occupies a unique position within a network, connecting individuals from different networks or maintaining connections with a larger number of individuals within the existing network.Based on input from the initial phase of the project, conducted in a developing country with high internet use but low AI adoption, we use qualitative research methods to better understand the practical aspects of AI adoption.Our early findings suggest that AI adoption goes beyond the right technology or skills and is strongly influenced by trusted communities and networks that shape decisions about AI adoption."Social brokers" play a key role in this process.They help close knowledge gaps, address concerns of people who have not adopted AI or have adopted it at a low level, and show how AI can be relevant and useful for specific jobs and tasks.These "social brokers" are often seen as trusted friends, technology influencers, former colleagues, or respected local industry experts.Their presence and activities in tightly connected social networks appear to be very important for reducing the gap in AI adoption.The next phase of this research will focus on identifying the aspects of social capital that influence AI adoption, understanding the relationships that help overcome resistance to adopting AI, and developing strategies that use social capital to encourage faster AI adoption in developing countries.

PurposeThis paper aims to explore factors impacting citizen intention toward artificial intelligence (AI) adoption, considering government regulation as a moderating variable. It focuses on the Palestinian Cellular Communications Sector in Gaza Strip, providing insights into the citizen-AI relationship dynamics. The research contributes to enhancing comprehension of AI technology from clients’ perspective.Design/methodology/approachTo test the hypotheses, a questionnaire was used in an empirical study to collect primary data. In total, 347 Palestinian citizens responded to the survey.FindingsThe findings of this paper reveal that perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns significantly influence citizen intention toward AI adoption. Furthermore, government regulations as a moderating variable strengthen the impact of perceived usefulness, perceived ease of use, perceived risks, social influence, user experience and privacy and security concerns on citizen intention toward AI acceptance and adoption. Thus, further research should explore specific domains and cultural contexts to gain a more comprehensive understanding of the factors shaping acceptance and adoption.Research limitations/implicationsThe findings of the study should be understood in the context of their limitations. First, the study ignored cultural or domain-specific subtleties in favor of generic characteristics, which calls for more research in these particular circumstances. Second, relying on self-reported data might result in biases and limitations due to subjectivity in reporting, indicating the necessity for alternate data gathering methods and approaches in future research.Practical implicationsPolicymakers, developers and organizations working to promote the acceptability and implementation of AI applications should consider the practical implications of this study’s results. To secure the long-term use of AI technologies in a responsible and user-centric way, policymakers should give priority to public education and awareness, user-centered design and ethical AI development techniques. They should also stimulate partnerships and create monitoring systems.Originality/valueThis paper investigates the originality of factors that influence citizen intention toward AI acceptance and adoption. It uniquely examines the moderating role of government regulations in shaping this intention. By addressing this novel aspect, the paper contributes to advancing our understanding of the complex dynamics surrounding citizen intentions toward AI applications.

Purpose This study investigates the complex interplay between technological and ethical factors influencing artificial intelligence (AI) adoption in entrepreneurship and startup ecosystems, with a particular focus on how these dynamics impact innovation outcomes and organizational performance. Design/methodology/approach Employing a comprehensive analytical framework, the research examines quantitative data to assess the relationships among technology related factors (such as interactivity, relative advantage, and perceived intelligence), ethical principles (including fairness, accountability, transparency, accuracy, and autonomy), ethical dilemma, and their collective influence on AI adoption and exploitative innovation within entrepreneurial contexts. Data was collected using a self-administrated questionnaire to 207 respondents, in the Iran entrepreneurship and startup ecosystem. The Partial Least Square-Structural Equation Modeling (PLS-SEM) technique was used to examine the proposed hypotheses of the study. Findings The findings reveal that technology related factors specifically interactivity, relative advantage, perceived intelligence, transparency, and accuracy significantly drive AI adoption among entrepreneurs. In contrast, ethical considerations such as fairness, accountability, and autonomy do not exhibit a direct influence on adoption decisions. Also, the moderating relationship of ethical dilemma between exploitative innovation and organizational performance by AI adaptation was rejected. Notably, the study highlights the pivotal mediating role of exploitative innovation, AI adoption enhances exploitative innovation, which in turn improves organizational performance; however, there is no direct relationship between AI adoption and organizational performance. Practical implications Entrepreneurs and startup leaders should prioritize AI technologies that offer clear interactive capabilities, relative advantages, and transparent, accurate operations to maximize adoption and performance benefits. While ethical principles remain important, their influence may be more pronounced at later stages of implementation or in highly regulated sectors. Policymakers and ecosystem builders are encouraged to focus on fostering environments that support the practical integration of AI, particularly in ways that enhance exploitative innovation and organizational scalability. Originality/value This research provides novel insights by disentangling the relative importance of technological versus ethical factors in AI adoption within entrepreneurial settings. It advances the literature by empirically demonstrating the limited direct impact of certain ethical considerations on adoption decisions and by highlighting the central role of exploitative innovation as a mediator between AI implementation and organizational outcomes.

Purpose The purpose of this study is to enhance the understanding of management practices that expand artificial intelligence (AI) adoption in public organizations. Design/methodology/approach The research approach is an exploratory study. Research data were collected from informants representing various organizations working for public or government services in Finland. Findings The findings of this study indicate that large-scale AI adoption is a complex process in the public sector. This study identified three main practices of AI adoption: technological design practices, AI project design and management practices and networking practices. Additionally, this study shows that several value drivers and barriers to AI adoption are related to technological, organizational and environmental dimensions. This study emphasizes the importance of developing cross-functional AI capabilities and resources, which are crucial for expanding AI initiatives across organizations and networks. Practical implications Expanding and scaling AI adoption across organizational boundaries requires new management practices and multidisciplinary teamwork, from technological skills to AI governance practices and change management. Originality/value This study contributes to the emerging research on management practices involved in AI adoption in the public sector.