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Navarro R: Work Reimagined

Work Reimagined

The AI Era

Springer Proceedings in Business and Economics

This open access book presents the latest findings of researchers from around the globe who presented their work at the 12th International Conference of Strategic Innovative Marketing and Tourism (ICSIMAT) in 2025. It provides an up-to-date information and discusses current trends, issues, and debates, both theoretical and practical research, on strategic innovative marketing and tourism and applications from social media and emerging technologies in Artificial Intelligence and the Internet of Everything. Topics covered in the chapters include social media in marketing and tourism hospitality, culture, strategic tools, and techniques employed and implemented by some of the top research laboratories in the world to the industry. This book brings together work from both academia and industry and continues the successful impact of the previous years' conference on the academic discussion of the topics.
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Navigating Global Trends and Managing Challenges —12th ICSIMAT, Olympus Riviera, Greece, 2025

Strategic Innovative Marketing and Tourism

Platform Supply Chains in the AI Era

Risk Management

Platform Supply Chains 5

Most decisions at work are not made in systems, meetings, or strategy decks.

They are made in messages people hesitate to send, in words they soften, and in truths they quietly work around.
That environment already exists. Most organizations operate inside it every day.

When work breaks down, it is rarely because technology failed or process was missing. It breaks because people adapt to incentives, fear, ambiguity, and unspoken rules. Those adaptations leave evidence. They accumulate in emails, chats, documents, and handoffs. Over time, everyday communication becomes the place where judgment actually occurs, and where intent can be observed rather than declared.

AI enters this environment without understanding context, politics, or consequences. It processes what it is given. But because it operates at scale, speed, and proximity to daily work, it makes patterns visible that were previously diffuse, ignored, or easy to rationalize away. Not because it is insightful, but because it is literal.
What becomes visible is often uncomfortable.

This book examines why organizations routinely misunderstand how work is really done, why transformation efforts fail even when the strategy is sound, and why AI surfaces coordination and accountability problems leaders did not realize they were managing implicitly. Not through predictions about the future of work. Through close attention to how decisions, judgment, and responsibility already move through everyday communication.

You will start noticing:Where decisions quietly shift outside formal processes
How responsibility blurs without anyone explicitly choosing it
Why tools amplify existing behavior rather than correcting it



This is not a how to guide, a productivity system, or a manifesto.

It is written for people accountable for outcomes, who can sense that something is missing in how AI now fits into their daily work, and who want a clearer way to understand that relationship before it quietly reshapes how decisions and responsibility actually work.

If AI is already embedded in daily work, this book offers a way to understand what it makes visible, so you leave with clearer judgment about how decisions, responsibility, and trust are actually forming.

An Inbox Between Us

Palgrave Studies in Accounting and Finance Practice

This book is a comprehensive guide designed for professionals in high-risk sectors, bridging the gap between traditional auditing methods and the modern digital landscape. This book offers innovative solutions, practical Python coding exercises, and real-world scenarios, equipping readers with invaluable insights into fraud analytics. Tailored for internal auditors, data scientists, and analysts, it's an essential resource for navigating the complexities of fraud in the digital era.

Data Science and Machine Learning Techniques for Detecting Fraud in the Digital Age

Fraud Analytics in Action

A complete guide to deep neural networks – the technology behind AI – covering fundamental and advanced techniques to apply machine learning in real-world scenarios. Deep Learning Crash Course goes beyond the basics of machine learning to delve into modern techniques and applications of great interest right now, and whose popularity will only grow in the future. The book covers topics such as generative models (the technology behind deep fakes), self-supervised learning, attention mechanisms (the tech behind ChatGPT), graph neural networks (the tech behind AlphaFold), and deep reinforcement learning (the tech behind AlphaGo). This book bridges the gap between theory and practice, helping readers gain the confidence to apply deep learning in their work.

Deep Learning Crash Course

India Studies in Business and Economics

Data-Driven Benchmarking of School Education Systems in India

Mathematical Methods in the Digital Age

Data science, quantitative analysis, computer optimisation, and machine learning are all explored in this book. Insightful mathematical analysis of ML models is presented in this work. Various fields, including healthcare, banking, and e-commerce, are covered in this book through case studies. To choose features, hyperparameters, and models, the latter shows how optimisation can be implemented. Practical insights on the assessment and improvement of machine learning models in real-world situations, such as e-commerce, healthcare, banking, and agriculture, are included. Professionals in the fields of research, engineering, mathematics, and computer science are the target audience for this book. Through the integration of theoretical principles with&nbsp;practical applications, the usage of computational optimisation techniques in real-time settings is to be enhanced.&nbsp;

Machine Learning and Fuzzy Systems

Computational Optimization

We're living through a transformation as profound as electricity or the printing press, yet AI often feels more like hype than help. With clear language, vivid illustrations, and humor, veteran AI researcher Sairam Sundaresan unpacks machine learning, natural language, computer vision, and recommender systems, showing how they drive the tech we use daily. You'll learn how AI works, where it appears, and how to make smarter choices about its role in your life and work. In two parts, the book moves from core concepts to real applications while debunking myths and stripping away buzzwords.

An Illustrated Introduction

AI for the Rest of Us

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

A rigorous, cutting-edge overview of the range of methods used to conduct causal inference in the social sciences. This textbook provides a lucid, rigorous, and cutting-edge overview of the methods used to conduct causal inference in the social sciences, covering all the core techniques and latest advances. Offering a detailed survey of the current state of microeconometric theory, Damian Clarke delves deeply into machine learning applications and presents developments in difference-in-difference methods, instrumental variables, multiple hypothesis testing, and other advanced topics. With a diverse range of examples and exercises offering hands-on experience, Applied Microeconometrics equips graduate students and researchers to apply state-of-the art scholarship to actionable problems. <ul><li>Integrates a rich array of machine learning methods into causal modeling frameworks</li><li>Covers recent advances in difference-in-differences and dynamic research designs, formal discussions of challenges related to inference and hypothesis testing, and heterogeneity analysis</li><li>Features a breadth of real-world examples from recent papers</li><li>Includes coding implementation in Python, R, and Stata</li></ul>

Applied Microeconometrics

Edge AI is transforming everyday devices by putting intelligence where it matters most: directly inside the hardware. With on-device inference, a camera can recognize a visitor instantly, a phone can translate speech without streaming audio to the cloud, and a wearable can detect anomalies in real time-fast, private, and reliable even when the network disappears.This book is your practical guide to building exactly those kinds of systems with the Raspberry&nbsp;Pi AI&nbsp;HAT+ and the Hailo-8L accelerator. You'll start with clear foundations: core&nbsp;AI and machine-learning concepts, how neural networks work, and what truly distinguishes Edge&nbsp;AI from cloud AI-plus an honest look at ethical considerations and future impacts.Then it's straight to hands-on physical computing. Step by step, you'll set up Raspberry&nbsp;Pi&nbsp;OS, power and cooling, and develop in Python using the Thonny&nbsp;IDE. You'll learn GPIO basics with lights and servos, mount the AI&nbsp;HAT+ hardware, install and verify the Hailo software stack, and connect the right camera-official modules or USB webcams, even multiple cameras.From your first pipeline to real projects, you'll run person detection, pose estimation, segmentation, and depth estimation, then level up with YOLO object detection: smart alerts, guest counters, and custom extensions. You'll even connect vision to motion by combining gesture recognition with servo-driven mechanisms, including a robotic arm.With troubleshooting tips, hardware essentials, and a practical Python refresher, this book turns Edge&nbsp;AI from buzzword into buildable reality.

AI Projects for the Raspberry Pi with the AI HAT+

Edge AI Made Practical

Artificial intelligence is rapidly transforming the insurance sector. This book explores how industry practitioners are already implementing AI and how its role will continue to develop. Covering foundational concepts, real-world use cases and strategic and regulatory considerations, it provides a comprehensive, practice-oriented perspective: How can an AI unit be established? What role does AI play in underwriting, brokerage, and back-office operations? And how is it transforming the customer experience?
Leading experts from across the entire spectrum of the industry share their insights. 

The book examines the gap between AI's theoretical potential and its practical implementation, presenting a structured overview of current use cases, opportunities, and risks. It offers a clear understanding of how AI is shaping the insurance industry today - and how it is set to transform it in the years to come.

Insider Perspectives on Opportunities, Risks, and Practical Applications of AI

Artificial Intelligence in the Insurance Industry

Work Reimagined

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