In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive. Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success.

The convergence of modern technology and social dynamics have shaped the very fabric of today’s organizations, making the role of Business Intelligence (BI) profoundly significant. Data-Driven Business Intelligence Systems for Socio-Technical Organizations delves into the heart of this transformative realm, offering an academic exploration of the tools, strategies, and methodologies that propel enterprises toward data-driven decision-making excellence. Socio-technical organizations, with their intricate interplay between human and technological components, require a unique approach to BI. This book embarks on a comprehensive journey, revealing how BI tools empower these entities to decipher the complexities of their data landscape. From user behavior to social interactions, technological systems to environmental factors, this work sheds light on the multifaceted sources of information that inform organizational strategies. Decision-makers within socio-technical organizations leverage BI insights to discern patterns, spot trends, and uncover correlations that influence operations and the intricate social dynamics within their entities. Research covering real-time monitoring and predictive analytics equips these organizations to respond swiftly to demands and anticipate future trends, harnessing the full potential of data. The book delves into their design, development, and architectural nuances, illuminating these concepts through case studies. This book is ideal for business executives, entrepreneurs, data analysts, marketers, government officials, educators, and researchers.

In an increasingly complex world, decision-makers face the challenge of optimizing multiple conflicting objectives across various scenarios. Multi-Criteria Decision-Making (MCDM) techniques have emerged as essential tools for addressing these challenges and offer methods to evaluate alternatives and minimize subjectivity. As the landscape of MCDM evolves with new approaches such as fuzzy set theory, rough set theory, and neutrosophic set theory, decision-making in situations involving varied and complex data becomes more reliable and consistent. Recent Theories and Applications for Multi-Criteria Decision-Making explores the latest trends and innovations in this field. The book includes thought-provoking input from renowned researchers who cover case studies, real-world applications, challenges, and cutting-edge methodologies. It highlights the integration of advanced technologies such as AI, big data, and IoT with MCDM, while offering practical insights into strategic decision-making in today's digital age. This volume serves as a valuable resource for scholars, practitioners, and researchers keen to improve their decision-making capacity.

Technology’s rapid advancement has revolutionized how organizations gather, analyze, and utilize data. In this dynamic landscape, integrating artificial intelligence (AI) into business intelligence (BI) systems has emerged as a critical factor for driving informed decision-making and maintaining competitive advantage. This integration allows business to respond quickly to market changes, personalize customer experiences, and optimize operations with greater precision. As AI-driven BI tools continue to evolve, they empower organizations to harness vast amounts of data more effectively, making strategic decisions that are both timely and data-driven, thereby securing their position in an increasingly competitive marketplace. AI-Powered Business Intelligence for Modern Organizations provides a comprehensive overview of this transformative intersection, addressing the diverse challenges, opportunities, and future trends in this field. By exploring the integration of AI into BI systems, the text delves into how advanced analytics, machine learning, and automation are reshaping the way businesses operate. Covering topics such as augmented analytics, decision-making, and sustainability metrics, this book is an excellent resource for business leaders and executives, data scientists and analysts, IT and technology managers, academicians, researchers, graduate and postgraduate students, consultants, industry experts, and more.

Artificial Intelligence (AI) is evolving at an unprecedented rate, changing industries and reshaping social landscapes. However, the question still stands: how can we make sure that, even with this growth, AI stays ethical and trustworthy? In an effort to investigate this issue, the book Trustworthy Artificial Intelligence in Industry and Society provides a thorough analysis of AI's potential to promote resilience, accountability, and trust in a variety of contexts. Chapter 1 explores the essential need for transparent and interpretable AI systems, starting with the foundation of Explainable Artificial Intelligence (XAI) and laying the framework for fostering trust among users, stakeholders, and society at large. In Chapter 2, deep learning and machine learning are explored, along with their applications, methods, and implementation challenges. In Chapter 3, the book delves into the impact of artificial intelligence (AI) on Environmental, Social, and Governance (ESG) initiatives. It specifically highlights the applications of AI in the financial services and investment sectors. We look at the adoption and application of AI in the construction sector in Chapter 4, offering some insight into the drivers, patterns, and obstacles that will shape the technology's future. The use of AI to improve supply chain sustainability and revolutionize the transportation industry is covered in Chapters 5 and 6, with a focus on generative AI technologies and ethical issues. Chapter 7 explores how artificial intelligence is affecting customer relationship management, highlighting how sentiment analysis is transforming customer loyalty and experience. This book seeks to shed light on the opportunities and difficulties that artificial intelligence (AI) brings to business and society by exploring these areas.

With the emergence of Artificial Intelligence (AI) in the business world, a new era of Business Intelligence (BI) has been ushered in to create real-world business solutions using analytics. BI developers and practitioners now have tools and technologies to create systems and solutions to guide effective decision making. Decisions can be made on the basis of more reliable and accurate information and intelligence, which can lead to valuable, actionable insights for business. Previously, BI professionals were stymied by bad or incomplete data, poorly architected solutions, or even just outright incapable systems or resources. With the advent of AI, BI has new possibilities for effectiveness. This is a long-awaited phase for practitioners and developers and, moreover, for executives and leaders relying on knowledgeable and intelligent decision making for their organizations. Beginning with an outline of the traditional methods for implementing BI in the enterprise and how BI has evolved into using self-service analytics, data discovery, and most recently AI, AI Meets BI first lays out the three typical architectures of the first, second, and third generations of BI. It then takes an in-depth look at various types of analytics and highlights how each of these can be implemented using AI-enabled algorithms and deep learning models. The crux of the book is four industry use cases. They describe how an enterprise can access, assess, and perform analytics on data by way of discovering data, defining key metrics that enable the same, defining governance rules, and activating metadata for AI/ML recommendations. Explaining the implementation specifics of each of these four use cases by way of using various AI-enabled machine learning and deep learning algorithms, this book provides complete code for each of the implementations, along with the output of the code, supplemented by visuals that aid in BI-enabled decision making. Concluding with a brief discussion of the cognitive computing aspects of AI, the book looks at future trends, including augmented analytics, automated and autonomous BI, and security and governance of AI-powered BI.

In the modern and data-driven business landscape, organizations face an increasingly complex challenge: how to harness the power of Artificial Intelligence (AI) and digital twin technology to stay competitive and relevant. This challenge is exacerbated by the rapid evolution of these technologies and the urgent need for businesses to adapt. AI continues to advance in areas such as language comprehension, pattern recognition, decision-making, and experiential learning, while digital twins are transforming into electronic replicas of real-world entities. Consequently, businesses grapple with the complexities of navigating this transformative convergence. Digital Twin Technology and AI Implementations in Future-Focused Businesses addresses the pressing issue of how AI and digital twin technology are altering the very fabric of business operations. It delves into the profound implications for productivity, creativity, and decision-making in organizations across diverse industries. Staying ahead of the technical curve is crucial, and this book equips readers with the knowledge and insights needed to thrive in this evolving business landscape.

Technology and entrepreneurship converge in the digital era, presenting many possibilities and hurdles. One of the most pressing issues facing entrepreneurs is the ability to harness the power of artificial intelligence (AI) to drive innovation and create sustainable businesses. While AI holds immense potential for transforming entrepreneurial ideas across various fields, many individuals and organizations need help understanding its practical applications and implications. Generating Entrepreneurial Ideas With AI offers a comprehensive solution to this challenge. By examining the intersection of AI and entrepreneurship from a multidisciplinary perspective, we provide readers with invaluable insights and strategies for leveraging AI to enhance their entrepreneurial endeavors. This book is designed for students, entrepreneurs, policymakers, and academics. It is a practical guide and roadmap for integrating AI into entrepreneurial practices. Through a series of in-depth analyses and case studies, we demonstrate how AI can effectively identify new business opportunities, optimize operations, and enhance the overall competitiveness of ventures.

The ever-evolving field of management in today's corporate world is marked by constant disruptions and turbulence. The emergence of Artificial Intelligence (AI) and Emotional Intelligence (EI) presents opportunities for automation, optimization, and effective leadership, but it also raises concerns about job displacement and the need to bridge the gap between these two domains. AI and Emotional Intelligence for Modern Business Management: Bridging the Gap and Nurturing Success offers solutions to closing the knowledge gap. This book provides comprehensive insights and practical strategies to academic scholars, researchers, practitioners, educators, and students. Targeting a diverse audience, this book serves as a solution-oriented resource for navigating the complexities of AI and EI in business management. By addressing both AI and EI, the book equips readers with the necessary tools to integrate these domains seamlessly into modern business management practices, stimulating informed discussions, inspiring innovative approaches, and fostering a deeper understanding of the opportunities and challenges posed by these emerging fields.