Ai Enabled Analytics For Business

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.

This book explores and discusses how businesses transit from big data and business analytics to artificial intelligence (AI), by examining advanced technologies and embracing challenges such as ethical issues, governance, security, privacy, and interoperability of capabilities. This book covers a range of topics including the application of cyber accounting and strategic objectives, financial inclusion, big data analytics in telecommunication sector, digital marketing strategies and sports brand loyalty, robotic processes automation in banks, and the applications of AI for decision-making in human resources, healthcare, banking, and many more. The book provides a comprehensive reference for scholars, students, managers, entrepreneurs, and policymakers by examining frameworks and business practice implications through its discussions which embrace a wide variety of unique topics on business analytics, AI, and how it can be applied together to address the challenges of the digital era.

Lead your organization to become evidence-driven Data. It’s the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries. The availability of unprecedented technology-enabled tools has made AI (Artificial Intelligence) an essential component of business analytics. But what’s often lacking are the leadership skills to integrate these technologies to achieve maximum value. Here, the authors provide a comprehensive game plan for developing that all-important human factor to get at the heart of data science: the ability to apply analytical thinking to real-world problems. Each of these tools and techniques comes to powerful life through a wealth of powerful case studies and real-world success stories. Inside, you’ll find the essential tools to help you: Develop a strong data science intuition quotient Lead and scale AI and analytics throughout your organization Move from “best-guess” decision making to evidence-based decisions Craft strategies and tactics to create real impact Written for anyone in a leadership or management role—from C-level/unit team managers to rising talent—this powerful, hands-on guide meets today’s growing need for real-world tools to lead and succeed with data.

This book explores and discusses how businesses transit from big data and business analytics to artificial intelligence (AI), by examining advanced technologies and embracing challenges such as ethical issues, governance, security, privacy, and interoperability of capabilities. This book covers a range of topics including the application of cyber accounting and strategic objectives, financial inclusion, big data analytics in telecommunication sector, digital marketing strategies and sports brand loyalty, robotic processes automation in banks, and the applications of AI for decision-making in human resources, healthcare, banking, and many more. The book provides a comprehensive reference for scholars, students, managers, entrepreneurs, and policymakers by examining frameworks and business practice implications through its discussions which embrace a wide variety of unique topics on business analytics, AI, and how it can be applied together to address the challenges of the digital era.

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

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.

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.