Automation Of Tasks Research Articles (Page 1)

Mechanical automation has become a core component of modern manufacturing. In recent years, the rapid development of machine learning and artificial intelligence technologies has provided new momentum for industrial automation. However, current Automated Machine Learning (AutoML) methods have significant limitations in hyperparameter search efficiency, model structure selection and handling large-scale data, which makes them less flexible in addressing the diverse needs of complex tasks. To address these issues, this paper proposes an innovative Automated Intelligent Machine Learning (AIML) framework that combines reinforcement learning and Bayesian optimization, aimed at achieving efficient model search and hyperparameter tuning. Specifically, AIML employs a Q-learning-based reinforcement learning strategy to more efficiently explore model structures. At the same time, a progressive multi-fidelity strategy is introduced in the Bayesian optimization process to accelerate hyperparameter search while balancing exploration and exploitation. Experimental results demonstrate that AIML performs excellently on the OpenML-CC18 public dataset, significantly outperforming traditional AutoML methods in both model performance and search efficiency, thereby proving its advantages in automated model optimization. This framework offers a more efficient and intelligent optimization solution for machine learning tasks in industrial automation, with broad application prospects.

In the era of Artificial Intelligence (AI) and automation, intelligent personal assistants have become integral to modern digital ecosystems. However, most existing assistants are cloud-dependent, limited in offline capabilities, and constrained to predefined functions. This paper proposes Jarvis AI, a Python-based intelligent personal assistant capable of performing multitasking operations through speech recognition, natural language processing (NLP), and automation modules. Jarvis can understand voice commands, interact conversationally, execute system operations (like opening applications, searching the web, sending WhatsApp messages, managing files), and even generate AI-based images through integrated APIs. Using modular architecture and machine learning, Jarvis learns user preferences and optimizes responses over time. The system integrates speech-to-text, text-to-speech, and task automation technologies with a graphical interface for real-time interaction, providing a scalable and efficient approach toward personalized digital assistance.

Artificial intelligence (AI) has emerged as a transformative force across various sectors, providing innovative solutions and enhancing operational processes. In the e-commerce domain, AI has significantly contributed to customer-centric approaches and strategic decision-making, fostering superior customer experiences. This study investigates the role and impact of AI in the Saudi e-commerce sector, focusing on the perspectives of female customers and retailers. Grounded in sociotechnical theory, the research employs a mixed-methods approach, combining quantitative surveys and semi-structured interviews. The quantitative findings demonstrate that AI-enabled e-commerce positively influences customer experience, customer satisfaction, and operational efficiency. Key AI capabilities, such as task automation, personalized recommendations, and predictive analytics, enhance online retail systems’ performance. The qualitative analysis highlights both the opportunities and challenges associated with AI adoption, emphasizing the need for specialized infrastructure and skilled professionals. Participants recommend addressing the skill gap and adopting phased implementation strategies to optimize AI integration. This study provides actionable insights and strategic recommendations for policymakers and stakeholders in the Saudi e-commerce sector.

Purpose The study proposes practices for the successful implementation of an automated electronic invoicing (e-invoicing) process based on a network model and identifies the main challenges to e-invoicing implementation. Design/methodology/approach The research employs a real-world case study at Hilti to identify potential improvement areas (PIAs) for the e-invoicing process reengineering and test the applicability of practices. It combines qualitative methods, including interviews and focus groups with experts, with quantitative data analysis using the Arena simulation software to compare the improvements of the reengineered implementation process. Findings The study reveals that process transparency, redundant activities, confusing communication, and task automation are the primary challenges to effective e-invoicing implementation. The proposed practices help overcome the identified challenges and streamline the implementation process. Research limitations/implications The research is based on a single case study, which may limit the generalizability of its findings. However, the proposed practices are designed to be flexible and customizable for replication in other companies. Practical implications The proposed recommendations offer practical guidance for businesses seeking to effectively implement e-invoicing, considerably reducing processing time and improving operational efficiency. They provide a framework for addressing common implementation challenges, particularly in network-based e-invoicing models. Originality/value This research contributes to the limited literature on practical e-invoicing implementation, particularly within network models. It provides a real-world application of process reengineering to e-invoicing and offers a valuable tool for businesses looking to overcome implementation challenges and accelerate their digital transformation.

The speed at which deep learning (DL) is developing has brought a new dawn in the field of mineral exploration and analysis of geological data, and it has provided extremely useful tools to meet the increasing complexity and size of the geoscience data. In this review the importance of DL applications in mineral exploration is discussed in general and within the fields of remote sensing image classification, geophysical anomaly detection, geochemical pattern recognition and drilling data interpretation in particular. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) and transformer models form DL architectures that have been found to be most effective in the automation and improvement of geological tasks previously limited by manual interpretation and a lack of scalability. Other challenges evidenced in the study are data heterogeneity, deficiency of labeled datasets, and interpretability of models. In addition, this paper addresses future research topics, with the integration of multimodal data, better explainability, transfer learning, and real-time decision-making systems. This review highlights how deep learning can change the face of geoscience today and how it can create a more novel, productive and controllable mineral exploration.

In today’s AI-driven digital world, Governance, Risk, and Compliance (GRC) has become vital for organizations as they leverage AI technologies to drive business success and resilience. GRC represents a strategic approach that helps organization using Large Language Models (LLMs) automation tasks and enhances customer service, while maintaining the regulatory complexity across various industries and regions. This paper explores a machine learning approach to evaluate Governance, Risk, and Compliance (GRC) risks associated with Large Language Models (LLMs). It utilizes Azure OpenAI Service logs to construct a representative dataset, with key features including response_time_ms, model_type, temperature, tokens_used, is_logged, data_sensitivity, compliance_flag, bias_score, and toxicity_score. These features are used to train a model that predicts GRC risk levels in LLM interactions, enabling organizations to improve efficiency, foster innovation, and deliver customer value, while maintaining compliance and regulatory requirements.

Quantitative Systems Pharmacology (QSP) is a powerful approach to provide decision-making support throughout the drug development process. QSP comes with many challenges in model development, validation, and applications. Traditional QSP workflows are limited by slow knowledge integration, labor-intensive model construction, inconsistent validation practices, and restricted scalability. In this work, we introduce QSP-Copilot, the first end-to-end AI-augmented solution designed to improve QSP modeling workflows by integrating a multi-agent system utilizing large language models (LLMs). QSP-Copilot provides modular support from project scoping and model structuring to model evaluation and reporting. Through the automation of routine tasks, QSP-Copilot reduces model development time by approximately 40% and improves methodological transparency through systematic documentation of literature sources and modeling assumptions. We demonstrate QSP-Copilot's application for two rare diseases of blood coagulation and Gaucher disease. In the blood coagulation case, automated extraction from ten peer-reviewed articles yielded 179 biological entity interaction pairs; out of these, only 105 unique mechanisms were retained after standardization. For Gaucher disease, screening nine articles produced 151 pairs, which were consolidated into 68 distinct biological interactions following the same post-processing workflow. The extraction precision for blood coagulation and Gaucher disease is 99.1% and 100.0%, respectively. QSP-Copilot extractions can be incorporated into effect diagrams with minimal expert filtering, significantly reducing the manual curation burden. The integration of AI-augmented workflows like QSP-Copilot represents a pivotal shift toward enhanced scalability and impact for QSP across the drug development pipelines, especially in disease areas where biological knowledge is sparse, such as rare diseases.

The automation of repetitive and labor-intensive tasks in construction has become increasingly important for enhancing safety, efficiency, and cost-effectiveness. Rebar tying, a fundamental step in reinforced concrete construction, is traditionally carried out manually, resulting in high labor demands, ergonomic risks, and limited precision. Existing automated solutions often fall short in addressing dynamic disturbances such as the impulsive backlash generated during the tying process. This paper presents a novel mobile manipulator designed for fully automatic rebar tying, featuring a reconfigurable locomotion mechanism and an RRR-type robotic arm integrated with a robust adaptive force control scheme. The proposed control algorithm actively compensates the unpredictable backlash forces during the tying operation, ensuring positional stability and consistent performance. Simulations conducted in MATLAB-Simulink demonstrate accurate trajectory tracking, even in the presence of external disturbances. These results highlight the potential of the proposed system to improve operational speed, reduce physical strain on workers, and advance automation in construction environments.

This study explores the role of human-centric Artificial Intelligence (AI) in enhancing organisational productivity across diverse Asian contexts. As AI technologies become integral to the digital transformation of workplaces, there is a growing emphasis on deploying AI systems that augment human capabilities while ensuring ethical, inclusive, and socially responsible results. This study investigates how organisations in Asia—specifically in Singapore, South Korea, India, Japan, and China—integrate human-centric AI principles to drive productivity improvements. Employing a qualitative comparative case study methodology, data from organisational documents, expert interviews, and productivity reports were thematically analysed from various secondary sources to uncover patterns and challenges in human-AI collaboration across diverse sociocultural contexts. The findings reveal that organisations adopting transparent and inclusive AI governance, such as Singapore’s multi-stakeholder frameworks and South Korea’s public-private partnerships, demonstrate significant efficiency gains and workforce satisfaction. Conversely, centralised governance models, such as China’s, emphasise social stability but reveal potential tensions surrounding employee participation in AI oversight. India’s grassroots-driven AI applications illustrate how contextualised human-centric AI can optimise productivity in underserved sectors. Japan’s integration of AI governance with social welfare underscores the ethical concerns unique to an aging society. This study highlights key productivity benefits, including task automation, decision-making acceleration, and innovation facilitation. However, challenges persist, notably workforce digital skill gaps, infrastructural limitations, and ethical governance complexities. This study advocates for culturally sensitive, adaptive governance, continuous digital literacy investment, and participatory change management to ensure that AI technologies augment rather than replace human potential. These findings offer valuable insights for policymakers and organisational leaders seeking to harness human-centric AI to sustainably boost productivity while safeguarding ethical standards in Asia’s pluralistic workplaces.

ABSTRACT Drawing from research on work automation and the digital transformation of work, this study examines the process involved in transforming work through Robotic Process Automation (RPA). Employing a multi-case qualitative research design, we investigate three organisations implementing RPA. We find that work transformation occurs progressively, involving sequences of actors’ frames, technology-work configurations, and their effects on work. These sequences evolve through three stages: convergent, diffusing, and divergent, driven by three mechanisms: goal alignment, technology legitimation and effect accumulation. The findings shed light on the stages involved in the transformation of work with RPA, revealing different interpretations of RPA technology and its various effects on work that emerge during this process. Our analysis also identifies mechanisms that sustain this transformation over time, highlighting the roles of automation teams and managers in facilitating this process. We offer a framework that links the immediate effects of task automation with RPA to the technology’s long-term effects on transforming work. The framework clarifies the enablers and barriers involved in RPA implementation as well as the solutions to leverage them, and demonstrates how RPA use evolves over time, producing different but complementary effects on work.

The rapid advancement of artificial intelligence (AI) has generated significant interest within the educational sector, particularly in information technology (IT) education. This study explored the current challenges, opportunities, and future directions of AI in IT education in the Philippines, a nation working to enhance its educational system in the face of digital transformation. Through a survey research design, data was collected from IT students, and educators. Results highlight the key challenges such as inadequate infrastructure, limited resources, gaps in AI literacy, and concerns around ethics and data privacy. Despite these challenges, opportunities such as personalized learning, streamlined administrative processes through task automation, and advancements in research through improved data collection, processing, and analysis provide hope for the integration of AI in IT curricula. Moving forward, efforts should focus on curriculum development, supportive policy frameworks, and continuous research to leverage AI's benefits in IT education. With robust government support, industry collaboration, and ethical AI practices, the Philippines can effectively use AI to transform IT education and equip students for a tech-driven future.

Endoscopy plays a crucial role in reducing the incidence and mortality of gastrointestinal cancers. Ensuring high procedural quality is essential to maximize its effectiveness, and comprehensive endoscopy reports documenting quality-related findings are indispensable. However, generating these reports requires endoscopists to perform numerous manual tasks, from evaluating factors necessary for reporting to documenting findings. Additionally, analyzing endoscopy quality based on reports and related data, such as pathological findings, is labor-intensive. These manual processes are prone to inaccuracies. Artificial intelligence (AI) holds promise for improving the efficiency, accuracy, and quality of endoscopy reporting. AI-driven automation of key evaluation tasks before documentation could significantly reduce the reporting burden on endoscopists while enhancing objectivity and overall report quality. Several AI applications have been explored, including real-time identification and labeling of key anatomical landmarks, examination time assessment, and recognition of endoscopic tools. While full automation of evaluation and documentation using AI remains an ideal yet distant goal, solutions such as voice recognition systems have been developed to alleviate the workload. These systems have demonstrated the potential usefulness in shortening reporting time. Evaluating quality indicators based on endoscopy reports is essential, and monitoring and feedback on these indicators are considered beneficial. Several quality indicators require integration with pathological findings and patient characteristics, which traditionally involves manual data processing. Natural language processing is emerging as a promising alternative to reduce this workload. Further advancements in AI-driven evaluation, documentation, and data integration are needed to fully realize its potential in improving endoscopy report quality.

The introduction of precision dairy farming has led to increasing automation of core tasks in dairy farming. However, the impact of these technologies on animal welfare remains the subject of ongoing debate. A previous study using the Welfare Quality ® (WQ) Assessment protocol to examine the impact of dairy farm automation on cattle welfare found an effect on animals’ behavior. While the WQ protocol is widely used to evaluate dairy cattle welfare, it is often criticized for subjectivity. Thus, more objective indicators are demanded. Concurrently, hair cortisol concentration (HCC) has emerged as a promising objective indicator of long-term stress in animals, offering a potential indirect welfare indicator. Thus, the present study aimed to investigate the relationship between farm automation levels and dairy cattle welfare, using HCC as a biomarker of animal stress. Furthermore, associations between HCC and WQ indicators are examined. Therefore, German farms ( n = 32) were categorized into three automation levels based on a newly developed classification system. On each farm, welfare assessment was performed using the WQ protocol, and hair samples were collected from 15 cows to determine HCCs. Median HCC values were compared across automation levels using the non-parametric Kruskal–Wallis test. Associations between HCC and WQ indicators were examined using multiple linear regression analysis. A trend of lower HCC levels with increasing automation was observed. Even so, the differences were not statistically significant, likely due to substantial variabilities in housing, management, and settings of automatic systems such as individual milking intervals or frequencies of feeding, bedding, and so forth. among farms. Significant correlations were found between median HCC per farm and the WQ protocol indicators “percentage of moderately lame cows,” “cows with at least one hairless patch and no lesion,” “tendency to be apathetic,” and “absence of injuries.” However, these indicators are not recommended as standalone measures of welfare. Nevertheless, consistent with the existing literature, our findings support lameness and integument alterations as key indicators of poor welfare in dairy cattle, which was also reflected in elevated HCC levels. As the number of highly automated farms is expected to increase in upcoming years, future studies with larger sample sizes are recommended.

The article discusses topical issues of digitalization and automation of specific process tasks in mining operations in the Arctic region, where implementation of large-scale commercial mining and geological information systems is limited by economic and technological factors. A brief analysis of mining and geological information systems used for automated control of the mining equipment, monitoring of the rock mass condition and control of the production environment parameters is provided, and the limitations and challenges of their implementation in small and medium-sized operations in Yakutia are identified, including the high cost of licenses, redundancy of functionality and the need for dedicated personnel training. Examples of computer applications that support certain mining processes are provided, demonstrating their functionality and practical significance. The practical necessity is confirmed by the cases when these software applications were used in production environment with account of the specific regional mining, geological and climatic features.The results demonstrate an increase in the efficiency of the process tasks, a reduction in the time required for design calculations, and improved control of the industrial safety. The outcome can be of interest to specialists in small and medium-sized mining enterprises.

This paper presents a case study of end-to-end (E2E) automation of corporate financial expense processing by combining generative AI (GenAI) and intelligent document processing (IDP) technologies with automation agents and shows the automation of intelligent tasks in a modern digital transformation environment. Although conventional RPA is effective in automating repetitive, rule-based, and simple tasks, it has limitations in handling unstructured data, responding to exceptions, and making complex decisions. In this study, we designed and implemented a four-step integration process, including automatic recognition of proofs such as receipts through OCR/IDP, item classification based on policy database, intelligent judgment support for exceptional situations through GenAI (LLMs), and human final decision and system learning (human-in-the-loop) through automation agents. As a result of the application to Company S, a large Korean company, quantitative effects such as reducing the processing time of branch receipt expenses by more than 80%, reducing error rates, and improving compliance rates were confirmed, as well as qualitative effects such as improving work accuracy and consistency, increasing employee satisfaction, and supporting data-based decision-making. In addition, the system learns from human judgment and continuously improves its ability to automatically handle exceptions, creating a virtuous cycle. This study empirically demonstrates that the organic combination of GenAI, IDP, and an automation agent overcomes the limitations of existing automation and is effective in realizing E2E automation of complex corporate tasks. In addition, it suggests the possibility of expansion to various business areas such as accounting, human resources, and purchasing in the future, as well as the development direction of AI-based hyperautomation. Received: 30 May 2025 | Revised: 30 July 2025 | Accepted: 22 September 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Cheonsu Jeong: Conceptualization, Supervision, Writing – original draft. Seongmin Sim: Software, Writing – review & editing. Hyoyoung Cho: Software, Writing – review & editing. Sungsu Kim: Software, Writing – review & editing. Byounggwan Shin: Software, Writing – review & editing.

The integration of Artificial Intelligence (AI) into education emerges as a transformative paradigm, enhancing the personalization of learning and the automation of administrative and assessment tasks. This article aimed to investigate how teacher training has been addressed in the literature in light of the advancement of AI in education, analyzing its influence as a tool to support teaching work. The bibliographic review supports AI as a support for educational development and teacher training, highlighting the need to critically integrate new technologies into pedagogical practice. The methodology followed the PRISMA protocol, with a systematic search in the Scielo, Scopus and Google Scholar databases, resulting in the final selection of 10 articles. The results and discussion were organized into three areas: the practical applications of AI in supporting teachers; ethical challenges, such as algorithmic biases and data privacy; and the imperative need for professional training for critical integration. It is concluded that AI is an effective support tool, whose potential will be maximized by teachers trained to use it critically and creatively, redirecting their time to irreplaceable human interactions. Investments in training programs that combine technical, critical and pedagogical skills are of the utmost importance.

BackgroundLarge Language Models (LLMs) have emerged as transformative tools in engineering, offering capabilities that streamline complex processes and support decision-making across diverse disciplines. Despite notable advancements in applications such as robotics task planning, autonomous driving, program repair, and technical documentation, challenges persist concerning ethical considerations, transparency, and accountability in safety-critical systems.PurposeThis study aims to conduct a systematic literature review (SLR) on the applications, challenges, and ethical implications of LLMs in engineering. The objective is to synthesize existing knowledge and identify research gaps to guide future investigations.MethodA comprehensive review of peer-reviewed publications from 2014 to 2024 was conducted, resulting in the selection of 23 relevant articles. These articles were classified into five thematic categories: automation of complex engineering tasks, knowledge generation and discovery, enhancing engineering education, ethical considerations and challenges, and integration with real-world engineering practices.ResultsThe review highlighted (i) increasing interest in LLM applications across multiple engineering domains, (ii) a growing emphasis on ethical and regulatory concerns related to LLM adoption, (iii) significant potential for enhancing productivity and fostering innovation, and (iv) a critical need for interdisciplinary collaboration to address reliability and scalability challenges.ConclusionsLLMs hold considerable promises for advancing engineering practices by automating tasks, facilitating knowledge discovery, and supporting education. However, ensuring ethical deployment, transparency, and model reliability remains essential. Future research should focus on developing frameworks for responsible AI adoption and fostering interdisciplinary efforts to overcome existing limitations.

Background - Human resources (HR) are a crucial and crucial element for a company because HR is the primary driver of operations and in achieving organizational goals. Effective HR management will improve productivity, performance, and a company's competitiveness in a competitive market. HR is an important company asset, especially in developing the company's business. Speed and accuracy in internal aspects, especially in employee management records, are currently irrelevant if done traditionally. Therefore, a company needs to utilize technology to increase competitiveness and strengthen the role of its human resources. Aim - This research aims to analyze as a utilize technology in the era of digital transformation that continues to develop and there is a shift from the role of HR to the role of technology, there is a strategic change and is oriented towards achieving overall organizational goals, namely a shift in the focus of human resource management from transactions to strategy. This helps human resource management professionals to focus more on more important things, such as the effectiveness of human resource planning, employee development, and strategic decision making that drive organizational success Design / methodology / approach - This study uses a research method model with a qualitative interpretative case study approach where this approach is used to implement OrangeHRM at gadget store X in Surabaya. Findings – The findings indicate that results of this study are where there is an application that can be used by employees to manage their personal data independently, through a module in the OrangeHRM application called Employee Self Service (ESS) for their company, they can also transfer routine operational tasks through this OrangeHRM application so that organizational management can focus on strategic planning to win the competition with competitors and improve aspects of organizational sustainability. Conclusion - OrangeHRM improves the efficiency of employees' daily work operations and is an application that allows them to independently manage their personal data. Through the Employee Self-Service (ESS) module within the OrangeHRM application, employees can also access their personal data management independently, thus helping the HR department improve the effectiveness and efficiency of employee data management. Research implication – This study contributes to future management studies, particularly in terms of efficiency, decision-making, and HR strategy development. The use of HRIS, including OrangeHRM, enables automation of HR tasks, improved data analysis, and data-driven decision-making, all of which contribute to increased management effectiveness. Limitations – This study has several limitations, include implementation costs, lack of flexibility, data security challenges, and internal resistance to change.

Radiology departments (RDs) face an increasing volume of data, images, and information, leading to a higher workload for radiologists. The integration of artificial intelligence (AI) presents an opportunity to optimize workflows and reduce the burden on radiologists. This review explores the role of advanced imaging analysis units (AIAUs) in enhancing radiological processes and improving overall patient outcomes. A literature review was conducted to assess the impact of AI-driven AIAUs on RD workflows. The study examines the collaboration between radiologists, technicians, and biomedical engineers in the extraction and processing of imaging data. Additionally, the integration of AI algorithms for task automation is analyzed. The implementation of AIAUs in RDs has the potential to enhance workflow efficiency by minimizing radiologists' workload and improving imaging analysis. These units facilitate collaborative work among radiologists, technicians, and engineers, fostering continuous communication, feedback, and training. AI algorithms incorporated into AIAUs support automation, streamlining pre- and postprocessing imaging tasks. AIAUs represent a promising approach to optimizing RD workflows and improving patient outcomes. Their successful implementation requires a multidisciplinary approach, integrating AI technologies with the expertise of radiologists, technicians, and biomedical engineers. Continuous collaboration and education within these units will be essential to maximize the benefits of emerging digital technologies in radiology.

Digitalization emerged as an urgent need for organizations during Covid -19 pandemic in remote working conditions which enabled connecting over digital platforms for all kinds of day-today business operations. This adoption of technology as a means of convenience and survival in the business world, also started influencing the employees’ work well – being having both positive and negative effects. Digitalization reached its peak in the recent years due to its potential benefits in terms of e-recruitment, automation, data – driven decision making, predictive analytics etc. Its adoption into HR practices led to enhanced employee experience causing increased level of employee engagement through tailored AI – driven HRD interventions. It has been observed that a more engaged employee shows more productivity, due to alignment of goals through a mutual identification with the organization. This enables the development of a greener organizational environment and formation of green attitude where employees work efficiently, utilizing the available resources in a responsible and effective manner producing eco-friendly products and services. In fact, digitalization plays a crucial role in reducing the organization’s carbon foot print and attaining organizational sustainability through automation of repetitive tasks saving time, money and energy, digital waste reduction leading to effective production, predictive analytics to understand the future trends and identify the employee needs to provide tailored HRD interventions aimed at enhanced employee well-being. The available literature provides evidence establishing the relationship between employee well-being and sustainable HR practices which is said to be strengthened by leveraging AI adoption in HR. Organizational sustainability relies on how the HR practices are suitable enough to sustain or retain its talented employees depending on enhanced employee well-being. And this further affects organizational sustainability depending on how much an employee feels connected and dedicated to work beyond employee roles and responsibilities to achieve organizational goals. However, this adoption of AI into HR and its integration, is not free from challenges, the most notable one being ‘techno stress’ caused due to work exhaustion. It arises from techno complexity, techno anxiety and techno insecurity, affecting the employees’ work well-being in terms of fear of losing their job as being unable to adapt to the technological advancements or ethical considerations, data privacy concerns or issues of trust and scepticism due to social influence etc. This widens the gap in the adoption and integration of AI into HR and it can further lead to decreased organizational performance as not being able to sustain amidst the challenges in the competitive business world of today. The objective of this study is to analyse the current state of research on the impact of digitalization on employee well-being and sustainable HR practices, to identify the benefits and challenges coming across its way of adoption in HRM and to provide recommendations for organizations to implement AI driven HR practices in a responsible and effective manner to enable enhanced employee well-being thereby leading to organizational sustainability. The study of the available literature has given a clarity about the impact of digitalization on employee well-being and sustainable HR practices highlighting its potential benefits along with the challenges it faces in the process of its adoption and integration. The findings of the studies emphasize on few possible ways to deal with the challenges, which could be fostering a culture of work based learning, digital leadership, knowledge sharing, knowledge diversity, employee self-efficacy, technological support at the organizational and individual level, maintaining work life balance through IT mindfulness, sustainable HR strategies to enhance employee work life balance by restricting use of technology etc. But the gap identified in the existing research pertains to the lack of investigation and presentation of preventive measures to avoid techno stress thereby aiming to achieve enhanced employee well-being and organizational sustainability. A systematic review approach based on the existing literature has been adopted and PRISMA framework has been used to identify and include literature aiming to establish the relationship between digitalization and its impact on employee well-being and sustainable HR practices and providing valuable insights for industry and HR professionals to understand the challenges hidden behind digitalization. This study provides a strong base for future scope of research on identifying digitalization as a job demand or as a job resource thereby leveraging it's adoption in HRM in a responsible manner providing sustainable strategies to cope with the challenges affecting employees’ well-being within an organization.