Artificial Intelligence for Agile Business Solutions
Springer International Publishing (Verlag)
978-3-032-04737-3 (ISBN)
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The book Artificial Intelligence for Agile Business Solutions: Modernization of Outdated Systems" offers a comprehensive exploration of how AI technologies can transform legacy systems across various industries. The book covers a wide variety of topics such as Mathematical Modeling in Computer Science, the Evolution of Big Data in Modern Accounting Practice, and innovative strategies for integrating legacy security solutions with advanced AI-enabled capabilities. Additionally, it delves into modern network architectures crucial for supporting scalable and secure AI-driven solutions, including topics on cloud computing, edge computing, and network virtualization. By combining theoretical foundations with practical applications, this book provides valuable insights for developing resilient, efficient, and adaptive business infrastructures. Designed for researchers, IT professionals, business strategists, and graduate students, it bridges the gap between emerging technologies and real-world implementation, empowering organizations to excel in the era of digital transformation.
Mohammad Alsyasneh is a researcher, INTI International University, 71800 Negeri Sembilan, Malaysia. He is a dedicated researcher currently affiliated with INTI International University in Negeri Sembilan, Malaysia. With a master's degree in Business Administration, he has cultivated a strong foundation in academia and professional practice. His primary research interests include accounting, information technology, strategic management, and organizational behavior, reflecting his multidisciplinary expertise and a commitment to advancing knowledge in these critical areas of business and management. Alsyasneh's professional journey is marked by a wealth of experience gained through his managerial roles with the United Nations and various international non-governmental organizations (NGOs). Over the years, he has held multiple leadership positions, specializing in program management and support management. His work involved designing and executing complex programs, optimizing organizational processes, and fostering sustainable development initiatives in challenging and dynamic environments.
Dr. Jolly Masih is an associate professor and scientist in Business Analytics and Marketing at BML Munjal University, INDIA, ORCID: 0000-0002-8420-1517, Skills and Expertise: Big Data Analysis, R Programming, Consumer Behavior, Marketing, Agribusiness Management, Agricultural Economics With a diverse educational background, Dr. Masih brings a wealth of expertise to her role. She completed her Post-Doctoral Research at the esteemed Erasmus School of Economics, Erasmus University, Netherlands. Additionally, she holds an Executive Post-Graduation in Business Analytics from IIM Indore and earned her Ph.D. in Marketing and Big Data Analytics from IABM, India, in collaboration with Drexel University, USA. Dr. Masih's areas of expertise span business analytics, marketing strategy, consumer behaviour analysis, and data-driven decision-making.
1. Intelligent CoreTechnologies that facilitate AI-based transformation of core systems- AI for agile frameworks in traditional business units.- 2. Generative artificial intelligence (GenAI).- 3. Issues with AI feedback loops/ Recursive Loops- AI and Agile Project Management.- 4. Quantum Computing in Business Innovation- Machine Learning- Deep Learning- Data architecture coherence.- 5. Predictive Analytics.
| Erscheinungsdatum | 29.11.2025 |
|---|---|
| Reihe/Serie | Studies in Systems, Decision and Control |
| Zusatzinfo | X, 1254 p. 269 illus., 233 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Technik ► Bauwesen | |
| Schlagworte | Artificial Intelligence (AI) • business agility • Intelligent Core • legacy systems |
| ISBN-10 | 3-032-04737-4 / 3032047374 |
| ISBN-13 | 978-3-032-04737-3 / 9783032047373 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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