Generative AI for Business
ISBN: 9789377068530
516 pages
Publication Year: 2026
For more information write to us at: acadmktg@wiley.com
Description
Generative AI for Business is a comprehensive, practice-first guide to deploying generative AI across enterprise functions. As AI moves beyond analyzing historical data to actively creating, reasoning, and executing, organizations face an enterprise tipping point. This book bridges the critical gap between heavy technical texts and purely theoretical works, providing business leaders, strategists, and operational managers with the knowledge and tools to harness AI for measurable ROI. Organized into four logical sections — Foundations, Architecture & Governance, Cross-Functional Playbooks, and Looking Ahead — the book guides readers from core concepts through organizational strategy into real-world departmental applications.
- Designed to be relentlessly practical — balances conceptual rigor with real-world, actionable applications.
- Labs and Agentic Workflows included in every section for hands-on practice over abstract philosophy.
- Cross-functional playbooks span Marketing, Sales, Operations, Finance, HR, and Product Innovation.
- Covers responsible AI, ethics, governance frameworks, and global compliance requirements.
- Strategic frameworks for building AI roadmaps, calculating risk-adjusted ROI, and managing organizational change.
- 16-chapter structure with 90-day action plan for launching AI pilots and establishing governance.
- Online resources: PowerPoint Slides, Instructor Solutions Manual, Teaching Notes, and Lab Activities.
Section I Foundations
Chapter 1 The Generative AI Revolution: From Text to Multimodal Creation
1.1 Introduction
1.2 What Is Generative AI?
1.3 Beyond Text: The Power of Multimodal Creation
1.4 The Power Behind the Scenes: Understanding Foundation Models
1.5 Tracing the Evolution of Generative AI
1.6 The Transformative Impact Across Industries
1.7 Evaluating the Economic and Competitive Implications of Generative AI
1.8 The Strategic Imperative: Why Businesses Must Act Now
Summary | Conceptual Questions | Key Terms | Fun Facts | References
Chapter 2 Mastering Generative AI Platforms and Tools
2.1 Introduction
2.2 Navigating the Generative AI Landscape
2.3 Understanding the 'Brains' — Foundation Models and Their Business Strengths
2.4 Fine-Tuning the Output — Key Parameters for Control and Cost Efficiency
2.5 Implementing Workflow Automation with Generative AI
2.6 Making Informed Choices: Developing Platform Selection Criteria
2.7 Configuring Generative AI for Specific Business Needs
Summary | Conceptual Questions | Key Terms | Fun Facts | References
Chapter 3 Prompt Engineering Essentials: From Basics to Advanced Techniques
3.1 Introduction
3.2 Foundations of Prompt Engineering
3.3 Applying Essential Prompt Engineering Techniques
3.4 Advanced Prompt Engineering Techniques
3.5 Diagnosing, Refining, and Optimizing Prompts
3.6 Challenges in Prompt Optimization
3.7 Building a Personal Prompt Library
Summary | Conceptual Questions | Key Terms | Fun Facts | References
Chapter 4 Single-Agent Systems: From Simple Workflows to Agentic AI
4.1 Introduction
4.2 The Leap from Simple Prompts to Automated Systems
4.3 The Core Components of AI Autonomy
4.4 Orchestrating Complexity: How Agents Execute Multi-Step Reasoning
4.5 Designing Practical Applications and Defining Boundaries
Summary | Conceptual Questions | Key Terms | Fun Facts | References
Chapter 5 Under the Hood: Model Types, Retrieval-Augmented Generation and Fine-Tuning
5.1 Introduction
5.2 The AI Engine Room: A Simplified View of Model Architecture
5.3 The Knowledge Bridge: Retrieval-Augmented Generation (RAG)
5.4 Customizing the Brain: Fine-Tuning vs. Prompt Engineering
5.5 Strategic Integration and Measuring Success
Summary | Conceptual Questions | Key Terms | Fun Facts | References
Chapter 6 Data, Ethics, and Responsible AI Use
6.1 Introduction
6.2 Key Tenets of Responsible AI: Fairness, Accountability, Transparency, Privacy
6.3 Identifying and Mitigating Risks: Hallucinations, Error Cases, and Bias
6.4 Mitigation Strategies
6.5 Implementing Authenticity and Watermarking for Content Verification
6.6 Understanding Legal Implications: Copyright and Deepfakes
6.7 Building AI Governance Frameworks: Global Standards and Organizational Oversight
6.8 Implementing Guardrails Using Open-Source Libraries
6.9 Ensuring Regulatory Compliance and Establishing Ethical GenAI Guidelines
6.10 Auditing Responsible Generative AI Practice
Summary | Conceptual Questions | Key Terms | Fun Facts | References
Section II Architecture and Governance
Chapter 7 Crafting a Generative AI Roadmap
7.1 Introduction
7.2 Defining the Generative AI Strategy: From Vision to Value
7.3 Pinpointing Value: Aligning Use Cases with P&L Impact
7.4 Structuring for Scale: Organizational Models and Governance
7.5 The Implementation Blueprint: Designing the Roadmap
7.6 Establishing Success Metrics and Managing Risk
Summary | Conceptual Questions | Key Terms | Fun Facts | References
Chapter 8 Change Management and Talent
8.1 Introduction
8.2 Designing Change Management Programs for Generative AI Adoption
8.3 Developing AI Literacy, Upskilling, and Fluency Programs
8.4 Creating Talent Development Strategies: Job Redesign and Workforce Transformation
8.5 Building an AI-Inclusive Culture and Talent Acquisition
8.6 Implementing AI-Augmented Performance Management
Summary | Conceptual Questions | Key Terms | Fun Facts | References
Section III Cross-Functional Playbooks
Chapter 9 Marketing and Customer Experience
Chapter 10 Sales and Service Copilots
Chapter 11 Document and Content Synthesis for Operations
Chapter 12 Creative Content Generation for Product Innovation
Chapter 13 Generative AI for Finance, Risk, and Compliance
Chapter 14 Generative AI for HR and Employee Experience
Section IV Looking Ahead
Chapter 15 Multimodal Agents, Edge AI, and Autonomous Enterprises
Chapter 16 Your First 90-Day Action Plan