Figure - available from: Frontiers in Psychology
This content is subject to copyright.
Source publication
This paper presents an in-depth literature review on the driving forces and barriers for achieving operational excellence through artificial intelligence (AI). Artificial intelligence is a technological concept spanning operational management, philosophy, humanities, statistics, mathematics, computer sciences, and social sciences. AI refers to mach...
Similar publications
Abstract. The concern of this research is to determine to what extent the municipal environment or, in a broader sense, innovation impact on the performance of the municipality's MSMEs. To this end, a research method was proposed that complements direct information from a sample of MSMEs with interviews with business leaders and state and municipal...
Citations
... Todavia, antes de incorporar qualquer tecnologia como solução milagrosa, ressalta-se que sistemas de IA para monitoração e predição para saúde conformam a última camada na arquitetura de automação em sistemas de informação 17 . Previamente à camada de sistemas de IA, é essencial constituir as camadas de reorganização do trabalho e automação para aquisição de dados (prontuário eletrônico, exames laboratoriais, equipamentos de suporte à vida e equipamentos de monitoração fisiológica); padronização dos respectivos protocolos de comunicação eletrônica; sincronização temporal de todas as categorias de dados, armazenamento e organização desses dados uma única central, padronização da arquitetura informacional para visualização das equipes multiprofissionais e, por fim, modelar os algoritmos para suportar a criação e implementação de sistemas de IA. ...
Inteligência Artificial no Monitoramento em Unidade de Terapia Intensiva (UTI) Qual o avanço da inteligência artificial na UTI? A busca pelo conhecimento em prol dos avanços no cuidado ao paciente crítico, adoção de estratégias inovadoras e redefinição de padrões sempre estiveram presentes no dia a dia da terapia intensiva. Em 1998, com a criação de um protocolo de proteção pulmonar para pacientes que desenvolvem a síndrome do desconforto respiratório agudo (SDRA) é um dos exemplos transformadores na busca da individualização e precisão terapêutica. Este estudo (Effect of a Protective-Ventilation Strategy on Mortality in the Acute Respiratory Distress Syndrome - NEJM), realizado no HCFMUSP, demonstrou ao mundo que a estratégia protetora de ventilação mecânica foi responsável por diminuir pela metade os óbitos desses pacientes em UTI.1 Em 2015, um outro trabalho do grupo de pesquisadores da UTI Respiratória HC FMUSP (Driving Pressure and Survival in the Acute Respiratory Distress Syndrome - NEJM) instrumentalizou a estratégia de ventilação protetora e, portanto, além de individualizar a mecânica ventilatória, foi possível ajustar a terapia conforme a evolução do pulmão, ajuste dinâmico ao momento fisiológico do paciente.2 Estes exemplos, bem como os inúmeros esforços dos últimos 20 anos relatam que o cuidado individualizado e sua relação temporal estão diretamente relacionados ao sucesso na terapia intensiva. O monitoramento contínuo de eletrocardiografia (ECG), associado aos sinais vitais medidos intermitentemente e resultados laboratoriais armazenados em registros médicos eletrônicos, é usado para detecção precoce da deterioração clínica em pacientes de graves.3 Um estudo multicêntrico com população mista de enfermaria e UTI, validou um algoritmo baseado no aprendizado de máquinas (InSight) para a detecção e previsão de sepse, sepse grave e choque séptico com alta sensibilidade e especificidade. A análise de apenas seis sinais vitais comuns (pressão arterial sistólica, pressão arterial diastólica, frequência cardíaca, frequência respiratória, saturação de oxigênio capilar periférico e temperatura), retirados dos registros eletrônicos hospitalares, apresentou excelentes resultados, podendo ser uma opção de baixo custo para novos usuários.4 Os dados armazenados dos registros médicos podem ser utilizados para a condução de estudos retrospectivos para melhorar as ferramentas de aprendizado das máquinas e aumentar a qualidade do uso. Conforme este estudo conduzido em um centro acadêmico de cuidados terciários, conseguiu-se prever alterações fisiológicas em até 24 horas antes da detecção clínica, podendo ser utilizadas precocemente através de máquinas antes da deterioração.5 O controle da ventilação mecânica tem um papel fundamental na sobrevida do paciente crítico em UTI. Por outro lado, o quão antes for percebida uma assincronia ventilatória, maiores as chances de evitar lesões pulmonares pela própria terapia e, com isso, mitigar a possibilidade de complicação infecciosa.6 Em um estudo global, para definição dos critérios de consenso internacional para sepse pediátrica e choque séptico, o Phoenix Sepsis Score foi validado como uma potencial ferramenta em indivíduos menores de 18 anos com infecção, podendo melhorar o atendimento clínico, avaliação epidemiológica, identificação da sepse pediátrica e choque séptico em todo o mundo.7 A interpretação rápida dos dados obtidos por meio de escores SOFA, SAPS3, GLASGOW e KDIGO, por exemplo, é fundamental para a obtenção de metas hemodinâmicas adequadas e tratamento precoce dos pacientes críticos.8,9,10 Em atenção a tais demandas, cenários realísticos têm sido criados em UTIs para avaliar a precisão das ferramentas de inteligência artificial (como ChatGPT 4.0 Plus, Bard e Perplexity) na interpretação dos dados gerados pelos pacientes com a pontuação de score SOFA. O ChatGPT 4.0 Plus demonstrou um excelente desempenho, podendo ser uma ferramenta auxiliar na redução da sobrecarga de trabalho dos médicos.11 Outras áreas hospitalares também estão utilizando inteligência artificial em algoritmos de aprendizado de máquinas para a avaliação de imagens cardiovasculares na tomografia computadorizada. Essa ferramenta trabalha com dados armazenados, auxiliando no diagnóstico e prognóstico médico.12 Observamos um ritmo semelhante de evolução na angiotomografia computadorizada, amplamente utilizada para o diagnóstico da doença arterial coronariana e em fase final de validação para uso em pacientes. Em um futuro próximo, presume-se que a inteligência artificial estará totalmente integrada ao fluxo de trabalho hospitalar, reduzindo a sobrecarga de trabalho e transferindo o foco da medicina para a precisão no tratamento.13
... As organizations strive to remain relevant in dynamic and competitive markets, the integration of technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) has become essential in transforming traditional business models. These technologies enable businesses to automate processes, improve resource management, and gain actionable insights from data that were previously inaccessible [2]. The growing adoption of AI and IoT is reshaping how companies optimize their operations, interact with customers, and manage resources in a more interconnected and data-driven environment [3]. ...
As organizations face increasing competition and technological advancements, optimizing operations and managing resources efficiently is crucial for maintaining a competitive edge. The integration of emerging technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) enhances efficiency, improves resource allocation, and drives growth. This study explores how AI and IoT adoption optimizes business processes, improves decision-making, and fosters a competitive advantage Using a quantitative approach, data from 200 executives in AI and IoT-implemented industries were analyzed. The analysis, conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM), indicates that AI and IoT significantly enhance efficiency, resource utilization, and overall performance. Real-time monitoring and predictive analytics improve market alignment and operational trends The findings suggest that organizations adopting AI and IoT can better navigate dynamic business environments, enhance productivity, and sustain growth. Moreover, fostering innovation and continuous technological improvement is essential. This research underscores AI and IoT’s transformative potential in reshaping business operations and securing a competitive edge. Future research should explore these technologies' industry-specific impacts and broader innovation potential.
... A sub-discipline of artificial intelligence concerns the creation of computers or machines that learn automatically and move toward statistical learning. Machine learning aims to create algorithms that can manage real-time information and improve the data management experience without being explicitly personalized (Tariq et al., 2021). There is a range of levels of AI, starting from task-specific specialized AI, evolving to general AI with extensive capabilities, and culminating in superintelligent AI that surpasses human intelligence. ...
This paper portrays the role of the interplay between artificial intelligence and human resources as evidenced by an extensive review of academic literature. The study investigated 402 abstracts of scholarly articles published in the Business Management and Accounting domain of the Scopus database spanning from 2000 to 2023. Using QDA Miner 2024, a novel approach based on content, link, and proximity analysis was employed to conduct the literature review. Three major findings were revealed by our investigation. First, while codes such as 'AI' and 'employees' dominate the academic discourse, there is an evolving trend toward more sophisticated analyses of AI-human resources interactions, including their impact on business strategies and performance. Second, research methods show significant diversification over time, going from more descriptive approaches to sophisticated quantitative and qualitative methodologies. Third, several areas appear to lack research focus, such as the connection between employee recruitment and future career paths with AI and business progress. The findings contribute to understanding how the technological revolution shapes business operations, particularly workforce management, while highlighting the need for structural reforms in organizational approaches to AI adoption, and provide valuable insights for both scholars and practitioners interested in the integration of AI in human resource management.
... The main barriers to adopting artificial intelligence (AI) include cultural constraints, fear of the unknown, lack of skills, strategic planning issues, and technological transformation challenges [40]. Clear communication about AI benefits can help alleviate fears among employees and emphasize new opportunities instead of job losses [35]. ...
... Creating a well-defined strategic plan that connects AI initiatives with business goals is crucial. Organizations must integrate AI projects into their overall strategy and have a solid grasp of the anticipated advantages [40]. ...
Enterprise Resource Planning (ERP) systems are fundamental to the operation of contemporary businesses, effectively streamlining processes, optimizing resources, and enabling data-driven decision-making. As advancements in Artificial Intelligence (AI) technologies progress rapidly, organizations are increasingly integrating AI capabilities into ERP systems to enhance functionality, efficiency, and intelligence. This research paper delves into the intricacies of AI integration in ERP systems, highlighting significant opportunities such as improved predictive analytics, intelligent automation, and personalized user experiences. For instance, studies indicate that businesses adopting AI-driven ERP solutions have experienced over a 30% increase in user satisfaction and a 25% boost in productivity due to enhanced personalization of interfaces. However, the integration process is not without challenges, including data quality issues and resistance to change within organizational culture. Remarkably, over 50% of organizations plan to incorporate AI capabilities within the next two years, signifying a notable shift towards more efficient operations and strategic decision-making. The paper synthesizes literature, case studies, and expert opinions to provide valuable insights into the evolving role of AI in shaping the future of ERP systems. In light of these findings, practical recommendations are provided for organizations aiming to harness the potential of AI-driven ERP solutions, emphasizing the importance of aligning AI initiatives with broader business objectives to ensure sustainable competitive advantages and improved operational outcomes.
... In the digital era, TQM's role in developing knowledge assets extends to the effective utilisation of data and information (Matthews & Harris, 2006). Technological advancements such as big data analytics, artificial intelligence, and machine learning are becoming integral components of TQM strategies, enabling organisations to extract valuable insights from their operations (Tariq, Poulin & Abonamah, 2021). ...
Total Quality Management (TQM) and the development of intellectual capital go hand-in-hand in organizations. TQM enables continuous improvement and customer satisfaction, which are essential for success in a knowledge-driven economy. TQM intersects with knowledge management, creating, utilizing, and preserving intellectual capital. Examples of TQM success stories include Toyota, General Electric, Tesla. Intellectual capital fuels innovation, adaptability, and strategic decision-making, providing a sustainable competitive advantage. The challenges in implementing TQM for intellectual capital include resistance to change and resource constraints. However, there are strategies available to overcome these challenges and maximize knowledge assets. TQM plays a critical role in enhancing intellectual capital and offers recommendations for organizations seeking to optimize their knowledge assets.
... Current literature provides significant insights into the role of leadership in AI contexts, yet there is a lack of comprehensive studies that explore the intersection of multiple personality traits in driving leadership effectiveness [12]. This research, however, aims to fill this gap by providing a comprehensive analysis of the role of key personality traits in enhancing leadership in AI-driven business environments. ...
In the rapidly evolving landscape of AI-driven business environments, the integration of key personality traits—Emotional Intelligence, Achievement Orientation, Analytical Thinking, and Structured Leadership—into leadership practices is becoming increasingly crucial. This study explores the significance of these four traits in enhancing business leadership in the AI era. By analyzing data from 409 respondents using the FIKR personality assessment, the study identifies how these traits contribute to effective leadership, particularly in managing the human-AI interface, driving performance, making data-driven decisions, and ensuring the ethical implementation of AI technologies. The findings highlight that leaders who cultivate these traits are better equipped to navigate the complexities of modern business environments, ensuring that AI enhances rather than disrupts organizational success. The study concludes that the future of business leadership lies in harmonizing human traits with AI capabilities to drive innovation, ethical decision-making, and sustainable growth. In conclusion, integrating Emotional Intelligence, Achievement Orientation, Analytical Thinking, and Structured Leadership into leadership practices is crucial for navigating the challenges and opportunities presented by the AI era. These traits enable leaders to manage the human-AI interface effectively, drive innovation, make data-driven decisions, and implement AI technologies responsibly. As AI continues to reshape the business world, leaders who cultivate these traits will be better equipped to lead their organizations to success, ensuring that AI enhances rather than disrupts their operations. This finding will explore how these traits are interconnected and how they can be harnessed to achieve effective and ethical leadership in the AI-driven business landscape.
... As organizations navigate the complexities of integrating AI, they face the critical task of managing cultural transformation to ensure alignment with strategic objectives and sustained employee engagement (Tariq et al. 2021). The rapid evolution of AI has introduced profound changes in how tasks are performed, altering traditional workflows and necessitating new skill sets (Odonkor et al. 2024). ...
... This study aims to explore the impact of AI on organizational culture, with a particular focus on how AI-driven changes influence employee behavior, communication patterns, leadership dynamics, and the overall work environment (Tariq et al. 2021). The research seeks to provide insights into the challenges and opportunities that AI presents for organizational cultural transformation, and to identify strategies that can help organizations navigate this complex process effectively (Odonkor et al. 2024). ...
... The integration of AI technologies has the potential to significantly enhance efficiency and productivity within organizations (Tariq et al. 2021). By automating routine tasks and augmenting decision-making processes, AI systems can streamline workflows, reduce manual errors, and increase the operational efficiency (Wan et al. 2020). ...
The advent of Artificial Intelligence (AI) is profoundly transforming organizational landscapes, significantly influencing work practices and triggering cultural shifts. This study explores the role of AI in reshaping organizational work practices and examines the resulting cultural transformation. Through a systematic literature review, this study synthesizes existing research to provide a comprehensive understanding of AI’s impact on organizational landscapes. A systematic literature review was conducted, analyzing peer-reviewed articles, books, and conference papers to identify key themes related to AI-driven changes in work practices, including automation, decision making, and employee roles. It also explores how these changes influence organizational culture, particularly shifts toward innovation, agility, and continuous learning, alongside challenges like resistance to change and ethical concerns. While AI adoption promises benefits such as enhanced efficiency, productivity, and innovation, it also presents significant challenges related to cultural alignment, employee resistance, ethical concerns, and leadership communication. Effective leadership, transparent communication, and investments in skills development emerge as pivotal strategies for overcoming these obstacles and ensuring successful AI implementation. The findings offer insights into the complex interplay between AI adoption and cultural transformation, highlighting gaps in the current research and suggesting directions for future studies. This study serves as a valuable resource for academics and practitioners seeking to understand the broader implications of AI on organizational structures and culture.
... The BoD's involvement in creating a clear, unified strategy for AI adoption is essential to overcome this barrier. In this view, Tariq et al. (2021) highlighted that the outdated infrastructure and insufficient data foundations hinder banks' ability to utilize advanced AI and ML technologies fully. The findings indicate a need for significant investments in technological infrastructure, led by strategic guidance from the BoDs, to support advanced AI and ML initiatives. ...
The aim of the paper is twofold. First to examine the role of the board of directors in facilitating the adoption of AI and ML in Saudi Arabian banking sector. Second, to explore the effectiveness of artificial intelligence and machine learning in protection of Saudi Arabian banking sector from cyberattacks. A qualitative research approach was applied using in-depth interviews with 17 board of directors from prominent Saudi Arabian banks. The present study highlights both the opportunities and challenges of integrating artificial intelligence and machine learning advanced technologies in this highly regulated industry. Findings reveal that advanced artificial intelligence and machine learning technologies offer substantial benefits, particularly in areas like threat detection, fraud prevention, and process automation, enabling banks to meet regulatory standards and mitigate cyber threats efficiently. However, the research also identifies significant barriers, including limited technological infrastructure, a lack of cohesive artificial intelligence strategies, and ethical concerns around data privacy and algorithmic bias. Interviewees emphasized the board of directors’ critical role in providing strategic direction, securing resources, and fostering partnerships with artificial intelligence technology providers. The study further highlights the importance of aligning artificial intelligence and machine learning initiatives with national development goals, such as Saudi Vision 2030, to ensure sustained growth and competitiveness. The findings from the present study offer valuable implications for policymakers in banking in navigating the complexities of artificial intelligence and machine learning adoption in financial services, particularly in emerging markets.
... The widespread usage of AI in new technologies marks a significant change toward intelligent systems that adjust to the demands and actions of users, https://internationalpubls.com producing more dynamic and responsive applications [2]. Future technological landscapes will be significantly shaped by AI as it develops, so it is critical to consider not only the technical capabilities of these systems but also the user experience [3] [4]. ...
The paper proposes a comprehensive methodology for integrating UX design principles into AI-driven systems to enhance user experience in emerging technologies. The approach emphasizes a user-centered, iterative development process that incorporates advanced prototyping tools, continuous feedback loops, and collaboration between UX designers and AI developers. By focusing on refining user interfaces through detailed analysis of user interactions and behavior, the proposed methodology ensures that AI systems are not only functional but also intuitive and accessible. Key elements include leveraging machine learning models for personalization, utilizing natural language processing to enhance communication, and implementing recommendation systems that align closely with user needs and preferences. Data from external APIs, historical records, and real-time user behavior further support the AI engine in making informed, user-centric decisions. Continuous feedback mechanisms play a pivotal role in refining both AI algorithms and UX components, allowing the system to adapt dynamically to user feedback and evolving requirements. This methodology aims to bridge the gap between advanced AI capabilities and user-friendly design, ultimately driving higher user engagement, satisfaction, and trust in AI-driven systems.
... In the healthcare sector, Ambay et al. [65] demonstrated that AI reduces patient waiting times and increases equipment utilization, while Al-witwit and Ibrahim [66] found that AI achieved an accuracy of 95.25% in the personalization of policies in government operations, leading to significant efficiency improvements. Tariq et al. [67] and Agarwall et al. [68] emphasized that the adoption of AI technologies in business operations results in increased operational efficiency, reduced operational costs, and improved revenues for enterprises. Our findings further confirm that AI technologies contribute to reducing operational costs through automation and predictive analytics, allowing for better resource management and the reduction in unnecessary expenses. ...
The integration of artificial intelligence (AI) and the internet of things (IoT) is bringing revolutionary changes to the hospitality industry, enabling the advancement of sustainable practices. This research, conducted using a quantitative methodology through surveys of hotel managers in the Republic of Serbia, examines the perceived contribution of AI and IoT technologies to operational efficiency and business sustainability. Data analysis using structural equation modeling (SEM) has determined that AI and IoT significantly improve operational efficiency, which positively impacts sustainable practices. The results indicate that the integration of these technologies not only optimizes resource management but also contributes to achieving global sustainability goals, including reducing the carbon footprint and preserving the environment. This study provides empirical evidence of the synergistic effects of AI and IoT on hotel sustainability, offering practical recommendations for managers and proposing an innovative framework for enhancing sustainability. It also highlights the need for future research to focus on the long-term impacts of these technologies and address challenges related to data privacy and implementation costs.
