As the use of AI becomes more and more ubiquitous in companies around the world, managers charged with taking key decisions require resources to enable them to evaluate new projects effectively. The business case for AI projects is not necessarily clear cut and part of the reason for this is the lack of understanding on key decision criteria. AI touches on many ethical concepts - data privacy, validity and more importantly, its potential misuse. AI is often being used to replace human decision-making and there is often no real understanding of the implications of this. This book provides a detailed primer for practitioners without a deep technological background. It guides the reader through the basic issues and offers advice on ‘how to take decisions’. There is a dearth of such books currently available and this book aspires to fill a growing niche. ENDORSEMENTS: "This book is sure to offer value to business users, students and the general public." — K. Ananth Krishnan, Tata Consultancy Services "I highly recommend this book for the leader seeking an up-to-date review of AI to make strategic investments." — Kes Sampanthar, Innovation, BCG Brighthouse "The specificity of application in case studies and easy to understand definitions and recommendations make this a must read in the ever-growing field of literature around AI." — John C. Havens "The AI Enabled Organization is the perfect tool to embark on a thorough assessment of what AI means for your business." — Arno Fehler, Schmidt Kranz Group, Germany

We are entering the era of digital transformation where human and artificial intelligence (AI) work hand in hand to achieve data driven performance. Today, more than ever, businesses are expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions. AI-Enabled Analytics in Business is your Roadmap to meet this essential business capability. To ensure we can plan for the future vs react to the future when it arrives, we need to develop and deploy a toolbox of tools, techniques, and effective processes to reveal forward-looking unbiased insights that help us understand significant patterns, relationships, and trends. This book promotes clarity to enable you to make better decisions from insights about the future. Learn how advanced analytics ensures that your people have the right information at the right time to gain critical insights and performance opportunities Empower better, smarter decision making by implementing AI-enabled analytics decision support tools Uncover patterns and insights in data, and discover facts about your business that will unlock greater performance Gain inspiration from practical examples and use cases showing how to move your business toward AI-Enabled decision making AI-Enabled Analytics in Business is a must-have practical resource for directors, officers, and executives across various functional disciplines who seek increased business performance and valuation.

The AI enabled enterprise uses technology to continuously learn by monitoring its behavior and the environment as well as external knowledge sources in order to automate the decision-making and decision-implementation processes leading to continuous improvement over time. This book discusses the key challenges that organizations need to overcome in achieving an AI enabled enterprise: the role of digital twins in evidence-backed design, enterprise cartography that goes far beyond process mining, decision-making in the face of uncertainty, software architecture for continuous adaptation, democratized knowledge-guided software development enabling coordinated design, low code versus no code, and coherent design. For each challenge, the book proposes a line of attack along with the associated enabling technology and illustrates the same through a near real world use case.

Edge computational intelligence is an interface between edge computing and artificial intelligence (AI) technologies. This interfacing represents a paradigm shift in the world of work by enabling a broad application areas and customer-friendly solutions. Edge computational intelligence technologies are just in their infancy. Edge Computational Intelligence for AI-Enabled IoT Systems looks at the trends and advances in edge computing and edge AI, the services rendered by them, related security and privacy issues, training algorithms, architectures, and sustainable AI-enabled IoT systems. Together, these technologies benefit from ultra-low latency, faster response times, lower bandwidth costs and resilience from network failure, and the book explains the advantages of systems and applications using intelligent IoT devices that are at the edge of a network and close to users. It explains how to make most of edge and cloud computing as complementary technologies or used in isolation for extensive and widespread applications. The advancement in IoT devices, networking facilities, parallel computation and 5G, and robust infrastructure for generalized machine learning have made it possible to employ edge computational intelligence in diverse areas and in diverse ways. The book begins with chapters that cover Edge AI services on offer as compared to conventional systems. These are followed by chapters that discuss security and privacy issues encountered during the implementation and execution of edge AI and computing services The book concludes with chapters looking at applications spread across different areas of edge AI and edge computing and also at the role of computational intelligence in AI-driven IoT systems.

An essential book on the applications of AI and digital twin technology in the smart manufacturing sector. In the rapidly evolving landscape of modern manufacturing, the integration of cutting-edge technologies has become imperative for businesses to remain competitive and adaptive. Among these technologies, Artificial Intelligence (AI) stands out as a transformative force, revolutionizing traditional manufacturing processes and making the way for the era of smart manufacturing. At the heart of this technological revolution lies the concept of the Digital Twin—an innovative approach that bridges the physical and digital realms of manufacturing. By creating a virtual representation of physical assets, processes, and systems, organizations can gain unprecedented insights, optimize operations, and enhance decision-making capabilities. This timely book explores the convergence of AI and Digital Twin technologies to empower smart manufacturing initiatives. Through a comprehensive examination of principles, methodologies, and practical applications, it explains the transformative potential of AI-enabled Digital Twins across various facets of the manufacturing lifecycle. From design and prototyping to production and maintenance, AI-enabled Digital Twins offer multifaceted advantages that redefine traditional paradigms. By leveraging AI algorithms for data analysis, predictive modeling, and autonomous optimization, manufacturers can achieve unparalleled levels of efficiency, quality, and agility. This book explains how AI enhances the capabilities of Digital Twins by creating a powerful tool that can optimize production processes, improve product quality, and streamline operations. Note that the Digital Twin in this context is a virtual representation of a physical manufacturing system, including machines, processes, and products. It continuously collects real-time data from sensors and other sources, allowing it to mirror the physical system’s behavior and performance. What sets this Digital Twin apart is the incorporation of AI algorithms and machine learning techniques that enable it to analyze and predict outcomes, recommend improvements, and autonomously make adjustments to enhance manufacturing efficiency. This book outlines essential elements, like real-time monitoring of machines, predictive analytics of machines and data, optimization of the resources, quality control of the product, resource management, decision support (timely or quickly accurate decisions). Moreover, this book elucidates the symbiotic relationship between AI and Digital Twins, highlighting how AI augments the capabilities of Digital Twins by infusing them with intelligence, adaptability, and autonomy. Hence, this book promises to enhance competitiveness, reduce operational costs, and facilitate innovation in the manufacturing industry. By harnessing AI’s capabilities in conjunction with Digital Twins, manufacturers can achieve a more agile and responsive production environment, ultimately driving the evolution of smart factories and Industry 4.0/5.0. Audience This book has a wide audience in computer science, artificial intelligence, and manufacturing engineering, as well as engineers in a variety of industrial manufacturing industries. It will also appeal to economists and policymakers working on the circular economy, clean tech investors, industrial decision-makers, and environmental professionals.

As the spectrum of the internet of things (IoT) expands, artificial intelligence (AI)-assisted agile IoT is the way forward for sustainable finance. The depth of agile IoT has changed the financial market, and it may quickly evolve as a powerful tool in the future. The convergence of AI and IoT techniques will significantly extract valuable financial information and offer better services to customers. Some of the potential benefits of AI-assisted agile IoT for FinTech include prompt customer support, in-door client navigation, on-site queue management, improved customer experience, security and authenticity, wireless payments, increased business efficiency, self-checkout services, and business automation. There is no doubt that leveraging the complete potential of AI-assisted agile IoT will result in the creation of a new and innovative financial system. AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems presents the advances in AI-assisted agile IoT for financial technologies (FinTech). It further explains the new applications, current issues, challenges, and future directions of the field of AI-assisted agile IoT for FinTech applications and ecosystems. Covering topics such as consensus algorithms, IoT-based banking, and secure authentication, this premier reference source is an excellent resource for business executives and managers, IT managers, librarians, students and faculty of higher education, researchers, and academicians.

Streamline your business operations by leveraging AI across key functions using practical examples and insightful case studies Key Features Uncover the practical applications of Dynamics 365 AI across sales, customer service, marketing, and finance Explore Dynamics 365’s integration with advanced AI tools such as OpenAI, Azure Open AI, and MS Copilot Learn from real-world examples and case studies Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIf there is one hot topic being discussed in every boardroom meeting today, it’s AI. With Microsoft Dynamics 365 AI proving to be a game-changer, it’s essential for business professionals to master this tool. Microsoft Dynamics 365 AI for Business Insights will help you harness AI across key business functions to streamline processes and enhance customer experiences. Written by a seasoned professional with 15+ years of experience, this book guides you through Dynamics 365 AI’s practical applications across sales, customer service, marketing, and finance departments. You'll learn how to enhance customer experiences, streamline sales processes, optimize marketing strategies, and improve financial forecasting. This book also explores the integration of generative AI tools such as OpenAI Service, Azure Open AI, language models, and Microsoft Copilot within the Dynamics 365 ecosystem. With real-world examples, case studies, and expert insights, you’ll discover the transformative potential of this powerful toolkit. As well as driving sales insights and implementing fraud protection, you’ll explore emerging AI trends, Microsoft's roadmap for Dynamics 365 AI, and the upcoming features. By the end, you’ll be all set to unlock new growth opportunities using Dynamics 365 AI. What you will learn Overcome common challenges in Dynamics 365 AI implementation with ease Delve into the practical applications of advanced generative AI tools Integrate cutting-edge tools such as OpenAI Service, Azure Open AI, language models, and Microsoft Copilot with Dynamics 365 AI Derive insights from real-world examples of successful AI implementation Discover best practices and strategies for leveraging AI to find useful business insights and enhance operations Explore Microsoft's roadmap for Dynamics 365 AI and the forthcoming trends in AI for business Who this book is for This book is for Dynamics 365 consultants, architects, and IT managers, willing to implement AI in their organizations. Business consultants advising on business technology, especially those focused on Microsoft Dynamics 365, will find it useful for enhancing their skills. A basic understanding of Microsoft Dynamics 365 and its suite of business applications will be beneficial as the book’s focus is on implementing the tool’s AI modules. Familiarity with the principles of artificial intelligence and how it is applied in a business context will also be helpful.

This forward-looking Research Handbook makes an insightful contribution to the emerging field of studies on communication of, by and with AI. Bringing together state-of-the-art research from over 50 leading international scholars across various fields, it provides a comprehensive overview of the complex intersections between AI and communication.

Presents compendium of methodologies and technologies in industrial AI and digitalization Illustrates sensor to actuation approach showing complete cycle, that defines and differences AI and digitalization concept Covers a broad range of academic and industrial issues within the field of asset management Discusses impact of Industry 4.0 in other sectors Includes a dedicated chapter on real-time case studies

Businesses must confront a pressing challenge—how to navigate the turbulent seas of market dynamics and technological advancements. As traditional business models face obsolescence in the wake of relentless innovation, enterprises failing to quickly to harness the power of advanced technologies risk fading into obscurity. This predicament sets the stage for a critical exploration of the profound impact of artificial intelligence (AI) and smart technology on the sustainability of entrepreneurial ventures. Without a strategic embrace of these innovations, businesses find themselves adrift, struggling to cope with the demands of a rapidly evolving market. Utilizing AI and Smart Technology to Improve Sustainability in Entrepreneurship serves as a guide for entrepreneurs seeking to thrive in this era of unprecedented change. Readers begin with a deep dive into the challenges faced by contemporary businesses. This book meticulously dissects these challenges and provides guidance for those ready to chart a course toward sustainable success by leveraging the transformative and compelling aptitudes of AI. The proposed solution of the integration of AI and smart technologies into the business landscape is one of great promise.