Data Artificial Intelligence Research Articles

The Approach to Sensing the True Fetal Heart Rate for CTG Monitoring: An Evaluation of Effectiveness of Deep Learning with Doppler Ultrasound Signals.

Cardiotocography (CTG) is widely used to assess fetal well-being. CTG is typically obtained using ultrasound and autocorrelation methods, which extract periodicity from the signal to calculate the heart rate. However, during labor, maternal vessel pulsations can be measured, resulting in the output of the maternal heart rate (MHR). Since the autocorrelation output is displayed as fetal heart rate (FHR), there is a risk that obstetricians may mistakenly evaluate the fetal condition based on MHR, potentially overlooking the necessity for medical intervention. This study proposes a method that utilizes Doppler ultrasound (DUS) signals and artificial intelligence (AI) to determine whether the heart rate obtained by autocorrelation is of fetal origin. We developed a system to simultaneously record DUS signals and CTG and obtained data from 425 cases. The midwife annotated the DUS signals by auditory differentiation, providing data for AI, which included 30,160 data points from the fetal heart and 2160 data points from the maternal vessel. Comparing the classification accuracy of the AI model and a simple mathematical method, the AI model achieved the best performance, with an area under the curve (AUC) of 0.98. Integrating this system into fetal monitoring could provide a new indicator for evaluating CTG quality.

Researching the Topic of AI Data Analysis in Loyalty Programs

Loyalty programs have long served as valuable tools for customer engagement and retention. Today, with the increasing availability of customer data, new methods for analysis and program optimization are becoming a necessity. Artificial Intelligence (AI) has also been a topic of high relevance in recent years, and its connection with loyalty programs has not been researched and discussed enough to date. In this paper we aim to explore the emerging topic of AI data analysis in the context of loyalty programs. We examine various applications of AI data analysis in loyalty programs, including personalized rewards and incentives, customer segmentation and targeting and fraud detection and risk management. Covering these possible applications of AI data analysis provides a general understanding of AI data analysis functioning and its integration into loyalty programs. The provided information also serves as a good basis for understanding how AI data analysis functions and may be applied in various fields. In our paper we look at the outcomes of AI data analysis applications in loyalty programs from the point of view of a customer who is using those programs as well as from the point of view of the brand who manages them. By analyzing the identified points, we then highlight the benefits and challenges of using AI data analysis in loyalty programs. Key research gaps are also presented, emphasizing the need for further research on the topic, with particular attention given to problems such as ethical considerations, data privacy concerns, and the transparency of AI models. The main goal of our article is to provide readers with complex information on the topic of AI data analysis usage in loyalty programs and the ways this technology may benefit customers and the brand in the selected context. The article is theoretical-empirical and is based on external information from trustworthy sources, both digital and printed, completed with our own research outcomes.

Sharia Economic Transformation in the Era of Industrial Revolution 4.0

. Industrial Revolution 4.0, marked by technological advances such as artificial intelligence (AI), Internet of Things (IoT), big data, and blockchain, has brought significant changes in various economic sectors, including the sharia economy. Sharia economics, which is based on Islamic principles such as justice, transparency and social responsibility, faces great challenges and opportunities in this digital era. The integration of technology in the sharia economy is important to ensure its relevance and sustainability. This research analyzes the impact of the Industrial Revolution 4.0 on the sharia economy, identifies the necessary transformations, and provides strategic recommendations to overcome existing challenges. The method used is qualitative with document analysis from various primary and secondary sources. The discussion includes the adoption of technology such as blockchain to increase transparency, the use of AI and big data for risk analysis, as well as the development of sharia digital financial products and services. The research results show that technology can increase operational efficiency and sharia financial inclusion, but challenges such as rigid regulations and a lack of technological literacy must be addressed. This research provides in-depth insight into the synergy between technology and sharia principles, and paves the way for sustainable and inclusive innovation in the sharia economy.

Innovations of water pollution traceability technology with artificial intelligence

Water pollution traceability is a critical component of environmental management, essential for ensuring the safety of water supplies and maintaining ecological balance. Although many countries have established relatively mature water environment quality monitoring (WEQM) systems, these systems often face limitations such as inadequate professional sensing equipment, insufficient model stability, and high technical expertise requirements. Monitoring, early warning, and traceability of water pollution in complex watersheds remain challenging tasks. The advent of artificial intelligence (AI), the Internet of Things (IoT), and big data is driving the digital transformation of traditional WEQM systems, significantly enhancing their capabilities in digitalization, intelligence, and networking. This perspective paper examines the potential impacts of emerging technologies such as AI and big data on water environment monitoring and pollution traceability in watersheds. It identifies innovation barriers and the transformation potential of WEQM systems from various perspectives, including monitoring and early warning systems, pollution location and tracking, fingerprint spectroscopy technology, and the detection of new pollutants. This exploration aims to provide insights and inspiration for the accurate diagnosis of water pollution and the enhancement of global environmental monitoring systems.

Big data and AI in employment: The dual challenge of workforce replacement and protecting customer privacy in biometric data usage

The integration of Artificial Intelligence (AI) and Big Data is ushering in profound transformations across various industries, with biometric data usage standing out due to its deep implications for workforce dynamics and customer privacy. This review article critically examines the dual challenges presented by AI-driven automation and the extensive use of biometric data analytics, focusing on the resultant job displacement and escalating privacy concerns. Biometric technologies such as facial recognition, fingerprint identification, and voice analysis are increasingly deployed across sectors including finance, healthcare, and retail. These technologies aim to enhance security measures, improve user experience, and optimize operational efficiencies. However, they also bring to light substantial ethical dilemmas, particularly concerning the privacy of individuals and the security of the data being collected. The pervasive collection and analysis of biometric data can lead to invasive surveillance and profiling, exacerbating risks to personal privacy. Moreover, the use of AI in automating tasks that were traditionally performed by human workers is leading to significant shifts in employment structures. While AI can increase efficiency and reduce costs, it also raises the specter of widespread job displacement. This potential for automation-driven unemployment is especially pronounced in sectors that heavily utilize routine, repetitive tasks, posing critical socio-economic challenges. This article also explores the regulatory and technological frameworks currently in place, and those that are needed to address these challenges. The effectiveness of existing data protection laws, such as the General Data Protection Regulation (GDPR) in the European Union, and the California Consumer Privacy Act (CCPA) in the United States, is assessed in the context of AI and biometric data. We discuss the role of policy in shaping the ethical use of AI and protecting workers, along with the technological safeguards that could be implemented to secure biometric data and ensure privacy. By synthesizing insights from recent research, case studies, and expert analyses, this article provides a comprehensive overview of how AI and Big Data are reshaping the landscape of work and privacy. It critically discusses the need for a balanced approach that harnesses the benefits of technological advancements while safeguarding individual rights and employment security.

REGULATORY FRAMEWORKS FOR ARTIFICIAL INTELLIGENCE IN LAW: ENSURING ACCOUNTABILITY AND FAIRNESS

In recent years, the integration of artificial intelligence (AI) in the legal sector has transformed the way legal services are delivered, enhancing efficiency, accuracy, and accessibility. However, the rapid advancement of AI technology in law also raises significant concerns regarding accountability and fairness. This article explores the regulatory frameworks aimed at addressing these concerns and ensuring that AI systems in the legal domain operate ethically and responsibly. Beginning with an introduction to the role of AI in law and its implications, the article navigates through the complex landscape of AI regulation, highlighting global perspectives, key regulatory bodies, and existing laws relevant to AI in legal practice. It then delves into the principles and mechanisms essential for ensuring accountability in AI systems, including transparency, explainability, and data governance. Furthermore, the article investigates strategies for achieving fairness in AI-powered legal systems, addressing issues such as bias, discrimination, and the need for fairness metrics and evaluation methods. It explores governance mechanisms necessary for effective regulation, emphasizing stakeholder engagement, compliance, and enforcement strategies. Drawing insights from case studies and best practices, the article offers valuable lessons and recommendations for policymakers, practitioners, and stakeholders involved in shaping the future of AI regulation in the legal sector. In conclusion, it underscores the importance of continuous evaluation and adaptation to keep pace with the evolving landscape of AI technology and its impact on the legal profession.

Smart optimization in battery energy storage systems: An overview

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review.

Artificial intelligence (AI)-based medical devices have garnered attention due to their ability to revolutionize medicine. Their health technology assessment framework is lacking. This study aims to analyze the suitability of each health technology assessment (HTA) domain for the assessment of AI-based medical devices. We conducted a scoping literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. We searched databases (PubMed, Embase, and Cochrane Library), gray literature, and HTA agency websites. A total of 10.1% (78/775) of the references were included. Data quality and integration are vital aspects to consider when describing and assessing the technical characteristics of AI-based medical devices during an HTA process. When it comes to implementing specialized HTA for AI-based medical devices, several practical challenges and potential barriers could be highlighted and should be taken into account (AI technological evolution timeline, data requirements, complexity and transparency, clinical validation and safety requirements, regulatory and ethical considerations, and economic evaluation). The adaptation of the HTA process through a methodological framework for AI-based medical devices enhances the comparability of results across different evaluations and jurisdictions. By defining the necessary expertise, the framework supports the development of a skilled workforce capable of conducting robust and reliable HTAs of AI-based medical devices. A comprehensive adapted HTA framework for AI-based medical devices can provide valuable insights into the effectiveness, cost-effectiveness, and societal impact of AI-based medical devices, guiding their responsible implementation and maximizing their benefits for patients and health care systems.

Variational quantum circuit learning-enabled robust optimization for AI data center energy control and decarbonization

As the demand for artificial intelligence (AI) models and applications continues to grow, data centers that handle AI workloads are experiencing a rise in energy consumption and associated carbon footprint. This work proposes a variational quantum computing-based robust optimization (VQC-RO) framework for control and energy management in large-scale data centers to address the computational challenges and overcome limitations of conventional model-based and model-free strategies. The VQC-RO framework integrates variational quantum circuits (VQCs) with classical optimization to enable efficient and uncertainty-aware control of energy systems in AI data centers. Quantum algorithms executed on noisy intermediate-scale quantum (NISQ) devices are used for value function estimation trained with Q-learning, leading to the formulation of a robust optimization problem with uncertain coefficients. The quantum computing-based robust control strategy is designed to address uncertainties associated with weather conditions and renewable energy generation while optimizing energy consumption in AI data centers. This work also outlines the computational experiments conducted at various AI data center locations in the United States to analyze the reduction in power consumption and carbon emission levels associated with the proposed quantum computing-based robust control framework. This work contributes a novel approach to energy-efficient and sustainable data center operation, promising to reduce carbon emissions and energy consumption in large-scale data centers handling AI workloads by 9.8 % and 12.5 %, respectively.

AI Based Insight Vibrator with Glove for Visually Challenged and Auditory Challenged Individuals

This study introduces a novel assistive tool designed to enhance the communication abilities and sensory experiences of those with physical limitations. Seeing one's environment is one of humanity's greatest abilities. You may see more clearly around you using Eyes because of AI (Artificial Intelligence) data. The most terrible agony a person may experience is losing their vision as a result of problems or a variety of life circumstances. With or without assistance, a blind person can live a free and independent life in a variety of ways. People with impairments are granted certain rights and assistance by several nations worldwide.Users of the Insight Vibrator with Glove can sense and interpret information by means of haptic feedback technology integrated into a wearable glove.The glove is equipped with sensors that translate ambient input into distinct vibration patterns. This innovative technique aims to empower individuals with physical limitations and encourage increased independence and communication by providing them with a more sophisticated knowledge of their surroundings. The study looks at the development, functionality, and design of the Insight Vibrator with Glove, with a focus on how it could help those with physical limitations.

“In Minutes Instead of Weeks”: Discursive Constructions of Generative AI and Qualitative Data Analysis

The use of qualitative data analysis software (QDAS) platforms have always posed a dilemma for researchers, and the integration of generative artificial intelligence (AI) tools are complexifying this relationship even further. The way QDAS companies are positioning this new development will impact how researchers understand what qualitative analysis is and what it could be. Using discourse analysis methods, we explored how ATLAS.ti, NVivo, and MAXQDA websites constructed the relationship between AI-assist and qualitative research methods. We noted four “discursive dilemmas” across the websites: (a) automated insight-generation versus systematic meaning-making; (b) chatting with documents versus analyzing data; (c) high speed versus high engagement; and (d) novelty versus agency. While some level of hyperbolic discourse can be expected from corporations whose goal is to sell products, we argue that the discourses used on these websites may be incompatible with the epistemological foundations of qualitative research.

Integration of Emerging Technologies AI and ML into Strategic Supply Chain Planning Processes to Enhance Decision-Making and Agility

Purpose: The aim of this research was to discuss the use of artificial intelligence (AI), machine learning (ML), and big data analytics as fundamental pillars of strategic supply chain management, for better decision-making, more precise forecasting, and higher supply chain agility. Methodology: The paper reviewed existing literature and industry reports to get an in-depth insight into the modern supply chain planning environment, the problems that it faces, and the efficiency of traditional techniques. It then analyzed the opportunities of utilization of AI, ML and big data analytics as well as the certain technologies or techniques that could be utilized, such as the predictive/prescriptive analytics, digital twins and blockchain. Findings: The study concluded that the traditional supply chain planning processes are becoming more and more out of style and inefficient, taking into account the business environment that are constantly changing, global supply chains, and technological advancements. It emphasized the risks to long-term performance associated to relying too much on the past practices and a call for action for progressive modernization of supply chain planning mechanisms. Unique Contribution to Theory, Practice and Policy: The report pointed to innovative ways such as AI, ML, and big data analytics for the integration into the supply chain operations for increasing the productivity, resilience and competitiveness. Moreover, it promoted the increase of budgeting on the talent side in order to obtain an appropriate use of technology and to explore new paths in the market.

Comparative Analysis of Artificial Intelligence (AI) Languages in Predicting Sequential Organ Failure Assessment (SOFA) Scores.

The Sequential Organ Failure Assessment (SOFA) score plays a crucial role in intensive care units (ICUs) by providing a reliable measure of a patient's organ function or extent of failure. However, the precise assessment is time-consuming, and daily assessment in clinical practice in the ICU can be challenging. Realistic scenarios in an ICU setting were created, and the data mining precision of ChatGPT 4.0 Plus, Bard, and Perplexity AI were assessed using Spearman's as well as the intraclass correlation coefficients regarding the accuracy in determining the SOFA score. The strongest correlation was observed between the actual SOFA score and the score calculated by ChatGPT 4.0 Plus (r correlation coefficient 0.92) (p<0.001). In contrast, the correlation between the actual SOFA and that calculated by Bard was moderate(r=0.59, p=0.070), while the correlation with Perplexity AI was substantial, at 0.89, with a p<0.001. The interclass correlation coefficient analysis of SOFA with those of ChatGPT 4.0 Plus, Bard, and Perplexity AI was ICC=0.94. Artificial intelligence (AI) tools, particularly ChatGPT 4.0 Plus, show significant promise in assisting with automated SOFA score calculations via AI data mining in ICU settings. They offer a pathway to reduce the manual workload and increase the efficiency of continuous patient monitoring and assessment. However, further development and validation are necessary to ensure accuracy and reliability in a critical care environment.

Digital images generated by Artificial Intelligence as ethnographic experimentation

The article discusses the role of images produced by artificial intelligence (AI) in visual anthropology, highlighting their ability to represent identity and experience. It also addresses the technical and ethical challenges of AI data classification and its interaction with socio-technical networks, questioning technological neutrality. Technical implications include data categorization that can perpetuate biases and power relations. The simplification and distortion of representations by AI is highlighted, requiring a critical analysis of the stories embedded in the categorizations. It is proposed that anthropologists examine the relationship between image, label, and referent, recognizing differences and similarities in their roles and those of AI labelers in knowledge production. In addition, it discusses how AI images can influence anthropological interpretation and analysis by blending reality and emotion. It is argued that a critical engagement with the ethical and technical implications of the generation and use of these images is necessary.

Sustainable Landscape Design and Traditional Villages in Xuzhou, Jiangsu: Low-cost Strategies and Big Data Applications Influencing AI Integration

Big Data usage and Artificial Intelligence (AI) technology combined offer a potential approach to solving challenging problems. AI-driven solutions provide insightful analysis and creative solutions by utilizing the power of big data analytics. With an emphasis on the mediating role of technological literacy and the moderating effect of resource availability, this study investigates the effects of low-cost techniques, the usage of Big Data, and the integration of Artificial Intelligence (AI) on sustainability in landscape design. The purpose of this study is to look at the intricate connections between these factors and how they affect sustainable landscape design methods and results as a whole. A standardized questionnaire was answered by a sample of 458 landscape experts as part of a quantitative approach. Smart PLS (Partial Least Squares), which incorporates evaluations of measurement models, structural models, and mediation and moderation studies, was utilized for data analysis. The study found that using Big Data, implementing low-cost techniques, and incorporating AI all had very favourable&amp;nbsp;effects on sustainability in landscape design. The efficient use of Big Data and AI was found to be mediated by technological literacy, highlighting the importance of this concept in this context. Additionally, resource availability emerged as a critical moderating factor, influencing the strength of these relationships. This research contributes to the field by offering a holistic understanding of the dynamics within sustainable landscape design, emphasizing the importance of integration of AI and utilization of Big Data. It provides practical insights for landscape professionals, informs policy development, and advances educational curricula about AI and Big Data in landscape architecture. The study's limitations include potential response bias due to self-reported data and the cross-sectional design, which restricts the establishment of causal relationships. Additionally, the study focused on professionals, limiting the generalizability of findings to broader community perspectives.

"BIG DATA SOHASIDA SUNʼIY INTELLEKTNING HUQUQ VA MAJBURIYATLARI: MUAMMOLAR HAMDA YECHIMLAR "

"This article is devoted to analyzing problems and potential solutions in the regulation of the rights and responsibilities of artificial intelligence (AI) in the context of the processing and analysis of Big Data. We explore the legal and ethical aspects of accountability, transparency, non-discrimination, and protection of personal data in the use of AI. The research examines existing legal frameworks, and we propose measures that have the potential to improve them significantly, including the development of specific legislation, the establishment of independent audit mechanisms, the establishment of transparency and accountability requirements, and the promotion of international cooperation. Particular attention is paid to the role of transparency and accountability principles in AI regulation. The features of regulation of AI and Big Data in Uzbekistan are analyzed. It is concluded that with these promising measures, it is necessary to create an effective and balanced regulatory system that ensures the development of innovative technologies and the protection of human rights in the digital age. "

Exploring the Use and Implications of AI in Sexual and Reproductive Health and Rights: Protocol for a Scoping Review.

Artificial intelligence (AI) has emerged as a transformative force across the health sector and has garnered significant attention within sexual and reproductive health and rights (SRHR) due to polarizing views on its opportunities to advance care and the heightened risks and implications it brings to people's well-being and bodily autonomy. As the fields of AI and SRHR evolve, clarity is needed to bridge our understanding of how AI is being used within this historically politicized health area and raise visibility on the critical issues that can facilitate its responsible and meaningful use. This paper presents the protocol for a scoping review to synthesize empirical studies that focus on the intersection of AI and SRHR. The review aims to identify the characteristics of AI systems and tools applied within SRHR, regarding health domains, intended purpose, target users, AI data life cycle, and evidence on benefits and harms. The scoping review follows the standard methodology developed by Arksey and O'Malley. We will search the following electronic databases: MEDLINE (PubMed), Scopus, Web of Science, and CINAHL. Inclusion criteria comprise the use of AI systems and tools in sexual and reproductive health and clear methodology describing either quantitative or qualitative approaches, including program descriptions. Studies will be excluded if they focus entirely on digital interventions that do not explicitly use AI systems and tools, are about robotics or nonhuman subjects, or are commentaries. We will not exclude articles based on geographic location, language, or publication date. The study will present the uses of AI across sexual and reproductive health domains, the intended purpose of the AI system and tools, and maturity within the AI life cycle. Outcome measures will be reported on the effect, accuracy, acceptability, resource use, and feasibility of studies that have deployed and evaluated AI systems and tools. Ethical and legal considerations, as well as findings from qualitative studies, will be synthesized through a narrative thematic analysis. We will use the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) format for the publication of the findings. The database searches resulted in 12,793 records when the searches were conducted in October 2023. Screening is underway, and the analysis is expected to be completed by July 2024. The findings will provide key insights on usage patterns and evidence on the use of AI in SRHR, as well as convey key ethical, safety, and legal considerations. The outcomes of this scoping review are contributing to a technical brief developed by the World Health Organization and will guide future research and practice in this highly charged area of work. OSF Registries osf.io/ma4d9; https://osf.io/ma4d9. PRR1-10.2196/53888.

The audit revolution: Integrating artificial intelligence in detecting accounting fraud

This study aims to analyze the application of Artificial Intelligence (AI) in detecting accounting fraud in audits. The aim is to identify AI's efficiency, accuracy, and potential in detecting fraud and to explore the challenges and implications arising from using this technology in audit practice. This research is a type of qualitative research with a case study approach as the main focus and a literature study as a data triangulation approach. This research methodology will provide an in-depth understanding of the integration of artificial intelligence in detecting accounting fraud. The results show that AI improves efficiency and accuracy in detecting accounting fraud. AI techniques such as machine learning and natural language processing effectively identify fraud patterns. However, there are challenges, such as limitations of AI technology, ethical and data privacy issues, and barriers to accepting AI in the accounting industry. This research contributes to the accounting literature by highlighting how AI can change audit practices. It also offers guidance for accounting firms on utilizing AI to improve auditing and suggests directions for future research related to the development and integration of AI in accounting.

Quo vadis Indonesian public relations profession: dilemma on the rise of AI

The debate over AI has focused on its contrast with human employment. Simple perspectives are inadequate in public communication fields like Public Relations (PR). The conversation must shift from discussing AI's impact on human jobs to how AI will shape public communication as AI becomes more important in PR. To examine AI's effects on Public Relations, this study uses a literature review focusing on authority and public voice. Due to the risks of AI misuse, data discrimination, and data poisoning, public relations must carefully consider the implications of AI integration. Artificial Intelligence (AI) in Public Relations is studied using a literature review as its main method. Experts are consulted to identify AI's most significant challenges and opportunities in public relations. Public relations (PR) are affected by Artificial Intelligence (AI), which presents both opportunities and challenges. AI can boost public communication, but it also risks data misuse and discrimination. Ethical issues arise when AI represents the public's voice. This study provides useful insights into how Indonesia's Public Relations profession should approach Artificial Intelligence. PR professionals can overcome technical and ethical challenges and revolutionise public communication with AI integration. Therefore, ethical PR practice and the use of AI are becoming increasingly important.

DIGITAL TRANSFORMATION POTENTIAL: THE ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS

Purpose: The integration of Artificial Intelligence (AI) into business operations has become a pivotal driver of innovation and efficiency. This research paper explores the multifaceted landscape of AI implementation in businesses, examining the benefits of AI implementation. Theoretical Framework: The methodology adopted for this research comprises Semi-structured interviews with six key stakeholders’ executives, AI project managers, and digital transformation staff. Six additional globally businesses were chosen for case studies based on AI adoption maturity and digital transformation performance. Additionally, a qualitative content study of AI and digital transformation literature was conducted. Results and Discussion: The study revealed that as businesses continue to integrate AI, a balanced approach considering workforce implications is crucial to realizing the full potential of AI. The study also found out that the adoption of Artificial Intelligence in business is a nuanced process shaped by a confluence of factors. Organizational leadership, culture, resource availability, perceived benefits, regulatory considerations, data security, technology evaluation, and workforce readiness all play intricate roles. Thus, a holistic understanding of these factors empowers organizations to navigate the complex space of AI adoption, unlocking its transformative potential. Research Implications: The study's findings have many practical implications to businesses adoption of AI. AI, managers must promote innovation and adaptability. AI implementation requires human and technology capital. To reduce AI risks, legal and data security measures must be followed. To prepare employees for an AI-enabled workplace, organizations should adopt comprehensive training and development programs. Businesses may overcome AI implementation challenges and capitalize on its disruptive potential by addressing these five pragmatic elements. Originality and Value: This study contributes to the literature by offering insights into the nuanced landscape of AI adoption in businesses, emphasizing the crucial role of workforce considerations. It provides a comprehensive understanding of the multifaceted factors shaping AI implementation. The relevance and value of this research are evidenced by the holistic approach and contributes valuable guidance for organizations navigating the complexities of AI adoption, fostering innovation and efficiency while prioritizing workforce integration.