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Journal articles on the topic 'Intelligent Workflows'
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1
Buell, Catherine A., Yolanda Gil, William P. Seeley, and Ricky J. Sethi. "Intelligent workflows for visual stylometry." AI Matters 3, no. 4 (2018): 14–17. http://dx.doi.org/10.1145/3175502.3175507.
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Guan, Wen, Tadashi Maeno, Brian Paul Bockelman, et al. "An intelligent Data Delivery Service for and beyond the ATLAS experiment." EPJ Web of Conferences 251 (2021): 02007. http://dx.doi.org/10.1051/epjconf/202125102007.
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The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. iDDS has been designed to intelligently orchestrate workflow and data management systems, decoupling data pre-processing, delivery, and main processing in various workflows. It is an experiment-agnostic service around a workflow-oriented structure to work with existing and emerging use cases in ATLAS and other experiments. Here we will present the motivation for iDDS, its design schema and architecture, use cases and current status, and plans for the future.
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Lin, Shiyu. "Research on the Impact of Artificial Intelligence Applications on the Workflow of Advertising Agencies." Advances in Economics, Management and Political Sciences 117, no. 1 (2024): 106–10. http://dx.doi.org/10.54254/2754-1169/117/20242031.
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Abstract: At present, AI has gradually penetrated various industries, but there is little application in the advertising industry. This study aims to understand how the introduction of AI will affect advertising agencies' workflows and develop strategies for practical AI applications. Before this, much research has also discussed the impact of AI on marketing, but rarely from the perspective of the workflow of advertising agencies. This study analyzes the advertising agencies' workflows and how they will change after the introduction of AI by using a literature review approach. A review and summary of many current studies culminates in the impact of AI on workflows and recommended strategies. The results show that AI affects workflows in four areas: market research, creative attributes, creative production, and intelligent advertising. The study recommends that advertising agencies prioritize experimenting with AI in both market research and the discovery of creative attributes.
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Palanki, Vijaya Chaitanya. "AUGMENTING DATA SCIENCE WORKFLOWS: A COMPREHENSIVE ANALYSIS OF AI-DRIVEN PRODUCTIVITY ENHANCEMENTS." International Journal of Core Engineering & Management 5, no. 6 (2018): 89–95. https://doi.org/10.5281/zenodo.14064200.
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The rapid growth of artificial intelligence (AI) technologies has significantly impacted various fields, including data science. This paper explores how AI can enhance productivity in the data science workflow, from data collection and preprocessing to model development and deployment. We review key AI technologies such as automated machine learning, intelligent data cleaning, and AI-assisted coding; discussing their potential to streamline data science tasks and improve efficiency. Additionally, we examine the challenges and limitations of integrating AI into data science workflows, as well as future research directions. This comprehensive review aims to provide data scientists, researchers, and practitioners with insights into leveraging AI for increased productivity in data science projects.
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Mahaboob Subhani Shaik. "Impact of AI on Enterprise Cloud-Based Integrations and Automation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1393–401. https://doi.org/10.32628/cseit241061180.
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Artificial Intelligence has transformed enterprise cloud-based integrations and automation, revolutionizing how businesses manage data, workflows, and applications across distributed environments. This comprehensive article explores the impact of AI on enterprise systems, examining key areas, including intelligent data integration, automated workflow optimization, and enhanced security measures. The article delves into technical implementation considerations, discussing infrastructure requirements and integration architectures while highlighting the substantial business benefits in operational efficiency, cost optimization, and strategic advantages. Additionally, it addresses the critical challenges organizations face in technical and organizational dimensions when implementing AI solutions, providing insights into successful adoption strategies and future considerations for enterprise AI integration.
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Bala, Anju, and Inderveer Chana. "Intelligent failure prediction models for scientific workflows." Expert Systems with Applications 42, no. 3 (2015): 980–89. http://dx.doi.org/10.1016/j.eswa.2014.09.014.
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Favour Uche Ojika, Wilfred Oseremen Owobu, Olumese Anthony Abieba, Oluwafunmilayo Janet Esan, Bright Chibunna Ubamadu, and Andrew Ifesinachi Daraojimba. "AI-Enhanced Knowledge Management Systems: A Framework for Improving Enterprise Search and Workflow Automation through NLP and TensorFlow." Computer Science & IT Research Journal 6, no. 3 (2025): 201–30. https://doi.org/10.51594/csitrj.v6i3.1884.
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In the era of digital transformation, organizations are increasingly adopting artificial intelligence (AI) to enhance knowledge management systems (KMS) and gain a competitive edge. This paper proposes a novel framework for AI-enhanced knowledge management that leverages Natural Language Processing (NLP) and TensorFlow to improve enterprise search capabilities and workflow automation. Traditional KMS often struggle with unstructured data, inefficient information retrieval, and fragmented workflows, leading to reduced productivity and decision-making inefficiencies. By integrating advanced NLP algorithms with TensorFlow’s scalable machine learning capabilities, the proposed framework addresses these challenges through intelligent content classification, semantic search, and automated knowledge extraction. The framework begins with data ingestion from diverse sources, including emails, reports, and databases, which are processed using NLP techniques such as named entity recognition, sentiment analysis, and topic modeling. TensorFlow models are then employed to train and fine-tune neural networks for document classification and intent recognition, enabling contextual understanding and prioritization of enterprise content. The system supports a dynamic knowledge graph that interlinks related concepts, documents, and workflows, facilitating real-time, query-responsive search and content recommendation. Moreover, the framework incorporates workflow automation by integrating AI models that identify repetitive tasks and suggest optimized processes using predictive analytics. This reduces manual effort, enhances task routing, and supports intelligent alerts and decision support mechanisms. A case study in a mid-sized enterprise demonstrates a 35% improvement in knowledge retrieval time and a 28% reduction in workflow execution delays after implementation. The proposed AI-enhanced KMS offers a scalable, adaptive solution for managing organizational knowledge in real-time, thus supporting knowledge workers with timely, relevant, and context-aware insights. It emphasizes the role of NLP for linguistic comprehension and TensorFlow for deep learning-based model optimization, providing a robust foundation for future enterprise intelligence systems. The research contributes to the growing field of AI in enterprise settings, highlighting the potential of integrated technologies to redefine knowledge access and operational efficiency. Keywords: Artificial Intelligence, Knowledge Management Systems, Enterprise Search, Workflow Automation, Natural Language Processing, TensorFlow, Semantic Search, Knowledge Graph, Machine Learning, Information Retrieval.
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Venkateswara Reddi Cheruku. "AI-orchestrated workflow automation in cloud-based hospital information systems: Enhancing efficiency and patient outcomes." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 1544–54. https://doi.org/10.30574/wjarr.2025.26.2.1763.
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This technical article explores the integration of artificial intelligence technologies into enterprise-grade Hospital Information Systems and Electronic Medical Record platforms to automate clinical and administrative workflows. As healthcare organizations face increasing pressure to improve operational efficiency while enhancing patient care quality, AI-orchestrated workflow automation emerges as a transformative approach. The article examines the technical architecture, implementation challenges, and measurable benefits of these systems, highlighting successful deployments across various healthcare settings through detailed case studies. From intelligent triage to revenue cycle optimization, these AI-enabled systems demonstrate significant potential to reduce administrative burden, enhance clinical decision-making, and improve patient outcomes while addressing longstanding inefficiencies in healthcare delivery.
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Rama Krishna Debbadi and Obed Boateng. "Developing intelligent automation workflows in Microsoft power automate by embedding deep learning algorithms for real-time process adaptation." International Journal of Science and Research Archive 14, no. 2 (2025): 802–20. https://doi.org/10.30574/ijsra.2025.14.2.0449.
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The advent of intelligent automation has revolutionized business processes by integrating artificial intelligence (AI) with robotic process automation (RPA) to enable adaptive, efficient, and data-driven decision-making. Microsoft Power Automate, a widely used low-code automation platform, offers a powerful environment for developing intelligent workflows. However, traditional automation lacks dynamic decision-making capabilities, which can be significantly enhanced by embedding deep learning algorithms. This integration enables real-time process adaptation, allowing workflows to learn from historical data, predict outcomes, and make proactive adjustments without human intervention. This study explores the impact of embedding deep learning models within Power Automate workflows to enhance real-time process adaptability. By leveraging Azure Machine Learning and AI Builder, businesses can deploy deep neural networks for tasks such as anomaly detection, demand forecasting, sentiment analysis, and intelligent document processing. The research presents real-world applications across industries, including predictive maintenance in manufacturing, customer sentiment-driven automation in retail, and fraud detection in financial services. Challenges such as model deployment complexities, latency in real-time inference, and the need for seamless integration between AI services and Power Automate are also analyzed. Strategies for overcoming these challenges, such as optimizing model performance, leveraging cloud-based AI services, and ensuring scalable automation architectures, are proposed. The findings suggest that embedding deep learning models into Microsoft Power Automate can drive significant improvements in process efficiency, decision accuracy, and operational resilience, ultimately enabling businesses to achieve higher levels of automation intelligence and competitiveness.
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Rama Krishna Debbadi and Obed Boateng. "Enhancing cognitive automation capabilities with reinforcement learning techniques in robotic process automation using UiPath and automation anywhere." International Journal of Science and Research Archive 14, no. 2 (2025): 733–52. https://doi.org/10.30574/ijsra.2025.14.2.0450.
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Cognitive automation represents the next frontier in Robotic Process Automation (RPA), enabling systems to learn, adapt, and optimize decision-making processes dynamically. Traditional RPA platforms, such as UiPath and Automation Anywhere, excel in automating rule-based tasks but lack the ability to handle complex, evolving scenarios that require adaptive intelligence. Integrating reinforcement learning (RL) techniques into RPA workflows offers a transformative approach to enhancing cognitive automation capabilities. RL enables bots to make intelligent, data-driven decisions by learning from their environment, optimizing workflows, and improving operational efficiency over time. This study explores the integration of RL algorithms within UiPath and Automation Anywhere to develop self-learning automation systems capable of handling non-deterministic processes. Key applications include intelligent exception handling, dynamic process optimization, and adaptive customer service automation. By leveraging RL-based decision models, RPA bots can continuously improve their performance, reduce error rates, and optimize workflows beyond predefined rules. The research also examines challenges such as computational complexity, model interpretability, and integration barriers within enterprise automation environments. Solutions such as cloud-based reinforcement learning frameworks, hybrid AI-RPA architectures, and explainable AI techniques are proposed to mitigate these challenges. The findings indicate that reinforcement learning can significantly enhance cognitive automation in RPA, enabling businesses to achieve higher levels of efficiency, adaptability, and intelligent decision-making.
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Shaik, Moyinuddeen. "Implementing AI-Driven Efficiency: Best Practices for Intelligent Order Processing in SAP." International Journal for Research in Applied Science and Engineering Technology 12, no. 1 (2024): 218–25. http://dx.doi.org/10.22214/ijraset.2024.57828.
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Abstract: In today's hyper-competitive business environment, streamlining order processing is crucial for maximizing efficiency, minimizing errors, and fostering customer satisfaction. Traditional methods, often manual and error-prone, struggle to keep pace with the demands of modern commerce. This paper delves into the transformative power of Artificial Intelligence (AI) in revolutionizing order processing within SAP. the leading Enterprise Resource Planning (ERP) system. The article explores the integration of Artificial Intelligence (AI) and Optical Character Recognition (OCR) technologies, shedding light on how this union reshapes traditional workflows. From streamlining data entry processes to empowering intelligent decision-making, the article delineates the best practices for organizations seeking to harness the power of AI in SAP order processing. The discussion encompasses key considerations before implementation, seamless integration strategies, and best practices in training AI models. Real-world case studies illustrate successful implementations, highlighting the tangible benefits achieved in terms of efficiency, accuracy, and overall workflow optimization.
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Mallika Rao, Basaveni Siri, and Sai Santosh Goud Bandari. "Replacing AI Agents for Backend." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–8. https://doi.org/10.55041/ijsrem.ncft011.
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Abstract—Creating modern applications using a heavy backend is a big task for developers. Front end can be created easily using scripting languages like HTML, CSS, java script, react frameworks etc. and many low code tools like WordPress, Figma, Bolt etc., but creating a fully working backend is a difficult part for developers, students or startups. Instead of writing thousands of lines of code we can build applications using AI agents. Sounds unreal right? But yes, it’s possible we can replace maximum of our backend logic using AI agents. Traditional backend development involves significant manual effort in writing, testing and maintaining code to support functionalities of backend such as authentication, database management, business logic and notifications. With emergence of AI and workflow orchestration platforms, we can see a high potential on how we can transform backend systems using these intelligent agents, with advancements in artificial intelligence and no-code orchestration platforms such as n8n, there is a drastic change where backend systems can be created, managed and evolved by AI agents. This paper explores how AI agents can transform backend development eliminating boilerplate code and introducing adaptive, scalable and intelligent architectures to design an application. Keywords—AI Agents, Backend Development, Workflow Automation, n8n, No-Code Platforms, Intelligent Systems, Application Architecture, Natural Language Processing, Low-Code Development, Large Language Models (LLMs), Orchestration Tools, Business Logic Automation, API Integration, Smart Workflows
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Beyyoudh, Mohammed, Mohammed Khalidi Idrissi, and Samir Bennani. "Towards a New Generation of Intelligent Tutoring Systems." International Journal of Emerging Technologies in Learning (iJET) 14, no. 14 (2019): 105. http://dx.doi.org/10.3991/ijet.v14i14.10664.
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In this paper, a new approach of intelligent tutoring systems based on adaptive workflows and serious games is proposed. The objective is to use workflows for learning and evaluation process in the activity-based learning context. We aim to implement a system that allow the coexistence of an intelligent tutor and a human tutor who could control and follow-up the execution of the learning processes and intervene in blocking situations. Serious games will be the pillar of the evalu-ation process. The purpose is to provide new summative evaluation methods that increase learner’s motivation and encourage them to learn.
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Trejo-Morales, Antonio, Edgar Adrián Franco-Urquiza, Hansell David Devilet-Castellanos, and Dario Bringas-Posadas. "HUB3D: Intelligent Manufacturing HUB System." Technologies 12, no. 7 (2024): 109. http://dx.doi.org/10.3390/technologies12070109.
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HUB3D represents a cutting-edge solution for managing and operating a 3D printer farm through the integration of advanced hardware and software. It features intuitive, responsive interfaces that support seamless interaction across various devices. Leveraging cloud services ensures the system’s stability, security, and scalability, enabling users from diverse locations to effortlessly upload and manage their 3D printing projects. The hardware component includes a purpose-built rack capable of housing up to four 3D printers, each synchronized and managed by a manipulator arm controlled via Raspberry Pi technology. This setup facilitates continuous operation and high automation, optimizing production efficiency and reducing downtime significantly. This integrated approach positions HUB3D at the forefront of additive manufacturing management. By combining robust hardware capabilities with sophisticated software functionalities and cloud integration, the system offers unparalleled advantages. It supports continuous manufacturing processes, enhances workflow efficiency, and enables remote monitoring and management of printing operations. Overall, HUB3D’s innovative design and comprehensive features cater to both individual users and businesses seeking to streamline 3D printing workflows. With scalability, automation, and remote accessibility at its core, HUB3D represents a pivotal advancement in modern manufacturing technology, promising increased productivity and operational flexibility in the realm of additive manufacturing.
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Viswanadhapalli, Vamsi. "Integrating AI and RPA in Pega for Intelligent Process Automation: A Comparative Study." International Journal of Scientific Research and Management (IJSRM) 12, no. 06 (2024): 1317–33. https://doi.org/10.18535/ijsrm/v12i06.ec11.
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The integration of Artificial Intelligence (AI) and Robotic Process Automation (RPA) within Pega’s Intelligent Process Automation (IPA) framework is fundamentally transforming enterprise workflow management. Traditional RPA, while effective in automating repetitive, rule-based tasks, lacks the adaptability and cognitive capabilities required for handling dynamic business processes. AI-enhanced RPA, on the other hand, leverages machine learning (ML), natural language processing (NLP), predictive analytics, and decision-making algorithms to enable self-learning automation systems that optimize workflows, reduce errors, and improve operational efficiency. This study conducts a comparative analysis between traditional RPA and AI-powered RPA within the Pega ecosystem, focusing on key performance indicators (KPIs) such as process execution time, accuracy, cost-effectiveness, scalability, and adaptability. By evaluating empirical data from real-world implementations, this research identifies the tangible benefits of AI-enhanced RPA in automating complex business operations across industries such as finance, healthcare, and e-commerce. The comparative assessment is structured around efficiency gains, error reduction, financial viability, and scalability, providing quantifiable insights into the transformative potential of AI-driven process automation. Using real-world case studies and industry benchmarks, this study demonstrates how AI-enabled automation in Pega improves workflow orchestration, predictive decision-making, and end-to-end automation of critical business functions. AI-powered bots can analyze data, predict process bottlenecks, automate exception handling, and enhance customer interactions, thereby surpassing the limitations of traditional RPA. The findings from this research emphasize the strategic advantages of AI-enhanced RPA in digital transformation efforts. Organizations that integrate AI-powered IPA within their automation strategies gain a competitive edge by achieving greater operational efficiency, reducing costs, and enabling scalable, intelligent automation solutions that adapt to changing business needs. This paper provides actionable recommendations for enterprises looking to leverage AI in Pega-driven automation frameworks, ensuring a seamless transition from rule-based automation to intelligent, self-optimizing workflows. Ultimately, the study concludes that AI-driven RPA in Pega is not just an incremental improvement over traditional RPA but represents a paradigm shift toward autonomous and cognitive automation, setting a new standard for enterprise-level process management.
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Löwe, Peter. "Introducing the new GRASS module g.infer for data-driven rule-based applications." Geoinformatics FCE CTU 8 (October 14, 2012): 17–28. http://dx.doi.org/10.14311/gi.8.2.
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This paper introduces the new GRASS GIS add-on module g.infer. The module enables rule-based analysis and workflow management in GRASS GIS, via data-driven inference processes based on the expert system shell CLIPS. The paper discusses the theoretical and developmental background that will help prepare the reader to use the module for Knowledge Engineering applications. In addition, potential application scenarios are sketched out, ranging from the rule-driven formulation of nontrivial GIS-classification tasks and GIS workflows to ontology management and intelligent software agents.
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Venkatesan Kannan. "The future of IoT in pharmaceutical laboratories: Transforming analytical lab workflows through connectivity." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 2915–24. https://doi.org/10.30574/wjaets.2025.15.2.0850.
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The Internet of Things (IoT) stands poised to revolutionize pharmaceutical laboratories through comprehensive connectivity solutions that address longstanding challenges in data management and operational efficiency. By enabling seamless communication between analytical instruments and centralized systems, IoT creates intelligent laboratory environments were data flows automatically without manual intervention. This digital transformation eliminates traditional data silos and transcription errors while providing unprecedented visibility into instrument performance, environmental conditions, and workflow patterns. The integration of sensor networks, cloud platforms, and advanced analytics establishes a foundation for data-driven decision-making that enhances both productivity and compliance. As IoT capabilities continue to evolve through integration with artificial intelligence, digital twin technology, and predictive modeling approaches, pharmaceutical laboratories can expect accelerated innovation timelines, improved experimental reproducibility, and optimized resource utilization. The convergence of these technologies marks a fundamental shift from fragmented analytical ecosystems toward cohesive, intelligent laboratory environments that balance scientific advancement with regulatory requirements.
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Zha, Xinyao. "Digital Intelligence Operation Center of Tongwei: Research on the Value Creation Path of Digital Technology Application." BCP Business & Management 36 (January 13, 2023): 142–50. http://dx.doi.org/10.54691/bcpbm.v36i.3402.
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In the digital era, new technologies such as big data and artificial intelligence are rapidly emerging. The speed of digital transformation in enterprises is also accelerating. Tongwei listed company has made an innovative exploration in the field of intelligent finance; and built a Digital Intelligence Operation Center based on artificial intelligence and Robotic Process Automation. This paper introduces the framework, digital technology and business application scenarios of Tongwei’s new financial sharing center, discussing its innovation points and its future development path. This research expands the path of building intelligent finance workflows for enterprises and provides new ideas for the digital transformation of companies. The results of this paper have some reference value for corporate business process optimization and structural reorganization.
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Jitender Jain. "Optimizing Payment Gateways in Fintech Using AI-Augmented OCR and Intelligent Workflow." Journal of Electrical Systems 17, no. 1 (2024): 115–27. https://doi.org/10.52783/jes.8179.
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This research delves into the optimization of payment gateway systems in the fintech industry using AI-augmented OCR and intelligent workflow automation, especially with a focus on Azure Functions. The paper explores how these tools can make payment processing more efficient, scalable, and cost-effective by combining serverless computing with AI-driven technologies. Cases from financial institutions adopting Azure services, which indicate KPIs for transaction speed, operational efficiency, and total cost of ownership. Surveys from IT experts and system architects give real-world insight into how AI and OCR apply in the payments system.The event-driven architecture of Azure Functions features AI-powered Optical Character Recognition (OCR) for the automatic processing of payment information and intelligent workflows optimizing decision making. It would improve processing time, eliminate human errors, and make transactions' data accuracy more efficient. In addition, features such as Azure Active Directory, encryption, and compliance with PCI DSS standards ensure the safe handling of sensitive payment data, thereby enhancing the security of these systems.The paper compares AI-augmented OCR and intelligent workflows with traditional server-based models with respect to metrics involving transaction speed, system scalability, and cost-effectiveness. Preliminary findings suggest that AI- and OCR-based integration with Azure Functions reduces costs, enhances scalability, and strengthens security in payment gateway systems. These technologies bring about a revolution in payment processing in fintech and improve the efficiency and save operational expenses.
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Mullins, Ryan, Deirdre Kelliher, Ben Nargi, Mike Keeney, and Nathan Schurr. "Challenges and Opportunities in Collaborative Vulnerability Research Workflows." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (2020): 420–24. http://dx.doi.org/10.1177/1071181320641094.
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Recently, cyber reasoning systems demonstrated near-human performance characteristics when they autonomously identified, proved, and mitigated vulnerabilities in software during a competitive event. New research seeks to augment human vulnerability research teams with cyber reasoning system teammates in collaborative work environments. However, the literature lacks a concrete understanding of vulnerability research workflows and practices, limiting designers’, engineers’, and researchers’ ability to successfully integrate these artificially intelligent entities into teams. This paper contributes a general workflow model of the vulnerability research process, and identifies specific collaboration challenges and opportunities anchored in this model. Contributions were derived from a qualitative field study of work habits, behaviors, and practices of human vulnerability research teams. These contributions will inform future work in the vulnerability research domain by establishing an empirically-driven workflow model that can be adapted to specific organizational and functional constraints placed on individual and teams.
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Cantini, Riccardo, Fabrizio Marozzo, Alessio Orsino, Domenico Talia, and Paolo Trunfio. "Exploiting Machine Learning For Improving In-Memory Execution of Data-Intensive Workflows on Parallel Machines." Future Internet 13, no. 5 (2021): 121. http://dx.doi.org/10.3390/fi13050121.
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Workflows are largely used to orchestrate complex sets of operations required to handle and process huge amounts of data. Parallel processing is often vital to reduce execution time when complex data-intensive workflows must be run efficiently, and at the same time, in-memory processing can bring important benefits to accelerate execution. However, optimization techniques are necessary to fully exploit in-memory processing, avoiding performance drops due to memory saturation events. This paper proposed a novel solution, called the Intelligent In-memory Workflow Manager (IIWM), for optimizing the in-memory execution of data-intensive workflows on parallel machines. IIWM is based on two complementary strategies: (1) a machine learning strategy for predicting the memory occupancy and execution time of workflow tasks; (2) a scheduling strategy that allocates tasks to a computing node, taking into account the (predicted) memory occupancy and execution time of each task and the memory available on that node. The effectiveness of the machine learning-based predictor and the scheduling strategy were demonstrated experimentally using as a testbed, Spark, a high-performance Big Data processing framework that exploits in-memory computing to speed up the execution of large-scale applications. In particular, two synthetic workflows were prepared for testing the robustness of the IIWM in scenarios characterized by a high level of parallelism and a limited amount of memory reserved for execution. Furthermore, a real data analysis workflow was used as a case study, for better assessing the benefits of the proposed approach. Thanks to high accuracy in predicting resources used at runtime, the IIWM was able to avoid disk writes caused by memory saturation, outperforming a traditional strategy in which only dependencies among tasks are taken into account. Specifically, the IIWM achieved up to a 31% and a 40% reduction of makespan and a performance improvement up to 1.45× and 1.66× on the synthetic workflows and the real case study, respectively.
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Vamsi Krishna Kumar Karanam. "Intelligent Automation for Streamlining Prior Authorization Workflows Integrated with EHRs Using Agentic AI." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 01–08. https://doi.org/10.32996/jcsts.2025.7.5.1.
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Prior Authorization (PA) processes represent a significant administrative burden in healthcare systems worldwide, contributing to treatment delays, clinician dissatisfaction, and increased operational costs. While traditional automation approaches have addressed discrete components of the PA workflow, they lack the adaptability and autonomy necessary for comprehensive optimization. This article introduces a framework for implementing Agentic Artificial Intelligence (AI) to transform PA workflows through seamless Electronic Health Record (EHR) integration. The evolution of authorization automation is traced through three distinct technological generations, demonstrating the progressive advancement from basic rule-based systems to sophisticated goal-oriented agents capable of autonomous decision-making. A multi-layered architectural framework is presented, detailing the specialized components that enable these systems to extract clinical data, navigate payer requirements, assemble documentation, and manage submissions with minimal human intervention. Implementation strategies are outlined, emphasizing the importance of preparatory assessment, phased deployment, and critical success factors, including vendor collaboration, stakeholder engagement, and data quality initiatives. The benefits of Agentic AI implementation are substantial, including dramatic reductions in processing time, decreased operational costs, improved approval rates, and enhanced patient experience. Despite challenges in technical integration, data standardization, regulatory compliance, and change management, healthcare organizations can achieve transformative improvements through thoughtful mitigation strategies. As interoperability standards evolve and implementation methodologies mature, Agentic AI promises to fundamentally reimagine prior authorization workflows, liberating clinical teams from administrative burdens while improving patient care outcomes.
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John, Selvaraj Arulappan. "AI in Payroll: Unlocking Efficiency through Process Discovery and Automation Workflows." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 1615–18. https://doi.org/10.5281/zenodo.14792213.
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The application of Artificial Intelligence (AI) in payroll systems is revolutionizing traditional workflows by enabling process discovery and automation. AI-driven tools analyze operational data to identify inefficiencies, uncover hidden patterns, and streamline payroll processes. Through intelligent automation, tasks such as payroll calculations, tax compliance, and error detection are executed with greater speed and accuracy, reducing manual interventions and associated costs. Advanced algorithms facilitate real-time monitoring and optimization, ensuring compliance with evolving regulations while enhancing system resilience against fraud and anomalies. This paper delves into the transformative role of AI in automating payroll workflows [1], showcasing how process discovery methodologies uncover bottlenecks and drive operational efficiency. Case studies are presented to highlight the measurable impacts of AI-powered automation on cost reduction, workforce productivity, and strategic decision-making. As organizations adapt to an AI-driven payroll landscape, the shift toward automated workflows signifies a new era of financial and operational excellence.
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Ramesh Pingili. "AI-driven intelligent document processing for banking and finance." International Journal of Management & Entrepreneurship Research 7, no. 2 (2025): 98–109. https://doi.org/10.51594/ijmer.v7i2.1802.
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The banking and finance industry is buried in paperwork—loan applications, compliance reports, risk assessments, and fraud investigations. Manual processing and outdated automation slow operations, increase costs and expose institutions to compliance risks (Vaultedge, 2023). AI-driven Intelligent Document Processing (IDP) is changing this by automating document workflows, accelerating approvals, and enhancing fraud detection. AI-powered IDP integrates machine learning, NLP, and RPA to reduce verification times, reduce errors, and strengthen compliance monitoring. Banks using AI-driven document automation process loan approvals 70% faster, improve fraud detection rates by 50%, and lower compliance costs by 40% (Rajput et al., 2025). This paper explores real-world applications of AI in banking document processing, highlighting efficiency gains, challenges, and future potential. As financial institutions move toward self-learning AI models, IDP is set to become a critical driver of speed, accuracy, and security in banking operations. Keywords: AI-Driven Document Processing, Banking Automation, Fraud Detection, Regulatory Compliance, Machine Learning in Finance, Robotic Process Automation (RPA), Intelligent Workflow Optimization.
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Grambow, Niklas, and Jan Kuschan. "Intelligente Sensoren verbessern die Ergonomie in der Arbeitswelt." ASU Arbeitsmedizin Sozialmedizin Umweltmedizin 2025, no. 01 (2024): 19–20. https://doi.org/10.17147/asu-1-411956.
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Intelligent sensors improve ergonomics in the working environment Demographic change presents new challenges for the industry. A growing proportion of older employees necessitates greater attention to ergonomic aspects and health risks in the workplace. By leveraging intelligent sensor technology, human actions can be analyzed, ergonomic conditions assessed, and workflows systematically optimized.
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Tamisier, Thomas, and Fernand Feltz. "Intelligent Agent for Modeling and Processing Decisional Workflows in Logistics." International Journal of E-Entrepreneurship and Innovation 2, no. 4 (2011): 49–57. http://dx.doi.org/10.4018/jeei.2011100104.
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The authors present the design and some implementation trials of Atlas, a new reasoning and decision making assistant used for processing complex and heterogeneous procedural workflows. Benefiting from a multicore implementation, Atlas includes different solving engines that are selected according to the intrinsic complexity of the problem being processed. The operational knowledge of Atlas is accessed through 2 different views. In an analytical view, the knowledge is modeled on elementary if-then rules, which are processed by a resolution engine written in the Soar architecture. A synthetic view offers a pictorial representation of all the knowledge, and in particular, shows the inter-dependence of the rules and their procedural references. In addition to allowing an efficient processing, the system checks the coherence of the knowledge and produces a justification of the decision with respect to relevant operational procedures.
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Aher, Pooja. "VOX - Aide." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 703–6. https://doi.org/10.22214/ijraset.2025.68359.
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Abstract: VOX-Aide is a revolutionary voice-integrated desktop assistant designed to transform productivity and well-being in the digital age. By seamlessly integrating intelligent coding support, personalized workflow management, and advanced analytics, VOX-Aide streamlines workflows while prioritizing user experience. This innovative platform offers multilingual support, email automation, code generation, and health analytics, all accessible through natural voice commands. Built on a robust architecture combining Electron.js, React.js, Node.js, and FastAPI, VOX-Aide ensures scalability and reliability. Its human-centred design focuses on enhancing efficiency and promoting a healthier work-life balance, making it an indispensable tool for professionals worldwide.
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Gangapatnam, Krupal. "Revolutionizing Enterprise Resource Planning Through AI Integration: A Technical Deep Dive." European Journal of Computer Science and Information Technology 13, no. 15 (2025): 55–69. https://doi.org/10.37745/ejcsit.2013/vol13n155569.
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The integration of Large Language Models (LLMs) in Enterprise Resource Planning (ERP) systems represents a transformative advancement in business process automation. The implementation focuses on four key areas: dynamic data querying through natural language processing, automated workflow communications, intelligent error management, and conversational AI integration. These innovations have revolutionized how organizations interact with their ERP systems, enabling intuitive data access, streamlined workflows, proactive error handling, and enhanced user experiences. The adoption of LLM-enhanced ERP solutions has demonstrated substantial improvements in operational efficiency, system reliability, and user satisfaction while reducing manual intervention and processing times across various industry sectors.
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J. Thaker, Dhaval, Hitesh R. Raval, and Juhi Khengar. "Leveraging AI for Enhanced Dataset Usability: Intelligent Summarization and Labeling for Academic-Industry Collaboration." Cuestiones de Fisioterapia 54, no. 2 (2025): 3867–77. https://doi.org/10.48047/3xjtdx63.
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In the era of digital transformation, artificial intelligence (AI) and cloud-based technologies arerevolutionizing university-industry collaboration by enhancing data accessibility, organization, andusability. Traditional data management approaches often suffer from inefficiencies, leading to fragmented,underutilized datasets. This research proposes an AI-powered framework that integrates intelligent labeling,automated dataset summarization, and vector-based retrieval to optimize dataset management. The systemefficiently categorizes and summarizes datasets by leveraging natural language processing (NLP) andembedding models, improving research workflows and knowledge transfer.
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Manuel Joy. "Agentic Workflows in Healthcare: Advancing Clinical Efficiency through AI Integration." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 567–75. https://doi.org/10.32628/cseit25112396.
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This article explores the transformative impact of agentic workflows in healthcare settings, focusing on their implementation and effectiveness in addressing critical challenges in clinical operations. Agentic workflows, powered by advanced artificial intelligence technologies including domain-specific Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems, represent a paradigm shift from traditional automation approaches. These intelligent systems demonstrate sophisticated capabilities in managing complex healthcare tasks, from clinical documentation to patient management. It examines the integration of these technologies across various healthcare domains, evaluating their performance through both technical metrics and clinical impact assessments. The article highlights significant improvements in operational efficiency, clinical decision support, and patient care delivery through the implementation of these advanced systems. Furthermore, it discusses future directions in healthcare AI, including enhanced subspecialty models, advanced natural language processing capabilities, and improved predictive analytics for population health management, providing a comprehensive overview of the evolving landscape of healthcare automation.
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Rayaprolu, Ranjith, Kiran Randhi, and Srinivas Reddy Bandarapu. "Intelligent Resource Management in Cloud Computing: AI Techniques for Optimizing DevOps Operations." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 397–408. http://dx.doi.org/10.60087/jaigs.v6i1.262.
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Efficient resource management is a cornerstone of cloud computing, particularly for DevOps operations where automation and scalability are critical. Traditional resource allocation approaches often fall short in dynamic environments, leading to over-provisioning, under-utilization, or service disruptions. This paper explores how artificial intelligence (AI) techniques can optimize resource management in cloud environments, enhancing the performance and efficiency of DevOps workflows. We examine methods such as predictive analytics, reinforcement learning, and anomaly detection, providing case studies and actionable insights for implementing intelligent resource management systems.
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Krupal Gangapatnam. "Autonomous cloud migration: Leveraging reinforcement learning for intelligent transformation." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 3545–55. https://doi.org/10.30574/wjarr.2025.26.1.1504.
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The emergence of autonomous cloud migration frameworks powered by reinforcement learning marks a transformative advancement in enterprise digital transformation. As organizations increasingly adopt cloud technologies, the complexity of migration processes demands more sophisticated solutions than traditional manual approaches. Reinforcement learning-based systems offer intelligent automation that optimizes resource allocation, enhances security measures, and streamlines migration workflows. These frameworks leverage advanced pattern recognition, dynamic workload management, and adaptive control mechanisms to ensure seamless transitions while maintaining operational stability. The integration of artificial intelligence and edge computing capabilities further enhances these systems, enabling real-time decision-making and proactive risk mitigation across complex cloud environments.
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Sui, Xianfu, Xin Lu, Yuchen Ji, et al. "Intelligent Oil Production Management System Based on Artificial Intelligence Technology." Processes 13, no. 1 (2025): 133. https://doi.org/10.3390/pr13010133.
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Production management serves as a pivotal component in the operational activities of oilfield sites, with the effectiveness of management practices directly influencing the success of developmental outcomes. To enhance the maintenance-free operational period of oil production systems, elevate management standards, and reduce overall operational costs, advanced technologies such as artificial intelligence (AI) and big data analytics have been strategically integrated into oilfield operations. These technologies are able to incorporate data resources from all stages of oilfield production, thus providing a comprehensive view of oilfield production and guidance for production. This study uses a series of diagnostic and predictive methods to construct a management system that allows for the comprehensive monitoring and fault diagnosis of oil production systems, which can ensure the intelligent management of oil production systems at multiple levels throughout their life cycle. Automated monitoring workflows and proactive analytical processes are at the heart of the framework, enabling real-time monitoring and predictive decision-making. This not only minimizes the likelihood of system failure but also optimizes resource allocation and operational efficiency.
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Zhou, Hong, KangMing Xu, Qiaozhi Bao, Yan Lou, and Wenpin Qian. "Application of Conversational Intelligent Reporting System Based on Artificial Intelligence and Large Language Models." Journal of Theory and Practice of Engineering Science 4, no. 03 (2024): 176–82. http://dx.doi.org/10.53469/jtpes.2024.04(03).16.
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As large language models gain traction in the financial sector, they are revolutionizing the workflows of financial professionals. From data analysis and market forecasting to risk assessment and customer management, these models demonstrate significant potential and value. By automating data processing tasks, they enhance productivity and empower professionals to derive deeper insights and make more precise decisions. This article explores the application of conversational intelligent reporting systems, leveraging artificial intelligence and large language models, within the financial industry. It examines how these systems streamline reporting processes, facilitate efficient communication, and contribute to informed decision-making, ultimately reshaping the landscape of financial market operations.
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Researcher. "REVOLUTIONIZING LOAN SYSTEMS THROUGH INTELLIGENT AUTOMATION." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1508–18. https://doi.org/10.5281/zenodo.14220828.
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This article examines the transformative impact of intelligent automation on loan origination processes at a leading financial institution, demonstrating substantial operational improvements and enhanced compliance adherence; implementing an automated Loan Origination System (LOS) significantly reduced processing time from multi-day to same-day approvals while drastically minimizing manual errors through sophisticated validation algorithms and automated checkpoints. This article presents a comprehensive technical architecture that seamlessly integrates dynamic case management, real-time compliance monitoring, and adaptive workflow systems, addressing the longstanding challenges of traditional loan processing methods. This transformation extends beyond process automation, encompassing intelligent decision support systems and predictive analytics that enable proactive risk assessment and market-responsive lending strategies. The solution's implementation has fundamentally reshaped loan approval workflows, enhancing regulatory compliance while maintaining the agility needed in modern banking operations. Through detailed analysis of system architecture, implementation methodologies, and performance optimization strategies, this article provides crucial insights for financial institutions embarking on digital transformation initiatives. This article demonstrates how intelligent automation can simultaneously address the seemingly conflicting goals of accelerated processing, enhanced compliance, and improved customer experience in loan origination. This comprehensive examination of both technical and operational aspects serves as a valuable blueprint for financial institutions seeking to modernize their loan processing systems while maintaining robust compliance frameworks in an increasingly competitive and regulated market environment. This article also explores the broader implications of such transformations on organizational efficiency, risk management, and customer satisfaction in the evolving landscape of digital banking.
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Sivaprasad Yerneni Khaga. "Intelligent Automation with Power Platform: Transforming Office 365 Workflows with AI-Powered Solutions." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 409–16. https://doi.org/10.32996/jcsts.2025.7.4.49.
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The integration of artificial intelligence within Microsoft's Power Platform represents a transformative shift in Office 365 workflow automation. This article explores how Power Apps, Power Automate, and Power BI leverage AI capabilities to streamline business processes with minimal coding requirements. Through AI Builder's prebuilt models and the synergy between Power Automate and Graph API, organizations can develop sophisticated automation solutions that enhance productivity across SharePoint-based environments. The introduction of Copilot in Power Apps marks a significant advancement, enabling natural language-driven application development that dramatically accelerates the creation of functional prototypes. Real-world implementations, such as Clifford Chance's legal document management system, demonstrate the tangible benefits of intelligent automation in professional service environments, highlighting the platform's ability to revolutionize how organizations interact with and leverage their Office 365 ecosystem.
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Sri, Rama Chandra Charan Teja Tadi. "Process Mining Driven by Deep Learning for Anomaly Detection in Intelligent Automation Systems." Journal of Scientific and Engineering Research 11, no. 1 (2024): 317–29. https://doi.org/10.5281/zenodo.15100742.
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Intelligent automation revolutionizes enterprise operations, software orchestration, and financial systems by integrating AI-driven decision-making, real-time workflow optimization, and large-scale automated execution. However, ensuring system security, operational efficiency, and adaptability in such dynamic environments poses significant challenges. Additionally, heterogeneous automation ecosystems, incorporating cloud-based microservices, robotic process automation (RPA), and distributed AI agents, demand a scalable and adaptive anomaly detection paradigm that can effectively operate across multi-domain environments, particularly in the banking and financial sector, where real-time fraud detection and compliance monitoring are critical. This theoretical concept envisions a deep learning-driven process mining methodology continuously evolving alongside automation workflows, offering a proactive approach to anomaly detection in .NET-based enterprise applications. This paradigm employs multi-layered workflow analysis, anomaly inference through graph neural networks (GNNs), deep feature extraction, and reinforcement learning-driven optimization to deliver a scalable, self-adaptive anomaly detection mechanism. Additionally, the approach integrates semantic workflow analysis, automated event correlation modeling, and multi-objective optimization to refine anomaly classification granularity and predictive modeling accuracy. By addressing high-dimensional event interdependencies, context-aware deviation analysis, and anomaly reasoning, this model aims to establish a resilient and transparent automation security paradigm that enables real-time workflow intelligence, cross-domain adaptability, and self-improving anomaly mitigation strategies for financial risk assessment, automated loan processing, and fraud analytics in banking environments. Future extensions of this theoretical approach will explore Interpretable Machine Learning (IML), adversarial robustness in deep anomaly detection, and blockchain-based anomaly verification to enhance anomaly interpretability, security, and compliance in enterprise automation ecosystems.
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Narendra Chennupati. "Securing the Automated Enterprise: A Framework for Mitigating Security and Privacy Risks in AI-Driven Workflow Automation." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 624–32. https://doi.org/10.32996/jcsts.2025.7.3.71.
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This article examines the evolving security and privacy challenges faced by enterprises implementing AI-driven workflow automation technologies. As organizations increasingly deploy artificial intelligence and robotic process automation to enhance operational efficiency, they simultaneously introduce novel security vulnerabilities and privacy concerns that traditional cybersecurity frameworks may inadequately address. Through a comprehensive analysis of current security practices, regulatory requirements, and emerging threats, this article proposes an integrated framework for risk mitigation in automated enterprise systems. The framework encompasses critical dimensions including data encryption strategies, adaptive access control mechanisms, privacy-preserving AI training methodologies, and specialized threat detection approaches tailored to the unique characteristics of intelligent automation. By synthesizing insights from both industry implementations and academic research, this article offers enterprise security practitioners actionable guidance for safeguarding automated workflows while enabling continued innovation. The article highlights the importance of security-by-design approaches, continuous monitoring, and governance structures specifically calibrated to the challenges presented by AI and RPA technologies in enterprise environments.
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Ashwin Vijaykumar Bajoria. "Cognitive Companion CRM: Proactive Intelligence for Personalized and Efficient Healthcare." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 727–36. https://doi.org/10.32996/jcsts.2025.7.5.81.
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The Cognitive Companion CRM represents a paradigm shift in healthcare information management, transitioning from reactive documentation tools to proactive clinical partners that anticipate needs and deliver contextualized insights. By integrating artificial intelligence capabilities including machine learning, natural language processing, and predictive analytics directly into clinical workflows, this architecture continuously monitors data streams to identify patterns and surface actionable information without requiring explicit user prompting. The system addresses fundamental healthcare challenges through multiple mechanisms: reducing provider cognitive load and administrative burden, enabling personalized patient care through risk factor identification, and improving resource allocation through predictive capabilities. Core components include predictive analytics for anticipating patient needs, an intelligent insights engine for contextualizing information, natural language processing for patient engagement, administrative automation architecture, robust data integration frameworks, and comprehensive privacy infrastructure. Implementation success depends on thoughtful workflow integration, patient journey mapping, provider adoption strategies, and rigorous return on investment analysis. Despite promising potential, significant challenges remain regarding data quality, validation protocols, ethical considerations, and stakeholder acceptance, necessitating continued interdisciplinary collaboration.
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Kugler, Karl, Maria Mercedes Tejada, Christian Baumgartner, Bernhard Tilg, Armin Graber, and Bernhard Pfeifer. "Bridging Data Management and Knowledge Discovery in the Life Sciences." Open Bioinformatics Journal 2, no. 1 (2008): 28–36. http://dx.doi.org/10.2174/1875036200802010028.
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In this work we present an application for integrating and analyzing life science data using a biomedical data warehouse system and tools developed in-house enabling knowledge discovery tasks. Knowledge discovery is known as a process where different steps have to be coupled in order to solve a specified question. In order to create such a combination of steps, a data miner using our in-house developed knowledge discovery tool KD3 is able to assemble functional objects to a data mining workflow. The generated workflows can easily be used for ulterior purposes by only adding new data and parameterizing the functional objects in the process. Workflows guide the performance of data integration and aggregation tasks, which were defined and implemented using a public available open source tool. To prove the concept of our application, intelligent query models were designed and tested for the identification of genotype-phenotype correlations in Marfan Syndrome. It could be shown that by using our application, a data miner can easily develop new knowledge discovery algorithms that may later be used to retrieve medical relevant information by clinical researchers.
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Kanfar, Rayan, Abdulmohsen Alali, Thierry-Laurent Tonellot, Hussain Salim, and Oleg Ovcharenko. "Intelligent seismic workflows: The power of generative AI and language models." Leading Edge 44, no. 2 (2025): 142–51. https://doi.org/10.1190/tle44020142.1.
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Advanced seismic data processing involves specialized methods often implemented through various software, requiring extensive expertise and time from geoscientists to execute geophysical workflows. Recently, large language models (LLMs) have demonstrated the ability to understand natural language, reason about domain-specific topics, and assist users through complex tasks. In this paper, we introduce an LLM-based autonomous agent for seismic data processing, focusing on full-waveform sonic data workflows. The proposed agent is shown to reliably understand user queries, select appropriate tools, and execute geophysical tasks such as bandpass filtering, data clipping, and frequency spectrum analysis. Safeguards and guardrails are incorporated into the agent to ensure operation within defined parameters, maintaining data security and integrity. By automating routine processes, the agent allows geoscientists to focus on higher-level decision-making while ensuring accuracy and consistency across geophysical tasks. As generative artificial intelligence evolves, integrating LLMs with other models has the potential to revolutionize seismic data analysis, making advanced geophysical tasks more accessible and scalable for users at all levels of expertise.
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Espinoza, Angelina, Yoseba Penya, Juan Carlos Nieves, Mariano Ortega, Aitor Pena, and Daniel Rodriguez. "Supporting Business Workflows in Smart Grids: An Intelligent Nodes-Based Approach." IEEE Transactions on Industrial Informatics 9, no. 3 (2013): 1384–97. http://dx.doi.org/10.1109/tii.2013.2256792.
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Santosh, Kumar Vududala. "Automating the Future: Enhancing ETL Workflows with RPA and Intelligent Automation." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 7, no. 2 (2021): 1–11. https://doi.org/10.5281/zenodo.14883126.
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With the more recent improvements in the BI tool and the exploitation of big data, Extract, Transform, and Load (ETL) processes have become critically important. ETL workflows, in particular, are also associated with problems like slowness, poor performance when managing large amounts of data, and the risk of human intervention. Analysing how RPA and IA can revolutionise ETL, this paper aims to discuss the opportunities for change within the field. In light of this fact, RPA and IA enhance the ways organisations work with data by automating routine efforts, increasing data quality, and enabling instant data processing. The paper also describes the strategies for implementing RPA and IA in ETL and looks at the pros and cons of the whole process. It has been found by applying real-time polymerase chain reaction that experimental results have less processing time and lesser errors. Using comparative analysis, the automated ETL will be contrasted with other traditional methods to show how better it is. The discussion chiefly focuses on future trends and the increasing importance of implementing intelligent automation, especially in the future development of big data.
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Venkateswara Rao Banda. "Agentforce: Next-Generation Enterprise Support Platform Powered by Salesforce." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3491–503. https://doi.org/10.32628/cseit25112716.
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Agentforce represents a transformative enterprise support solution built on Salesforce Service Cloud, demonstrating how systematic implementation of AI-driven automation, structured workflow management, and intelligent collaboration tools revolutionizes service operations. The platform's comprehensive technical framework encompasses sophisticated Einstein AI capabilities, automated workflow management, intelligent knowledge discovery, and collaborative problem-solving tools, enabling organizations to achieve substantial improvements in operational efficiency through proper configuration and implementation. Through a detailed analysis of implementation methodologies and configuration best practices across critical components, this study presents organizations with a structured framework for optimizing their support infrastructure. The platform's ability to scale globally while maintaining consistent performance through properly configured integration points and automated workflows positions it as a foundational solution for modern enterprises. As organizations navigate increasingly complex support requirements and hybrid work environments, Agentforce's comprehensive technical architecture, combined with proper implementation of AI-driven insights and automated process flows, provides a clear pathway for sustainable operational excellence. This article offers organizations detailed implementation guidance and configuration best practices to maximize platform capabilities and adapt their support strategies for future operational demands, establishing new standards for enterprise service management in the digital age.
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Dileep kumar Hamsaneni Gopalaswamy. "Oracle fusion AI agents: Uses, impacts, trends and future plans." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1679–86. https://doi.org/10.30574/wjaets.2025.15.1.0394.
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Oracle Fusion AI Agents represent a significant evolution in enterprise application technology, moving beyond traditional automation to offer intelligent collaboration within business workflows. These agents, powered by large language models and advanced technologies, are designed to interact with their environment, automate complex tasks, and work alongside employees in real-time.1 Their reasoning capabilities enable them to make judgment calls, create action plans, and manage workflows with varying degrees of autonomy. Oracle's strategic commitment to embedding AI across its Fusion suite is evident in the breadth of applications and the introduction of the Oracle AI Agent Studio, a platform aimed at empowering organizations to customize and extend these intelligent assistants.2 The rapid advancement and adoption of AI agents signal a transformative shift in how enterprises can optimize operations, enhance productivity, and ultimately shape the future of work.
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Karthik Kapula. "Agentic RPA: Enabling self-driven decision-making workflows in enterprise automation." Global Journal of Engineering and Technology Advances 23, no. 3 (2025): 072–81. https://doi.org/10.30574/gjeta.2025.23.3.0180.
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Modern enterprise environments have become increasingly dynamic, requiring automation tools that move beyond the fixed workflows of traditional Robotic Process Automation (RPA). This study introduces the concept of Agentic RPA an advanced automation framework that redefines conventional bots as intelligent, autonomous agents capable of making real-time decisions based on goals and context. Drawing from agent-based programming principles and cognitive system design, Agentic RPA incorporates technologies such as decision logic engines, real-time data interpretation, and large language models to support adaptive and context-aware automation. Centered on the UiPath ecosystem, the paper outlines a modular structure that integrates AI components with event-driven workflows and optional human oversight. Practical applications across ERP, CRM, and HRIS platforms demonstrate measurable benefits, including reduced process cycle times and significantly fewer manual interventions. These results highlight Agentic RPA’s value in enhancing operational agility and resilience, offering a forward-looking path for enterprises seeking scalable, intelligent automation that aligns with evolving business needs.
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Rama Krishna Komma. "AI-Powered Vendor Form Automation: A Comprehensive Technical Analysis of Enterprise Implementation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2975–83. https://doi.org/10.32628/cseit251112285.
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This article presents a comprehensive examination of an enterprise-level vendor form automation system that leverages artificial intelligence to revolutionize traditional data collection processes. The implementation combines modern web technologies with advanced machine learning algorithms to create an intelligent form generation and validation pipeline. Through a detailed technical article, the system's architecture integrates React and Redux for robust state management with AI-driven form customization capabilities. It investigates the performance optimization strategies, security considerations, and enterprise integration patterns that enable seamless vendor interactions. It demonstrates a significant improvement in operational efficiency, error reduction, and cost savings, establishing a new paradigm for intelligent form processing in enterprise environments. The success of this implementation provides valuable insights into the practical application of AI technologies for streamlining complex business workflows and enhancing vendor relationships in modern enterprise ecosystems.
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Pareek, Chandra Shekhar. "Accelerating Agile Quality Assurance with AI-Powered Testing Strategies." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem15369.
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The infusion of Artificial Intelligence (AI) into Agile software development is revolutionizing the domain of software testing, reshaping conventional methodologies to meet the demands of today’s complex and accelerated development cycles. Agile frameworks, renowned for their iterative workflows and adaptability, often encounter limitations in scaling to the velocity and intricacy of modern projects. AI emerges as a game-changer, introducing sophisticated capabilities such as hyper-automation, predictive defect analytics, and context-aware decision-making, thereby addressing these limitations with precision and scalability. This paper investigates the transformative influence of AI on Agile testing methodologies, with a focus on specific use cases, the operational efficiencies gained through AI-augmented workflows, and the seamless collaboration between human testers and intelligent systems. A comprehensive, architecture-driven framework for embedding AI into Agile testing cycles is presented, with empirical validation through case studies that demonstrate tangible improvements in accuracy, productivity, and sprint adaptability. Keywords: Agile Methodology, Artificial Intelligence, Quality Assurance, Predictive Analytics, Test-Driven Development (TDD), Behavior-Driven Development (BDD), Natural Language Processing
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Quang Hai Khuat. "Leveraging Generative AI for Data Engineering Workflows." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 120–40. https://doi.org/10.32996/jcsts.2025.7.3.14.
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Generative AI represents a powerful new layer of automation for data engineering. When leveraged responsibly, it can improve efficiency, reduce errors, and even enable non-experts to contribute to data workflows, all while allowing expert data engineers to tackle more ambitious challenges. We are witnessing the early stages of this transformation. By staying informed of the latest tools, adopting best practices for AI usage, and continuously refining the human-AI partnership, data engineering teams can ride this wave to build more intelligent, adaptive, and robust data pipelines than ever before. The future data platform may very well be a co-creation of human engineers and AI, each complementing the other’s strengths – and the organizations that embrace this symbiosis will be positioned at the forefront of the data-driven era.
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Tarun Kumar Chatterjee. "AI-Enabled Cloud Orchestration for Automated Workflows: A Paradigm Shift in Enterprise IT Operations." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 3289–96. https://doi.org/10.32628/cseit25112809.
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AI-enabled cloud orchestration represents a fundamental paradigm shift in enterprise IT operations, transforming traditional reactive management into proactive, self-optimizing environments. The integration of artificial intelligence with cloud infrastructure enables organizations to predict workloads, optimize resource allocation, and automate complex tasks across distributed computing environments. This technological evolution addresses the increasing complexity of enterprise workflows where manual intervention becomes impractical due to scale and intricacy. As digital transformation accelerates across industries, AI-enabled orchestration provides the intelligence required to enhance service delivery, reduce operational overhead, and achieve continuous optimization. The progression from manual configuration to sophisticated AI-driven systems marks a transition that delivers measurable advantages in resource utilization, incident prevention, and business agility. Through dynamic resource optimization, intelligent workload placement, and automated incident response, organizations can rapidly adapt to changing environments while minimizing operational costs capabilities that prove crucial for enterprise modernization in an increasingly competitive digital landscape.