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Strategic Insights and Case Studies on
Navigating an Entrepreneurial Landscape

Strategic Insights and Case Studies on Navigating an Entrepreneurial Landscape

Transforming Organizations to Thrive in the Age of Climate Change

Agile Sustainability

The Essential Guide to Launching Your Career in Artificial Intelligence

Break into AI

Governing the Intelligent Enterprise

Overruled

Taylor and Francis Proceedings in Computer Science and Engineering

Reimagining Tomorrow: AI and Sustainable Imperative presents a curated collection of high-quality research contributions that explore the transformative role of Artificial Intelligence in addressing pressing sustainability challenges across a variety of sectors.This book highlights how advanced AI techniques-ranging from machine learning and deep learning to intelligent optimization-are being leveraged to enhance efficiency, reduce environmental impact, and support data-driven decision-making. The volume emphasises the growing importance of integrating technological innovation with sustainability goals to create resilient and future-ready systems. The primary focus of this book is on human-centred Artificial Intelligence, with particular attention given to its ethical, societal, and governance dimensions. It examines critical issues such as transparency, fairness, accountability, and privacy, while also showcasing AI-driven solutions for sustainable development in key domains, including healthcare, education, smart infrastructure, and Industry 5.0. The contributions further address interdisciplinary approaches, combining AI with emerging paradigms such as edge computing, IoT, and green technologies to enable scalable and responsible innovation.The book provides valuable insights for researchers, academicians, industry practitioners, policymakers, and students by offering a comprehensive perspective on both theoretical advancements and practical implementations of AI for sustainability and serves as a reference for understanding current trends, identifying research gaps, and exploring future directions in the design of intelligent, ethical, and sustainable systems.

AI and Sustainable Imperative

Reimagining Tomorrow

AI and quantum computing are two disruptive forces about to push financial services and the banking industry over the edge, demanding an unprecedented transformation. They are not fleeting trends but fundamental disruptors that will reshape the core of banking and finance. Authored by a senior Swiss banking technology executive, this practical guide helps board members and senior leaders turn disruption into a disciplined strategic agenda. It offers a rare insider perspective and a roadmap for navigating these seismic shifts. Bridging the gap between technological innovation and practical application, this book equips leaders with the tools to address the challenges and opportunities posed by AI and quantum computing. It features a thorough review of the latest business opportunities and threats associated with these technologies, alongside foresight-based strategic decision frameworks to guide prioritisation. Real-world case studies and an online companion with 125 AI use cases tailored for banks provide actionable insights. It also introduces an original AI Adoption Maturity Model and a Hybrid Enterprise Reference Architecture to ensure safe integration of internal systems with external frontier models. Finally, it provides two detailed multi-year roadmaps, offering a clear path to harness AI's potential and to prepare for a post-quantum future through crypto agility.Rather than offering abstract predictions or generic solutions, this book presents financial-services leaders with the frameworks, choices, and implementation path they need to act with confidence at a moment when waiting is no longer an option.

Evolutions at the Bleeding Edge

How AI and Quantum Computing Will Make or Break the Bank

A practical, clever, down-to-earth guide to making decisions big or small, fast—from writer, journalist, and nationally acclaimed decision coach Nell McShane Wulfhart. We are drowning in choices—from major life decisions like whether to move, marry, or change careers, to relentless daily dilemmas about what to wear, eat, say, or do. In a world that glorifies optimization and “smart” choices, we’ve wound up paralyzed, deferring, deliberating, and overthinking to the point of exhaustion.&nbsp; Decision-making has become a source of stress instead of empowerment. Nell McShane Wulfhart is here to change that. In Decide in an Hour, she tackles the epidemic of indecision head-on and offers readers a lifeline: the tools to make better choices quickly, without feeling guilty, spiraling, or second-guessing. In this book, Wulfhart teaches us how to: <ul><li>identify the decision we’re actually making (a shockingly common obstacle)</li><li>eliminate noise and make the call—fast</li><li>follow through with confidence, even when others disagree</li><li>make the most common major life choices (career, relationships, kids, where to live) through the use of a decision-making field guide</li></ul> Equal parts wise, warm, and no-nonsense, Decide in an Hour offers clarity and momentum for how to start making smart, confident decisions today.

The Handbook for Making Better Decisions Faster

Decide in an Hour

Leveraging Data Science and Machine Learning

Predictive Intelligence for Transforming Healthcare Insurance

A step-by-step guide to AI-native analytics systems for BI practitioners Building Business Intelligence Apps with AI provides a step-by-step guide for data analysts, data engineers, and BI developers who need to integrate AI into their analytics workflows. Written by Ryan Dolley, a data industry veteran with fifteen years of experience across BI development, consulting, and data vendor roles, this book shows how to partner with AI to deliver better data models, dashboards, and business queries while building AI-native experiences for end users. The book addresses data foundations, outsourcing repetitive tasks to AI, and scaling autonomous analytics systems that maintain accuracy and data governance. Readers learn practical steps for increasing individual and team effectiveness using AI while avoiding common pitfalls such as hallucination, data inaccuracy, and user abandonment. It delivers operating models that data departments need, covering everything from data blending and dashboard creation to personalization and responsiveness. Readers will also find: <ul> <li>Practical techniques for partnering with LLMs to accelerate data modeling, querying, and dashboard development across BI workflows</li> <li>Strategies to outsource repetitive analytics tasks to AI while maintaining rigorous data governance and accuracy standards</li> <li>Operating models for individuals and data departments to scale AI-native analytics systems that end users adopt</li> <li>Methods for building autonomous, personalized analytics experiences that address user expectations for speed and responsiveness</li> <li>Guidance on avoiding AI pitfalls including hallucination, data inaccuracy, and user abandonment in production analytics environments</li> </ul> Data analysts, data engineers, BI developers, and their managers will find in this book the specific skills and operating models needed to integrate AI into daily analytics work. It equips practitioners to build accurate, autonomous analytics systems while strengthening data governance and accelerating delivery across their teams.

Building Business Intelligence Apps with AI

The book explores how emerging digital technologies can address the interconnected challenges of food security, climate change, and sustainability. It delves into AI-driven predictive models for crop yield estimation, IoT-enabled soil and water monitoring, and renewable energy-powered smart irrigation systems. The book highlights practical applications of big data, precision farming, and cloud platforms to enhance productivity, reduce ecological footprints, and improve resilience to climate change. With global case studies and interdisciplinary insights, it serves as a resource for researchers, students, farmers, agribusiness leaders, and policymakers, offering actionable strategies for transforming agriculture into a climate-resilient and resource-efficient sector.

AI, Technology, and Sustainability

Smart Solutions for a Changing Climate

Wiley Finance

The handbook to unleash the power of AI in Finance with guidance to pragmatically managing AI Risk Are you ready to harness the transformative potential of artificial intelligence and machine learning in finance while effectively managing the associated risks? "AI and Risk Management in Finance: A Practitioner's Handbook" by Sri Krishnamurthy, CFA, is your indispensable guide. Tailored for financial professionals, this comprehensive handbook delivers practical insights, case studies, and frameworks to navigate the complex landscape of AI and ML in the financial sector. This pragmatic handbook, developed primarily for professionals involved in designing, developing, and deploying AI models in the financial industry explores cutting-edge technologies, regulatory landscapes, and pragmatic frameworks. From generative AI to robust risk management strategies, this handbook equips you with the tools you need to validate models, ensure compliance, and operationalize AI risk management effectively. Leveraging lessons from QuantUniversity's AI and Machine Learning Risk Management program offered in 18 countries, this book focuses on enabling practitioners apply the state-of-the-art AI and machine learning techniques factoring unique risks specific to the financial services domain. Whether you're a seasoned professional or a newcomer, this book offers invaluable insights for harnessing the power of AI responsibly in the financial industry. Highlights of the Book: <ul> <li>Machine Learning and AI in Financial Services: Dive into the fundamentals of ML and AI, exploring various applications within the financial industry through real-world case studies.</li> <li>Generative AI and LLMs in Finance: Understand the emerging landscape, key players, and advanced concepts like prompt engineering and fine-tuning with practical examples.</li> <li>Risk Management for AI/ML Applications: Learn about current and emerging regulatory landscapes, AI risk management frameworks, and legislative efforts shaping the field.</li> <li>Data Governance Issues: Address critical aspects of data quality, privacy, security, and governance, with actionable frameworks and illustrative case studies.</li> <li>Model and System Governance: Gain insights into validating machine learning models, quantifying model risk, and ensuring robust system governance with MLOps and model monitoring.</li> <li>Key Risk Assessment Facets: Focus on documentation, explainability, fairness, security, and reliability with detailed case studies.</li> <li>Verification and Validation: Distinguish between verification and validation and learn frameworks for effective AI application validation.</li> <li>Algorithmic Auditing: Conduct comprehensive algorithmic audits with detailed processes and real-world examples.</li> <li>Operationalizing AI Risk Management: Discover ten essential strategies for implementing AI risk management practices effectively in your organization.</li> </ul> Prepare for the future of finance with expert advice and actionable strategies. Don't miss out on this comprehensive guide-your roadmap to navigating AI and risk management in the financial sector.

A Practitioner's Guide to Machine Learning, Generative AI, Agentic Systems, and Responsible Deployment

Strategic Insights and Case Studies on Navigating an Entrepreneurial Landscape

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