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Navarro R: Work Reimagined

Work Reimagined

The AI Era

Equips legal professionals and stakeholders with a clear, authoritative, and practical guide to the UK legal treatment of digital payments and tokenised assets. The Law of Digital Payments is a guide to UK payments law, examining how English law and financial regulation are adapting to new forms of money and new ways of moving it. Value now passes between banks, payment service providers and distributed ledgers, and AI agents are beginning to initiate payments. For lawyers and compliance professionals, this raises fundamental questions about when a debt is discharged, who bears the loss when a payment goes wrong, and which activities fall within the regulatory perimeter. This title sets out the legal concepts of payment and money before mapping the regulatory perimeter under the Financial Services and Markets Act 2000, the Regulated Activities Order 2001, the Payment Services Regulations 2017 and the Electronic Money Regulations 2011. Later chapters provide clarity on funds transfers and settlement, card payments, open banking and payment initiation, digital wallets, strong customer authentication, unauthorised transactions and the recovery of misdirected payments, including authorised push payment (APP) fraud. Coverage of tokenised payment includes the classification of cryptoassets and their status as property. The new UK regimes for stablecoins and tokenised deposits are considered alongside the proposed digital pound. The final chapters address the protection of customer funds when a payment or e-money institution becomes insolvent. They also examine the legal questions raised by agentic payments, in which an AI agent makes payments on a user's behalf. Drawing upon legislation, FCA rules and guidance, case law and current reform proposals, the book is designed to help practitioners, in-house counsel, regulators, payment firms and academics understand and apply the law of digital payments. This title is included in Bloomsbury Professional's Banking and Finance Law online service.

The Law of Digital Payments

Tech, War and the Fight for Humanity

Thundercloud

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

The primary objective of this work is to develop a behavioral basis for an AI algorithm to assist managers of finance, production, and marketing divisions of firms in imperfect markets. Production, sales revenue, external finance, and networth (market value of the fixed assets) have the pivotal role as objectives. However, all of them cannot be maximized simultaneously due to the interdependence among them. The managers tend to pursue the most valuable objective using the most productive strategy at each point of time. This was utilized as a basis for the AI algorithm. Non-linearities in the switches in objectives and strategies are a short run phenomenon while swaps will be preferred if firms have a long run advantage. Such observed phenomena were also incorporated in the specification of the AI algorithms. The important results are as follows. (i) In most industries the shortage of demand is not a constraint. Instead, the ability of firms to increase their strategic supply is dominant. (ii) Most firms pursue strategic supply based on the capital stock available. This indicates a preference for external finance. (iii) In the short run firms utilize working capital finance and selling costs to ensure that the sales revenue achieved is commensurate with the strategic supply. (iv) Thus, the postulate that expected demand determines the strategic supply as well as the profit maximization postulate for the short run do not appear as the priorities.

The primary objective of this work is to develop a behavioral basis for an AI algorithm to assist managers of finance, production, and marketing divisions of firms in imperfect markets. Production, sales revenue, external finance, and networth (market value of the fixed assets) have the pivotal role as objectives. However, all of them cannot be maximized simultaneously due to the interdependence among them. The managers tend to pursue the most valuable objective using the most productive strategy at each point of time. This was utilized as a basis for the AI algorithm. Non-linearities in the switches in objectives and strategies are a short run phenomenon while swaps will be preferred if firms have a long run advantage. Such observed phenomena were also incorporated in the specification of the AI algorithms.&nbsp;The important results are as follows. (i) In most industries the shortage of demand is not a constraint. Instead, the ability of firms to increase their strategic supply is dominant. (ii) Most firms pursue strategic supply based on the capital stock available. This indicates a preference for external finance. (iii) In the short run firms utilize working capital finance and selling costs to ensure that the sales revenue achieved is commensurate with the strategic supply. (iv) Thus, the postulate that expected demand determines the strategic supply as well as the profit maximization postulate for the short run do not appear as the priorities.

Towards an AI Algorithm for Multiple Objectives in Imperfect Markets

Every organization has two versions of itself. There's the documented one - the processes, the org charts, the goals and values that make up the official story. Then there's the way things actually work. The workarounds people build because the real process doesn't fit. The spreadsheets no one asked for but everyone depends on. The conversations where someone finally says what they actually need.

That second version has always been there. It's how businesses actually run. And AI is about to walk right into the middle of it.

An Inbox Between Us is not a framework, a productivity system, or a technology roadmap. David Dean draws on nearly 20 years working inside complex organizations - close to the real decisions, workarounds, and human behavior that determine how work actually gets done - to examine what AI makes visible when it enters that environment. AI doesn't understand context, politics, or consequence. It processes what it's given. But because it operates at scale and proximity to daily work, it surfaces patterns that were previously scattered, easy to rationalize, or buried across everyday business activities.

What becomes visible is often uncomfortable. But it's also an opportunity to self-realize - as a person and as an organization.

Inside this book, you'll find:Why organizations routinely misunderstand how their own work gets done - and what AI reveals about the gap
How people creatively adapt around broken processes, and why those adaptations matter more than most leaders realize
Why AI solutions fail when they're built on the official story instead of the real one
What it means to build trust with AI the same way you'd build trust with a new team member - gradually, with guardrails you control
Why the people doing the undocumented work every day are the most important part of this entire equation



This book is for business leaders and for every working person who's been handed AI and told to figure it out, and wants a clearer, more human way to understand what it actually means for how they work.

An Inbox Between Us

A Comprehensive Guide to Managing Municipal Infrastructure Assets

This Palgrave Pivot develops an interdisciplinary approach to household financial decision-making by integrating behavioral finance, consumer finance, financial planning, and human-centered artificial intelligence. Moving beyond investor- and market-focused traditions, it situates financial risk tolerance, financial stress, and dynamic behavioral signals within the lived contexts of households, advisors, and institutions. The book reconceptualizes risk tolerance as a time-varying behavioral construct, positions financial stress as a core mechanism shaping decision-making and well-being, and introduces a framework of behavioral signals, including consumer complaints, sentiment, and market mood, that may precede price movements.It further examines how machine learning and AI can interpret these signals while reshaping financial advice, alongside their methodological limits and ethical implications for accountability and client autonomy. By linking psychological states, behavioral data, and advisory practice, the book offers a structured research agenda for behavioral personal finance and contributes to debates on financial well-being, vulnerability, and technology-mediated decision-making. It will be of interest to scholars and advanced students in behavioral finance, financial planning, consumer science, and decision psychology, as well as practitioners and policymakers concerned with AI and household financial behavior.Wookjae Heo
 is Associate Professor of Consumer Science at Purdue University. His research focuses on financial risk tolerance, financial stress, consumer financial behavior, and the application of machine learning and natural language processing in personal finance. He has published widely in leading journals in consumer finance and financial planning and is known for advancing behavioral personal finance as an interdisciplinary field linking household decision-making, well-being, and human-centered AI.

This Palgrave Pivot develops an interdisciplinary approach to household financial decision-making by integrating behavioral finance, consumer finance, financial planning, and human-centered artificial intelligence. Moving beyond investor- and market-focused traditions, it situates financial risk tolerance, financial stress, and dynamic behavioral signals within the lived contexts of households, advisors, and institutions. The book reconceptualizes risk tolerance as a time-varying behavioral construct, positions financial stress as a core mechanism shaping decision-making and well-being, and introduces a framework of behavioral signals, including consumer complaints, sentiment, and market mood, that may precede price movements.It further examines how machine learning and AI can interpret these signals while reshaping financial advice, alongside their methodological limits and ethical implications for accountability and client autonomy. By linking psychological states, behavioral data, and advisory practice, the book offers a structured research agenda for behavioral personal finance and contributes to debates on financial well-being, vulnerability, and technology-mediated decision-making. It will be of interest to scholars and advanced students in behavioral finance, financial planning, consumer science, and decision psychology, as well as practitioners and policymakers concerned with AI and household financial behavior.

Risk Tolerance, Financial Stress, and Human-Centered AI

Behavioral Personal Finance

Innovations in Intelligent Internet of Everything IoE

Biomedical Applications of AI and IoMT in Smart Healthcare

How Enterprises Build, Govern, and Operationalize AI

Trust at Scale

This book explores how AI is currently transforming work processes, decision-making, and organizational dynamics with a collection of unique, detailed, and meticulously analysed case studies of AI adoption. Each chapter covers a different core business function and is structured around an original research study conducted by the authors at one of several major international firms, including Generali, Ericsson, Volvo and more. The casebook provides direct exposure to how leading global companies are deploying AI in practice, offering a concrete, current, and exclusive picture that purely theoretical textbooks cannot provide. The discussion questions at the end of each chapter support active learning, critical thinking, and classroom debate. Intended for whole-book adoption or selective, modular use of the cases, and incorporating instructor materials, this essential teaching resource will be suitable for courses on digital transformation, supply chain management, operations management, AI in business, and management information systems.

Theory and Lessons from Leading Companies

AI-Powered Functions

In today’s AI-driven business environment, the ability to translate data into sound decisions is increasingly important. This textbook introduces the foundations and practical applications of business analytics, machine learning, and artificial intelligence. Readers learn how to explore and visualize business data, build and evaluate predictive models, and apply analytical methods to support business decisions.The book progresses from core concepts such as data visualization, optimization, model evaluation, supervised learning, and unsupervised learning to advanced topics in deep learning, computer vision, generative AI, text mining, natural language processing, and large language models. It also addresses the limitations and ethical challenges of AI. Step-by-step explanations, business examples, and Python exercises help readers connect analytical concepts with real-world applications.Designed for students and professionals with little or no prior experience in programming or machine learning, the book develops skills progressively from basic concepts to advanced methods. It prepares readers to select, implement, interpret, and evaluate AI-based analytical approaches for a wide range of business problems.Hyunhwan “Aiden” Lee
 is Assistant Professor of Marketing at California State University, Long Beach, USA. His research focuses on marketing analytics, brand and content analytics, and the application of artificial intelligence to business and marketing problems. His work uses machine learning, natural language processing, computer vision, location-based data analysis, large-scale data analysis, and mathematical modeling.
 Reo Song
 &nbsp;is Professor of Marketing and Director of the M.S. in Marketing Analytics program at California State University, Long Beach. His research focuses on machine learning, AI, business analytics, and digital marketing, with publications in leading journals including&nbsp;
 Journal of Marketing
 &nbsp;and&nbsp;
 Production and Operations Management
 . He has advised organizations on AI and machine learning applications, delivers frequent invited talks on AI and business analytics, and contributes regularly to newspaper columns on AI and digital transformation.

In today’s AI-driven business environment, the ability to translate data into sound decisions is increasingly important. This textbook introduces the foundations and practical applications of business analytics, machine learning, and artificial intelligence. Readers learn how to explore and visualize business data, build and evaluate predictive models, and apply analytical methods to support business decisions.The book progresses from core concepts such as data visualization, optimization, model evaluation, supervised learning, and unsupervised learning to advanced topics in deep learning, computer vision, generative AI, text mining, natural language processing, and large language models. It also addresses the limitations and ethical challenges of AI. Step-by-step explanations, business examples, and Python exercises help readers connect analytical concepts with real-world applications.Designed for students and professionals with little or no prior experience in programming or machine learning, the book develops skills progressively from basic concepts to advanced methods. It prepares readers to select, implement, interpret, and evaluate AI-based analytical approaches for a wide range of business problems.

An Advanced Guide to Data-Driven Decision Making

Business Analytics and Artificial Intelligence

WiWi klipp & klar

In Zeiten digitaler Transformation liefert dieses Buch eine schnelle und zugleich fundierte Einführung in alle wichtigen Bereiche der Wirtschaftsinformatik. Aufbauend auf der Struktur der ersten Auflage wurden die Inhalte aktualisiert, erweitert und konsequent an aktuelle Entwicklungen ausgerichtet. Neben bewährten Grundlagen wurden insbesondere Themen wie generative Künstliche Intelligenz, KI-Agenten, NoSQL-Datenbanken, Cloud Resource Management und FinOps sowie aktuelle IT-Governance-Ansätze integriert und praxisnah aufbereitet.&nbsp;
Das Buch richtet sich an Studierende und Studieninteressierte aller Fachrichtungen mit Bezug zur Wirtschaftsinformatik ebenso wie an Praktiker*innen, die ihr Wissen gezielt auffrischen und einen Einblick in aktuelle Entwicklungen erlangen möchten. Anschauliche Grafiken, konkrete Beispiele sowie durchgängige Aufgaben mit Lösungen fördern das Verständnis und ermöglichen die direkte Anwendung des Gelernten. Der modulare Aufbau erlaubt einen flexiblen Einstieg in alle Themenbereiche.

In Zeiten digitaler Transformation liefert dieses Buch eine schnelle und zugleich fundierte Einführung in alle wichtigen Bereiche der Wirtschaftsinformatik. Aufbauend auf der Struktur der ersten Auflage wurden die Inhalte aktualisiert, erweitert und konsequent an aktuelle Entwicklungen ausgerichtet. Neben bewährten Grundlagen wurden insbesondere Themen wie generative Künstliche Intelligenz, KI-Agenten, NoSQL-Datenbanken, Cloud Resource Management und FinOps sowie aktuelle IT-Governance-Ansätze integriert und praxisnah aufbereitet.&nbsp;Das Buch richtet sich an Studierende und Studieninteressierte aller Fachrichtungen mit Bezug zur Wirtschaftsinformatik ebenso wie an Praktiker*innen, die ihr Wissen gezielt auffrischen und einen Einblick in aktuelle Entwicklungen erlangen möchten. Anschauliche Grafiken, konkrete Beispiele sowie durchgängige Aufgaben mit Lösungen fördern das Verständnis und ermöglichen die direkte Anwendung des Gelernten. Der modulare Aufbau erlaubt einen flexiblen Einstieg in alle Themenbereiche.

Wirtschaftsinformatik klipp & klar

Work Reimagined

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