Business Intelligence And Data Analysis In The Age Of Ai
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Business Intelligence and Data Analysis in the Age of AI
Author: Arshad Khan
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2025-03-01
Unlock data-driven decision-making with Business Intelligence and Data Analysis in the Age of AI. This guide combines traditional BI with AI’s transformative power to help professionals and newcomers excel in the data era. Whether you're a seasoned professional or new to BI, this book provides actionable strategies to navigate the complexities of modern data analysis. Embark on this illuminating journey to master the tools, strategies, and ethical considerations that define modern business intelligence and AI. FEATURES • BI Fundamentals: Master analytics processes and tools • Ethical and Regulatory Challenges: Navigate governance, security, privacy, and ethical frameworks • BI Tools: Learn the power of tools like R, SQL, Python, and data manipulation techniques • Visualize and Predict: Learn data visualization and predictive analytics to forecast trends and drive innovation • Embrace the Future: Discover how AI transforms BI, unlocking new opportunities and navigating emerging risks.
Competing in the Age of AI
Author: Marco Iansiti
language: en
Publisher: Harvard Business Press
Release Date: 2020-01-07
"a provocative new book" — The New York Times AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Now with a new preface that explores how the coronavirus crisis compelled organizations such as Massachusetts General Hospital, Verizon, and IKEA to transform themselves with remarkable speed, Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how "collisions" between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating models Explain the opportunities and risks created by digital firms Describe the new challenges and responsibilities for the leaders of both digital and traditional firms Packed with examples—including many from the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI.
The Economics of Artificial Intelligence
Author: Ajay Agrawal
language: en
Publisher: University of Chicago Press
Release Date: 2024-03-14
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.