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Andrew Pattison

Alan Medcroft

Managing AI Risk

A practical approach to responsibly managing AI with ISO 42001

Chapter One: Understanding AI and organisational risk
Chapter Two: Building a risk-aware AI strategy
Chapter Three: Identifying and assessing risks
Chapter Four: Developing an AI management system
Chapter Five: The future of AI and risk
Conclusion
Further reading

Turn enterprise AI into a source of strategic insight, not just productivity gains.The AI Gap by leading AI expert and keynote speaker Richard Foster-Fletcher shows business and technology leaders how to unlock greater value from enterprise AI by changing the structures, processes and culture surrounding it. Organizations invest in AI expecting new intelligence and transformative insights. Yet AI is often introduced through procurement, security, legal and compliance systems designed for standard software, restricting its potential before it can deliver on that promise. This book explains why this gap persists and what organizations must change to close it. You'll learn how to: - Identify organizational barriers limiting the value of AI - Move beyond productivity-focused AI implementation - Create conditions where AI can generate deeper insights - Enable employees to explore, experiment and challenge established thinking &gt;The AI Gap equips leaders to rethink AI implementation, create space for experimentation and turn artificial intelligence into a powerful driver of organizational insight and long-term business value. Themes include: enterprise AI, AI implementation, AI strategy, business transformation, organizational culture, AI innovation

How Business Decisions Expand What AI Can Do

The AI Gap

You know you have more to offer, but you're tired of waiting for a seat at the table to prove it. Most emerging leaders feel stuck in a holding pattern, believing that their ability to make an impact is capped by their current title or the person above them on the org chart. But influence isn't a reward for a promotion. It's a muscle you build in the mundane, everyday moments of your current role.In 90 Days to Grow Your Influence Clay Scroggins, bestselling author of How to Lead When You're Not in Charge, provides a daily roadmap for the "not-in-charge" leader. Drawing from his foundational work on leveraging influence without authority, this 90-day reader helps you navigate the tension of having great responsibility but very little power.Each day is designed to be a "snackable" but substantial 5-minute read, perfect for your morning coffee or the commute to the office. Through a mix of relatable stories, leadership metaphors, and spiritually grounded reflections, you'll learn how to: <ul><li>Understand Identity: Secure your worth in who you are, not just what you do or what your business card says.</li><li>Lead Yourself: Master the disciplines of self-awareness and emotional intelligence.</li><li>Choose Positivity: Cultivate a resilient, solution-oriented mindset that shifts the energy of every room you enter.</li><li>Think Critically: Stop being a passive observer and start solving problems.</li><li>Reject Passivity: Take ownership of the ground you're standing on today.</li><li>Challenge Well: Influence the leaders above you with relational equity and wisdom.</li></ul>Whether you are a new hire, a middle manager, or an aspiring executive, this book is your daily reminder that your seat on the org chart doesn't define your impact. You have everything you need to start leading right where you are. Real influence starts here.

Daily Habits for Leading from Any Level

90 Days to Grow Your Influence

Design AI-powered learning experiences that drive engagement, performance and measurable business impact. Learning Experience Design by Donald Clark is a strategic guide for senior learning professionals who need to modernize learning in an AI-driven world. As organizations adopt generative technologies, this book shows how to move beyond outdated course-led models and build adaptive, human-centred learning ecosystems.Focusing on the evolving role of the learning designer, this updated edition combines learning science, data insight and AI capability to help you design scalable, personalized and effective learning strategies. Drawing on research and real-world examples, it addresses the critical challenges of engagement, ethics and skills sustainability.You will learn how to: -Design AI-enabled learning experiences that adapt to learner needs - Use data, context and performance signals to personalize learning - Integrate simulations, games and workflow learning effectively - Employ ethical AI use and support sustainable skills development - Lead AI learning projects and embed learning into daily workThis book equips you to lead the transformation of learning, delivering smarter, scalable and more impactful outcomes across your organization.Themes include: learning experience design, AI in learning, adaptive learning, workplace learning, learning strategy, performance support

How to Build Effective Learning in the Age of AI

Learning Experience Design

Why the career ladder you were climbing has come apart, and what to do about it.Your job title may still be intact. The meetings continue; the monthly presentation still needs producing. But AI is already taking on the reporting, coordination, research and analysis work that once justified whole teams. The change seldom arrives with a dramatic announcement. It's more insidious than that: the hire is paused, the contractor is not renewed, the promotion evaporates.Written by someone building the tools behind this shift, The Next Rung looks at the knowledge-work middle: project leads, analysts, account managers, operations teams and managers whose jobs depend on orchestration. It traces the pressure from boardroom economics to kitchen-table arithmetic, and asks what happens when professional incomes and mortgages are exposed at the same time.Inside, you'll find: <ul> <li> The roles AI is quietly hollowing out </li> <li> Why the 'junior grind' is disappearing </li> <li> The 80/20 Collapse in professional work </li> <li> A practical audit of your exposure and runway </li> <li> The human capabilities that still matter </li> <li> Routes towards trades, portfolio work and community enterprise </li> <li> What AI's gains mean for housing, welfare and inequality. </li> </ul>This is a clear-eyed notebook from inside the transition, focused on judgement, resilience and building work with more human value. It will let you know where you stand, strengthen your options, and help you make your next move before the ladder disappears beneath you.

Why AI is breaking the career ladder, and how to find your next step

The Next Rung

Swipe, Scan, Shop

Interactive Visual Merchandising

Routledge Studies in Accounting

Implications for Practice, Education, and Research

Generative AI and Agentic Systems in Accounting

Essentials

essentials plus online course

Wissen wird zum Erfolgsfaktor, wenn es im richtigen Moment verfügbar, verständlich und nutzbar ist. Dieses Buch mit Online-Kurs zeigt praxisnah, wie Organisationen ihr Wissen mit künstlicher Intelligenz (KI) systematisch organisieren, sichern und für Menschen wie KI-Anwendungen erschließen können.
Im Zentrum stehen generative KI, RAG-Systeme, KI-Agenten und die Worksphere Map als Werkzeug, um Arbeitsgebiete, Wissensflüsse, Rollen, Prozesse und Quellen sichtbar zu machen. Daraus entstehen strukturierte Wissenselemente für bessere Zusammenarbeit, schnellere Einarbeitung, Wissenstransfer und verantwortungsvolle KI-Nutzung. Den Zugang zum Online-Kurs finden Sie direkt im Buch.
Der Inhalt
<ul>
<li>Wissensorganisation als Grundlage für Lernen, Handlungsfähigkeit und Wertschöpfung</li>
<li>KI als kognitives Exoskelett der Wissensarbeit</li>
<li>RAG, KI-Agenten und KI-gestützte Wissenssysteme</li>
<li>Worksphere Map, Wissensflüsse und Wissenselemente</li>
<li>Governance, Datenschutz, Qualitätssicherung und Reifegradmodell</li>
<li>Praxisbeispiele, Wissenstransfer und Einführung KI-fähiger Wissensplattformen</li>
</ul>
Die Autoren
Benno Ackermann ist Geschäftsführer der Wissenstransfer GmbH, Berater und Dozent für Wissensmanagement, Wissenstransfer und Organisationsentwicklung.
Prof. Dr. Oliver Krancher forscht und lehrt zu Wissensmanagement, Digitalisierung, KI-Governance und Mensch-KI-Zusammenarbeit.
Prof. Dr. Klaus North ist ein profilierter Experte für Wissensmanagement, wissensorientierte Unternehmensführung und organisationales Lernen.
Gemeinsam verbinden die Autoren wissenschaftliche Erkenntnisse mit Praxiserfahrung und zeigen Wege zu einer wirksamen, KI-gestützten Wissensorganisation.
&nbsp;

Strategien, Methoden und Werkzeuge für die wissensbasierte Organisation von morgen

Erfolgreiche Wissensorganisation mit KI und Worksphere Map

Artificial Intelligence and the Transformation of Organizations

Algorithmic Management

Hätten Sie gedacht, dass 'Wie hilft man Bienen?' und 'Wie oft kann ich Blut spenden?' zu den häufigsten Google-Anfragen gehören? Milliarden Fragen werden jeden Tag von Menschen auf der ganzen Welt in Google-Suchfenster getippt oder ins Handy gesprochen. Ob es um Informationen zu einem tollen Kochrezept geht, den nächsten Auftritt des Lieblingspopstars, die Größe des Universums, hilfreiche Tipps bei Schnupfen oder das beste Mittel gegen eine Ameisenstraße in der Küche - diese Anfragen geben ein höchst aktuelles Bild von Nöten, Vorlieben, Interessen und Befindlichkeiten eines großen Teils der Weltbevölkerung zu einer bestimmten Zeit. Und es ist ein ehrliches, denn wir bewegen uns bei den Anfragen nicht in der Öffentlichkeit z. B. der Social Media. Anhand dieser Riesenmenge an Daten lassen sich zeitnah Trends ablesen und phantastische Geschichten erzählen, vor allem auch solche, die Mut machen, weil sie zeigen, dass Tag für Tag Menschen sich tatsächlich kümmern und Anteil nehmen. Simon Rogers nimmt uns mit auf eine ebenso unterhaltsame wie augenöffnende Reise durch die Welt der Daten, die wir selbst täglich mitgestalten. Mit zahlreichen Infografiken. Ausstattung: Mit zahlreichen Illustrationen

Ein überraschend hoffnungsvolles Bild der Menschheit - Mit zahlreichen Illustrationen von Nigel Holmes

Was wir Google fragen

Kooperationswerke Beck - Hart

Article-by-Article Commentary

Artificial Intelligence Act

When an AI initiative dies, the machine takes the blame. It's almost always the wrong suspect. Every executive who has quietly shelved an AI project has the same explanation ready: the model underperformed, the vendor oversold, the technology "wasn't there yet." In Prevail: What Leaders Must Do Now That Machines Can't Manage, Dr. Tim Bottke argues that this story is a convenient way to avoid the real culprit - a chain of management decisions made long before anyone trained a model. The bug isn't in the technology. It's in us. Bottke turns two decades of decision data into a working diagnostic. PREVAIL starts at the ground floor, where leaders reach for the tool before they've named the problem, and moves up through data, experimentation, ownership, measurement, funding, and governance. The uncomfortable finding is that the damage concentrates on the ground floor and compounds as management builds on those decisions.&nbsp; Readers will discover: <ul> <li>Why AI initiatives fail and how to course correct before the budget is committed</li> <li>Chapter checklists that allow for a working audit</li> <li>Action lists for the operator fixing a rollout, the executive shaping a portfolio, and the board member or investor stress-testing a proposal</li> <li>The one question that predicts success better than anything: what problem does this solve?</li> <li>Composite case studies based on public filings that show each failure pattern in the wild, and the fix that would have caught it</li> <li>A shared vocabulary for boards, investors, and operators to pressure-test any AI proposal</li> <li>What the market actually does when companies admit an AI failure&nbsp;</li> </ul> &nbsp; Prevail is written for those who need something firmer than hype to judge what's in front of them.&nbsp;

What Leaders Must Do Now Because Machines Can't Manage

Prevail

International Series in Operations Research & Management Science

This book presents a rigorous, practitioner-ready framework for determining when interventional causal claims can be validly drawn from observational data. By integrating potential outcomes (PO), structural causal models (SCMs), directed acyclic graphs (DAGs), quasi-experimental and longitudinal designs, information-theoretic approaches, and modern machine-learning methods with decision-analytic principles, it identifies necessary conditions for each step of the causal inference process, from defining estimands and identifying causal effects to study design, measurement, estimation, interpretation, robustness, and external validity. The framework is operationalized through practical checklists, tables, diagnostic and falsification tests, and AI-assisted tools, making it accessible across fields such as epidemiology, environmental and occupational health, and regulatory risk analysis. Designed to meet the needs of professionals making high-stakes decisions based on nonexperimental data, the book addresses the lack of clear, testable criteria for credible causal inference. Real-world examples throughout the book illustrate diagnostics, falsification tests, and robustness checks, linking evidence directly to decision-making. Bridging multiple causal frameworks and modern machine-learning methods, it equips readers to assess when data support cause-and-effect conclusions, supporting more responsible, evidence-based policy and risk management.The book offers health risk analysts, epidemiologists, and policy professionals a practical guide to valid causal inference from observational data. It is also ideal for graduate students and workshop participants in data science, decision analysis, risk analysis, and AI/ML applications.

Valid Causal Inference from Observational Data

Managing AI Risk

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