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Journal articles on the topic 'Predictive Enterprise Analytics'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

VANKOVYCH, Liubomyr, Oleksandr RULIKIVSKYI, Andrii KHAVYCH, and Roman SHENDIUK. "Predictive analytics in enterprise management." Economics. Finances. Law 2/2025, no. - (2025): 18–21. https://doi.org/10.37634/efp.2025.2.4.

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Predictive analytics has become a crucial tool in modern enterprise management, enabling organizations to anticipate future trends, optimize business processes, and mitigate risks. With the rise of digital transformation, companies increasingly rely on data-driven decision-making to enhance operational efficiency and maintain competitiveness. Predictive analytics leverages historical data, machine learning, and artificial intelligence to develop models that forecast outcomes and provide actionable insights. This paper explores the theoretical foundations of predictive analytics, its core metho

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Chauhan, Shailesh. "Leveraging Data for Enhancing Predictive Analytics in Enterprise Decision-Making." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 1561–76. https://doi.org/10.22214/ijraset.2025.67118.

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Predictive analytics is critical in a data-driven business environment, which enables any organization to make proactive and well-informed decisions. The paper depicts a discussion on how enterprises can leverage predictive analytics in obtaining value-driven insights for enhancing decision-making processes. It provides a deep understanding of the different types of data, data engineering, and methodologies that enrich predictive modeling. Real-world applications and case studies from retail, health, and finance lead the role of predictive analytics in optimized operations to measurable busine

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Talekar, P. R. "Predictive Analytics for Market Trends." International Journal of Advance and Applied Research 5, no. 10 (2024): 64–66. https://doi.org/10.5281/zenodo.11299015.

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Predictive analytics as their name implies, tries to predict the things that happen in the future. It supports most of the enterprise activities, but we are focused on marketing. Rather than just describing who, where and when, it helps in predicting the future using many algorithms of regression equations. It not only describes what’s happening but also predicts what will happen in the future, Predictive analytics helps the marketers make better decisions in today’s generation by providing them with insights into what is likely to happen in future and helps in recognizing the patt

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MITSENKO, Nataliia, Oksana VORONKO, Volodymyr BODNARIUK, and Bogdan KABATSI. "BUSINESS ANALYTICS AS A STRATEGIC RESOURCE FOR THE DEVELOPMENT AND REALIZATION OF THE ENTERPRISE POTENTIAL." Herald of Khmelnytskyi National University. Economic sciences 312, no. 6(2) (2022): 129–35. http://dx.doi.org/10.31891/2307-5740-2022-312-6(2)-24.

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The purpose of the article is to develop the methodological foundations of business analytics as a necessary resource for effective strategic management of the enterprise and realization of its potential. The concept of business analytics, its relevance and role for the development of the enterprise is considered. Business analytics is proposed to be considered as a process of collecting, accumulating, processing, analyzing and transforming data into business information necessary for optimizing business processes of the enterprise and strategic management with the help of various methodologic

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Xu, Yining, Yufei Ma, Ruijie Hu, and Hengrui Wang. "Predictive Analytics Techniques in Consumer Behaviour: A Literature Review." Advances in Economics, Management and Political Sciences 97, no. 1 (2024): 20–31. http://dx.doi.org/10.54254/2754-1169/97/20231516.

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Under fierce market competition, there is a need for enterprises to analyze and predict the behavior of consumers in order to improve the market competitiveness. We find that in the previous literature, there is little specific systematic summary and research on the methods to predict customer behavior. Thus, in this paper, we discuss relevant concepts of customer behavior and predict consumer behavior by studying the operation mechanism of Big Data Analysis (BDA), Decision Tree (DT) and Consumer Relationship Management (CRM). We also study the application of each technology to enterprises in

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Oyeyemi, Babatunde Bamidele. "Data-Driven Decisions: Leveraging Predictive Analytics in Procurement Software for Smarter Supply Chain Management in the United States." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 2 (2023): 703–11. https://doi.org/10.54660/.ijmrge.2023.4.2.703-711.

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This study examines how predictive analytics integrated into procurement software influences data-driven decision-making for smarter and more resilient supply chain management in the United States. The study was premised on five research objectives and five research questions. Anchored on the underpinnings of Resource-Based View Theory, the study uses Survey Research Method with questionnaire as the instrument of data collection. The population of the study comprises procurement and supply chain professionals working in mid- to large-sized enterprises across major industrial sectors in the Uni

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GORODETSKIY, Yuriy. "THE FORMATION OF MARKETING MECHANISMS FOR MANAGING BUSINESS DEVELOPMENT BASED ON PREDICTIVE ANALYTICS." Management 35, no. 1 (2023): 67–75. http://dx.doi.org/10.30857/2415-3206.2022.1.6.

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BACKGROUND AND OBJECTIVES. Improving the operation of a commercial enterprise is an integral task of a growing business. In modern conditions of rapid global technological growth, systems of analysis and statistics come out on top in terms of efficiency. The introduction of a business management system through marketing and predictive analysis will allow you to stand out in a competitive environment and make the company more sustainable and competitive. The solutions currently available cannot solve the emerging business problems. These software solutions are often complex, require large amoun

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Sadhu, Sravan Kumar. "Integrating AI/ML-Powered Predictive Analytics into Data Protection Strategies." International Journal of Computing and Engineering 7, no. 5 (2025): 42–60. https://doi.org/10.47941/ijce.2910.

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The integration of artificial intelligence and predictive analytics represents a transformative paradigm shift in organizational data protection strategies, moving beyond traditional reactive methodologies toward proactive, intelligent frameworks that anticipate and prevent failures before they manifest. Modern enterprises face unprecedented challenges with exponential data growth, increasingly complex IT infrastructures, and evolving threat vectors that render conventional backup and disaster recovery approaches insufficient for maintaining continuous availability and minimal data loss tolera

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Le, Tran Duc, Thang Le-Dinh, and Sylvestre Uwizeyemungu. "Cybersecurity Analytics for the Enterprise Environment: A Systematic Literature Review." Electronics 14, no. 11 (2025): 2252. https://doi.org/10.3390/electronics14112252.

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The escalating scale and sophistication of cyber threats compel enterprises to urgently adopt data-driven security analytics. This systematic literature review, adhering to the PRISMA protocol, rigorously synthesizes current knowledge by analyzing 65 peer-reviewed studies (2013–2023) from six major databases on enterprise-level cybersecurity analytics. Our findings reveal a significant industry-wide transition from traditional signature-based tools towards advanced cloud-enabled, big-data and artificial intelligence-powered techniques, where machine learning and graph-based models are increasi

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HANUSHCHAK-YEFIMENKO, Liudmyla, and Sanja GONGETA. "PREDICTIVE ANALYTICS AND MARKETING MANAGEMENT: IMPLEMENTING BUSINESS STRATEGIES THROUGH INTELLIGENT MODELS." Management 41, no. 1 (2025): 80–94. https://doi.org/10.30857/2415-3206.2025.1.5.

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INTRODUCTION. In today's digital economy and highly competitive market conditions, companies are faced with the need to quickly adapt to changes in consumer behavior, market trends, and technological trends. In these conditions, marketing management ceases to be an exclusively intuitive art and is transformed into a systematic activity based on data analysis, customer behavior prediction, and customer interaction management based on objective patterns. One of the key tools that allows for such a transformation is predictive analytics, which provides companies with the ability to proactively ma

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Karthikeyan Selvarajan. "AI-Driven Enterprise Supply Chain Intelligence: A Technical Deep Dive." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 612–17. https://doi.org/10.32996/jcsts.2025.7.2.66.

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This article explores the transformative impact of AI-powered data platforms on enterprise supply chain management, focusing on architecture, implementation strategies, and performance optimization. The article examines how modern enterprises are leveraging artificial intelligence to enhance their supply chain operations through advanced analytics, cloud integration, and machine learning capabilities. The article presents a comprehensive analysis of key technical components, including real-time data processing, predictive analytics, and security frameworks, while evaluating their effectiveness

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TELNOV, Yu F. . "DEVELOPMENT OF THE DIGITAL ENTERPRISES ARCHITECTURES." Scientific Works of the Free Economic Society of Russia 230, no. 4 (2021): 230–35. http://dx.doi.org/10.38197/2072-2060-2021-230-4-230-235.

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The article examines the impact of the use of digital technologies, such as new production technologies, industrial Internet, artificial intelligence, big data and predictive analytics on the construction of dynamic and flexible architectures of modern digital enterprises. As a tool that implements a single information space of production and business processes of the enterprise, the concept of Industry 4.0 and the architectural approach based on RAMI are proposed, which consider the use of the network structure of the connected world as the technological basis of the enterprise. To model the

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Niranjana, Mangaleswaran. "Building Autonomous Data for the Enterprise with Data Fulcrum." Advanced Journal of Robotics 1, no. 1 (2020): 8. https://doi.org/10.5281/zenodo.4021931.

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<strong><em>ABSTRACT</em></strong> <strong>M</strong>any years ago, the Data architecture included structured normalized and denormalized data, in a semantic layer, where the data was queried by a BI layer on top of it, providing Descriptive Analytics and Diagnostic Analytics. Descriptive Analytics is a visualization of the past/current state of the business; Diagnostic Analytics is identifying the root cause of the business’s past/current state. However, in recent years, the Enterprise’s Data Landscape is evolving to become complex, and the different types of Data – structur

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Eido, Warveen Merza, and Subhi R. M. Zeebaree. "Smarter Marketing with AI: How Cloud Technology is Changing Business." Asian Journal of Research in Computer Science 18, no. 4 (2025): 331–59. https://doi.org/10.9734/ajrcos/2025/v18i4623.

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The integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing cap

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Shahwan, Younis Ali, and Maseeh Hajar. "AI-Powered Database Management: Predictive Analytics for Performance Tuning." Engineering and Technology Journal 10, no. 05 (2025): 5100–5112. https://doi.org/10.5281/zenodo.15472012.

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As data volumes and query complexities grow in modern applications, ensuring optimal database performance has become increasingly challenging. Traditional manual tuning approaches are reactive, time-consuming, and often lack adaptability to dynamic workloads. This paper explores the integration of Artificial Intelligence (AI) and predictive analytics into database management systems (DBMS) for proactive performance tuning. By leveraging machine learning models, such as regression analysis and anomaly detection, AI-powered systems can forecast performance degradation, recommend tuning actions,

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Venu Gopal Avula and Surya Narayana Chakka. "Advanced predictive analytics in enterprise systems: Machine learning models for business forecasting and strategic decision support." World Journal of Advanced Engineering Technology and Sciences 7, no. 1 (2022): 271–77. https://doi.org/10.30574/wjaets.2022.7.1.0078.

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The integration of machine learning (ML) models in enterprise systems has revolutionized business forecasting and strategic decision-making processes. This paper presents a comprehensive analysis of advanced predictive analytics frameworks applied to enterprise environments, focusing on the implementation of various ML algorithms for business forecasting and strategic decision support. Through empirical evaluation of multiple predictive models including Random Forest, Support Vector Machines, and Neural Networks, we demonstrate significant improvements in forecasting accuracy and decision-maki

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Dipteshkumar Madhukarbhai Patel. "Leveraging analytics integration in enterprise systems: A technical perspective." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 969–85. https://doi.org/10.30574/wjarr.2025.26.1.1083.

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The integration of analytics within enterprise systems has emerged as a critical differentiator for organizational success, transforming how businesses leverage their information assets and make decisions. This article explores the technical architecture, implementation approaches, and challenges associated with embedding analytics capabilities into enterprise infrastructure. Beginning with an examination of foundational components including data integration layers, centralized repositories, and real-time processing frameworks, the discussion progresses to advanced analytics techniques such as

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Kabanda, Gabriel. "An Evaluation of Big Data Analytics Projects and the Project Predictive Analytics Approach." Oriental journal of computer science and technology 12, no. 4 (2020): 132–46. http://dx.doi.org/10.13005/ojcst12.04.01.

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Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research d

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Prasanna Kumar Natta. "Generative AI in enterprise systems: Moving beyond conversational AI." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1695–701. https://doi.org/10.30574/wjaets.2025.15.1.0408.

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Generative Artificial Intelligence has evolved beyond conventional applications into a transformative enterprise technology that fundamentally reshapes organizational capabilities across multiple domains. This article explores how Gen AI transcends simplistic chatbot implementations to deliver substantive value through enterprise process automation, intelligent software development, and advanced predictive analytics. Integrating Gen AI into enterprise ecosystems creates profound operational efficiencies by optimizing development lifecycles, automating complex workflows, and enabling dynamic bu

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Vallakonda, Vikas Reddy. "LEVERAGING PREDICTIVE ANALYTICS AND AI FOR REVENUE OPTIMIZATION IN ENTERPRISE ARCHITECTURE." INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND MANAGEMENT INFORMATION SYSTEMS 16, no. 2 (2025): 1400–1415. https://doi.org/10.34218/ijitmis_16_02_088.

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Iziduh, Ebehiremen Faith. "AI-DRIVEN COST OPTIMIZATION: HOW PREDICTIVE ANALYTICS IS REVOLUTIONIZING ENTERPRISE SPENDING." International Journal of Engineering Technology Research & Management (IJETRM) 07, no. 07 (2023): 147–56. https://doi.org/10.5281/zenodo.15558875.

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In the age of digital transformation, firms are expected to cut costs and still perform efficiently and compete in today’smarket. With predictive analytics relying on AI, organizations are finding entirely new ways to control costs and makesmarter choices aboutspending. The article discusses how adopting AI-based approaches for cost optimization shapesenterprise financial strategies, allowing for live monitoring of expenses, projecting early trends and alerting companiesto any inefficient actions. Using lots of data and smart algorithms, businesses are able to decide how to spend theirmo

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Sunkara, Siva Prasad. "Human-AI Collaboration in Healthcare: Leveraging Cloud-Based Enterprise Systems for Enhanced Patient Care and Operational Excellence." European Journal of Computer Science and Information Technology 13, no. 10 (2025): 124–36. https://doi.org/10.37745/ejcsit.2013/vol13n10124136.

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This article explores the transformative potential of human-AI collaboration in healthcare through cloud-based enterprise systems that integrate Customer Relationship Management, Enterprise Resource Management, and automation platforms. It determines how this technological convergence enhances patient care through personalized treatment protocols driven by predictive analytics while simultaneously optimizing administrative processes and operational workflows. The discussion encompasses the infrastructure requirements for implementing AI-powered healthcare systems, the application of predictive

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Sigitov, Andrei, Maksim Zheleznov, and David Koyadinovich. "Management of the technical condition of engineering equipment for packaging and dosing in the production of building materials." Construction and Architecture 13, no. 2 (2025): 5. https://doi.org/10.29039/2308-0191-2025-13-2-5-5.

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Introduction. The most widespread today, modern predictive analytics platforms such as: “Foresight Analytical Platform”, KNIME Analytics Platform, SAS Enterprise Miner, Loginom, have a number of features that limit their use in small businesses. Materials and method. As part of the work on the dissertation on the topic "Management of the technical condition of engineering equipment for packaging and dosing in the production of building materials", a hypothesis was put forward that the combined use of cluster and qualimetric analysis of big data allows for the prediction of changes in the phase

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Yosra, Ali Hassan, and R. M. Zeebaree Subhi. "Big Data Cloud Computing and AI-Driven Digital Marketing in Enterprise Systems." Engineering and Technology Journal 10, no. 04 (2025): 4597–615. https://doi.org/10.5281/zenodo.15303155.

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The integration of Big Data, Cloud Computing, and Artificial Intelligence (AI) has significantly transformed digital marketing and modern enterprise systems. These technologies enable advanced data analytics, predictive modeling, and real-time customer engagement, fostering more personalized marketing strategies and improving overall business efficiency. AI-powered tools, including machine learning algorithms, automated Customer Relationship Management (CRM) systems, and sentiment analysis platforms, facilitate the delivery of targeted content and enhance customer satisfaction. Additionally, c

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Suresh Kumar Maddala. "The future of Automated Machine Learning (Auto ML) in enterprise predictive systems." Global Journal of Engineering and Technology Advances 23, no. 1 (2025): 117–26. https://doi.org/10.30574/gjeta.2025.23.1.0097.

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This comprehensive article analyzes the evolving role of Automated Machine Learning (Auto ML) in enterprise predictive systems, exploring its transformative impact on organizational analytics capabilities. The article investigates prominent Auto ML frameworks—including Auto-WEKA, IBM's Auto AI, and Microsoft's Neural Network Intelligence—evaluating their distinctive architectures, capabilities, and enterprise applications. By synthesizing implementation experiences across diverse industry contexts, we identify key benefits of enterprise Auto ML adoption, including substantial efficiency gains,

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Clement Praveen Xavier Pakkam Isaac. "Cloud digital twins: Redefining enterprise infrastructure management with predictive analytics and automation." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 1496–515. https://doi.org/10.30574/wjaets.2025.15.1.0341.

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Cloud Digital Twins (CDTs) represent a paradigm shift in enterprise infrastructure management, offering organizations a revolutionary approach to simulate, optimize, and automate complex multi-cloud and hybrid environments. This comprehensive framework creates AI-powered virtual replicas of cloud infrastructure that mirror the behavior, configuration, and performance characteristics of production systems. Through a three-tier architecture encompassing Infrastructure Digital Twins, Policy Digital Twins, and Operational Digital Twins, organizations can anticipate system failures, optimize resour

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Charabuddi, Ranadheer Reddy. "Enhancing Financial Approvals with AI-Powered Predictive Automation: Optimizing Invoice Management and Vendor Risk Assessment." European Journal of Computer Science and Information Technology 13, no. 17 (2025): 27–38. https://doi.org/10.37745/ejcsit.2013/vol13n172738.

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This article explores the transformative potential of AI-powered predictive automation in enterprise financial approval processes. By leveraging advanced machine learning models trained on historical vendor data, organizations can implement intelligent systems that classify invoices based on rejection likelihood, streamlining workflows and reducing manual intervention. The predictive capabilities enable automatic processing of low-risk vendor invoices while flagging higher-risk submissions for thorough review. This article addresses traditional inefficiencies in financial document processing,

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Karthikeyan Selvarajan. "AI-powered big data platforms for enterprise analytics." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 2151–61. https://doi.org/10.30574/wjaets.2025.15.1.0441.

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This article presents a comprehensive analysis of AI-powered big data platforms that are revolutionizing enterprise-scale analytics across industries. The article examines the architectural evolution from traditional data warehouses to modern lakehouse paradigms, detailing how artificial intelligence integration transforms core data platform capabilities, including ingestion, storage, processing, and security. The article demonstrates quantifiable performance improvements, with organizations achieving reductions in processing time and cost efficiency gains compared to conventional systems. Thr

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Ozpinar, Alper, Muhammet Mustafa Alarçin, Volkan Halim, and Hakkı Kıvanç Yeker. "IntelliOps: A Generic Multi-Source Monitoring Framework with Predictive Analytics for Enterprise Infrastructure." European Journal of Research and Development 4, no. 4 (2024): 378–93. https://doi.org/10.56038/ejrnd.v4i4.588.

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This paper presents IntelliOps, a novel monitoring framework that integrates multi-source system monitoring with predictive analytics capabilities for financial technology infrastructure. The proposed framework aggregates performance metrics from multiple monitoring platforms and consolidates them through a unified API, providing comprehensive visibility into both hardware and software performance metrics. IntelliOps introduces an innovative approach by synthesizing traditional monitoring methodologies with advanced machine learning techniques, incorporating time series predictive models (LSTM

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Ravi Sankar Korapati. "Leveraging AI-Driven Predictive Analytics in Modern ERP Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1639–51. https://doi.org/10.32628/cseit251112193.

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This comprehensive article explores the transformative impact of AI-driven predictive analytics in modern Enterprise Resource Planning (ERP) systems. The article examines how the integration of artificial intelligence and machine learning capabilities has revolutionized organizational decision-making processes, operational efficiency, and strategic planning. The article investigates key application areas including financial forecasting, inventory optimization, and customer behavior analysis, while also addressing technical implementation considerations and system architecture requirements. The

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Madabhushini, Indraneel. "Explainable AI (XAI) in Business Intelligence: Enhancing Trust and Transparency in Enterprise Analytics." American Journal of Engineering and Technology 7, no. 08 (2025): 9–20. https://doi.org/10.37547/tajet/volume07issue08-02.

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The integration of Artificial Intelligence in Business Intelligence systems has fundamentally transformed enterprise analytics capabilities, enabling sophisticated pattern recognition, predictive modeling, and automated decision-making processes. However, the opaque nature of many AI algorithms presents significant challenges in business contexts where transparency, accountability, and regulatory compliance remain paramount concerns. This comprehensive technical review examines the role of Explainable AI in addressing these critical challenges, providing detailed insights into current methodol

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Sanka, Eswar. "AI-DRIVEN PREDICTIVE FINANCIAL ANALYTICS IN CLOUD ERP: TRANSFORMING ENTERPRISE FINANCIAL MANAGEMENT." INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING AND TECHNOLOGY 16, no. 1 (2025): 3028–47. https://doi.org/10.34218/ijcet_16_01_212.

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Zaman, Sadia, Mohammad Ariful Islam, Sumyta Haque, and Hosne Ara Mohna. "A REVIEW OF AI-POWERED DATA VISUALIZATION IN ENTERPRISE REPORTING: DASHBOARD DESIGN AND INTERACTIVE ANALYTICS." American Journal of Advanced Technology and Engineering Solutions 02, no. 01 (2022): 32–54. https://doi.org/10.63125/gabst658.

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The increasing complexity and volume of data within modern enterprises have significantly elevated the importance of advanced, intuitive, and adaptive visualization tools for reporting and analytics. This study systematically reviews and conducts a meta-analysis of existing research on artificial intelligence (AI)-powered data visualization dashboards, with a specific emphasis on their design, interactivity, and analytical effectiveness within enterprise contexts. It critically evaluates the role of AI technologies—including predictive analytics, anomaly detection, and natural language process

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Adarsh Vaid and Chetan Sharma. "Data-driven predictive maintenance and analytics in SAP environments enhanced by machine learning." World Journal of Advanced Research and Reviews 17, no. 2 (2023): 926–32. https://doi.org/10.30574/wjarr.2023.17.2.0019.

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This article explores how data-driven predictive maintenance, enhanced by machine learning, is transforming SAP environments. It highlights the benefits of integrating predictive maintenance with SAP, made possible by robust data collection, real-time monitoring, and predictive models. Incorporating these models within the SAP Analytics Cloud (SAC) significantly boosts their efficiency and scalability. The advantages include reduced downtime, cost savings, improved asset lifespan, enhanced operational efficiency, and data-driven decision-making. This approach not only anticipates equipment fai

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Ishola Bayo Ridwan. "Dynamic strategic foresight using predictive business analytics: Strategic modeling of competitive advantage in unstable market and innovation ecosystems." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 473–93. https://doi.org/10.30574/wjarr.2025.26.2.1730.

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In a global environment characterized by technological disruption, geopolitical volatility, and accelerated innovation cycles, traditional strategic planning methods are often insufficient for maintaining long-term competitiveness. Enterprises increasingly require dynamic strategic foresight—a future-oriented capability that integrates real-time data, scenario modeling, and predictive business analytics to anticipate change and proactively shape strategic responses. This paper examines how organizations can use predictive analytics not merely as a descriptive or forecasting tool, but as a stra

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Siva Reddy Pulluru. "The Intelligent Enterprise: A Technical Examination of AI-Driven Analytics in SAP S/4HANA Environments." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 4421–29. https://doi.org/10.30574/wjarr.2025.26.2.2117.

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The integration of artificial intelligence into SAP ERP systems heralds a fundamental transformation in enterprise resource planning, elevating these platforms from traditional transactional engines to sophisticated decision support systems. This technical examination explores how SAP's AI-driven analytics capabilities are redefining core business processes through predictive insights, cognitive automation, and intelligent decision frameworks. By leveraging SAP's comprehensive AI technology stack—including SAP AI Core, Joule, and Analytics Cloud—organizations can implement self-optimizing supp

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Gopalaswamy, Karthi. "Integrating Artificial Intelligence in Enterprise Architecture: Enhancing Cloud-Based Salesforce Solutions for Sales and Marketing Optimization." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 8, no. 1 (2025): 66–79. https://doi.org/10.60087/jaigs.v8i1.344.

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The integration of Artificial Intelligence (AI) in Enterprise Architecture (EA) is transforming cloud-based Salesforce solutions, driving significant advancements in sales and marketing optimization. AI-powered tools, such as predictive analytics, natural language processing, and machine learning, enhance decision-making, customer relationship management, and sales forecasting. This paper explores how AI-driven capabilities improve enterprise-wide data management, personalization, and automation within Salesforce platforms. By leveraging AI, businesses can streamline workflows, enhance custome

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Gupta, Raktim Das, Durjoy Biswas, Anish Paul Ronty, Saykot Kapali, and Md Shakil Khan. "Empowering Digital Transformation: The Strategic Role of Artificial Intelligence in Enterprise Innovation." European Journal of Theoretical and Applied Sciences 2, no. 6 (2024): 210–18. http://dx.doi.org/10.59324/ejtas.2024.2(6).16.

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This paper studies the transformative role of AI in driving digital innovation across enterprises, especially high-tech sectors. Applications of AI, such as machine learning, natural language processing, and computer vision, guarantee operational efficiencies, improved customer experiences, and smooth strategic decision-making. Key areas of AI integration include supply chain management, where predictive analytics and dynamic routing improve logistics; production processes enhance productivity, including predictive maintenance and quality control; and customer service, where NLP-driven chatbot

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Raktim, Das Gupta, Biswas Durjoy, Paul Ronty Anish, Kapali Saykot, and Shakil Khan Md. "Empowering Digital Transformation: The Strategic Role of Artificial Intelligence in Enterprise Innovation." European Journal of Theoretical and Applied Sciences 2, no. 6 (2024): 210–18. https://doi.org/10.59324/ejtas.2024.2(6).16.

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This paper studies the transformative role of AI in driving digital innovation across enterprises, especially high-tech sectors. Applications of AI, such as machine learning, natural language processing, and computer vision, guarantee operational efficiencies, improved customer experiences, and smooth strategic decision-making. Key areas of AI integration include supply chain management, where predictive analytics and dynamic routing improve logistics; production processes enhance productivity, including predictive maintenance and quality control; and customer service, where NLP-driven chatbot

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Alva, Lingareddy. "AI-Driven Data Mesh with Generative AI for Enterprise Analytics." European Journal of Computer Science and Information Technology 13, no. 37 (2025): 97–108. https://doi.org/10.37745/ejcsit.2013/vol13n3797108.

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This article explores the transformative integration of generative AI capabilities with Data Mesh architecture to revolutionize enterprise analytics. Beginning with examining traditional data architectures' limitations, the discussion highlights how centralized proceeds towards creating bottlenecks that impede innovation and time-to-insight. The Data Mesh paradigm is presented as a fundamental shift that decentralizes data ownership while maintaining federated governance. The integration of generative AI within this framework enables natural language interfaces, synthetic data generation, auto

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Rahman, Md Musfiqur. "DATA ANALYTICS FOR STRATEGIC BUSINESS DEVELOPMENT: A SYSTEMATIC REVIEW ANALYZING ITS ROLE IN INFORMING DECISIONS, OPTIMIZING PROCESSES, AND DRIVING GROWTH." Journal of Sustainable Development and Policy 01, no. 01 (2025): 285–314. https://doi.org/10.63125/he1tfg25.

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This meta-analysis offers a comprehensive synthesis of empirical evidence on the strategic role of data analytics in business development, with particular emphasis on its contributions to informed decision-making, operational process optimization, financial planning, risk mitigation, and customer-centric growth. Drawing from a dataset of 112 peer-reviewed empirical studies published between 2010 and 2024, the study employs a meta-analytic methodology following PRISMA guidelines to ensure methodological rigor and analytical depth. The research systematically categorizes analytics into descripti

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Кулинич, Мирослава, Іванна Матвійчук та Андрій Гадзевич. "АНАЛІТИКА ДАНИХ І ОБЛІКОВО-АНАЛІТИЧНЕ ЗАБЕЗПЕЧЕННЯ ЯК ОСНОВА ІННОВАЦІЙНОГО РОЗВИТКУ В ЕЛЕКТРОННІЙ КОМЕРЦІЇ". Economic journal of Lesya Ukrainka Volyn National University 1, № 41 (2025): 52–61. https://doi.org/10.29038/2786-4618-2025-01-52-61.

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Introduction. In the era of digital transformation, e-commerce is rapidly evolving, offering increasinglyconvenient opportunities for buying and selling goods and services. According to Statista, the global e-commercemarket is expected to reach USD 4.32 trillion by the end of 2025, while in Ukraine the figure may reach USD 3.32billion. In this context, data analytics has become a critical asset for enterprises to remain competitive and maximizereturns on their digital investments. However, despite numerous advances, businesses still face challenges in managingunstructured data and unlocking it

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Mithun Kumar Pusukuri. "Leveraging Observability-Driven Predictive Analytics for Cost-Effective Hybrid Cloud Migration on AWS." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1760–67. https://doi.org/10.32628/cseit241061216.

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This article presents an innovative framework that revolutionizes hybrid cloud migration strategies by integrating advanced observability tools with predictive analytics capabilities on AWS infrastructure. The article introduces a comprehensive approach that combines real-time monitoring, machine learning-driven prediction models, and automated risk mitigation strategies to optimize migration success rates and reduce operational costs. Through extensive experimentation across multiple enterprise environments encompassing numerous virtual machines and applications, the framework demonstrated si

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Byreddy, Maheswar Reddy. "Predictive Analytics and SAP Integration in Pharmaceutical Supply Chain Management: A Comprehensive Analysis." European Journal of Computer Science and Information Technology 13, no. 30 (2025): 104–10. https://doi.org/10.37745/ejcsit.2013/vol13n30104110.

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The pharmaceutical industry faces significant challenges in supply chain management, particularly in maintaining optimal inventory levels and ensuring timely medication delivery. This comprehensive article examines the integration of predictive analytics and SAP systems in pharmaceutical supply chain management, focusing on their transformative impact on operational efficiency and risk management. The article explores the evolution from traditional reactive approaches to modern predictive analytics, analyzing the implementation of SAP's technical framework for demand forecasting and inventory

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Dimitrovska, Ivana, and Toni Malinovski. "Creating a Business Value while Transforming Data Assets using Machine Learning." Computer Engineering and Applications Journal 6, no. 2 (2017): 59–70. http://dx.doi.org/10.18495/comengapp.v6i2.205.

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Machine learning enables computers to learn from large amounts of data without specific programming. Besides its commercial application, companies are starting to recognize machine learning importance and possibilities in order to transform their data assets into business value. This study explores integration of machine learning into business core processes, while enabling predictive analytics that can increase business values and provide competitive advantage. It proposes machine learning algorithm based on regression analysis for a business solution in large enterprise company in Macedonia,

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Thirumal Raju Pambala. "Leveraging Artificial Intelligence for SAP analytics cloud: A new era of smarter decision-making." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 240–47. https://doi.org/10.30574/wjaets.2025.15.2.0548.

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The integration of artificial intelligence in SAP Analytics Cloud represents a transformative shift in enterprise analytics, enabling organizations to harness vast amounts of data for improved decision-making. This advancement combines sophisticated machine learning algorithms, natural language processing, and automated intelligence features to enhance business intelligence capabilities and streamline operational processes. The platform's AI-driven capabilities facilitate more accurate forecasting, intelligent data analysis, and enhanced performance management while promoting collaborative ana

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Sannapureddy, Ramadevi. "AI-Driven Cloud Integration for Next-Generation Enterprise Systems: A Comprehensive Analysis." European Journal of Computer Science and Information Technology 13, no. 34 (2025): 13–24. https://doi.org/10.37745/ejcsit.2013/vol13n341324.

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The convergence of artificial intelligence and cloud computing represents a transformative paradigm in enterprise architecture, creating unprecedented opportunities for operational excellence and competitive differentiation. This comprehensive examination of AI-driven cloud integration explores the multifaceted impact across key domains of enterprise computing. The integration of reinforcement learning into cloud orchestration delivers substantial infrastructure cost reductions while simultaneously enhancing performance metrics and environmental sustainability. In security frameworks, unsuperv

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Ravi Kumar Kurapati. "Leveraging Predictive Analytics for Enhanced Financial Market Risk Assessment." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 214–22. https://doi.org/10.32996/jcsts.2025.7.4.26.

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Predictive analytics has emerged as a transformative force in financial market risk assessment, fundamentally altering how financial institutions identify, quantify, and mitigate potential threats. This article examines the integration of advanced statistical techniques, machine learning algorithms, and big data technologies into comprehensive risk management frameworks across various domains, including market risk, credit risk, and liquidity risk. Predictive analytics enables financial institutions to process vast quantities of structured and unstructured data, identifying complex patterns an

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Researcher. "THE INTEGRATION AND IMPACT OF ARTIFICIAL INTELLIGENCE IN MODERN ENTERPRISE RESOURCE PLANNING SYSTEMS: A COMPREHENSIVE REVIEW." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 79–88. https://doi.org/10.5281/zenodo.14050064.

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This comprehensive article examines the transformative integration of Artificial Intelligence (AI) within Enterprise Resource Planning (ERP) systems, analyzing its impact across various organizational domains and functional areas. The article investigates how AI technologies revolutionize traditional ERP frameworks through advanced process automation, intelligent analytics, and adaptive learning capabilities, fundamentally enhancing organizational efficiency and decision-making processes. Through detailed analysis of core applications, including automated workflow management, predictive analyt

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Kukkuhalli, Shreesha Hegde. "Optimizing Snowflake Enterprise Data Platform Cost Through Predictive Analytics and Query Performance Optimization." Journal of Artificial Intelligence & Cloud Computing 3, no. 6 (2024): 1–3. https://doi.org/10.47363/jaicc/2024(3)406.

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