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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The Essential Guide to AI and Generative AI Product Creation from a Veteran AI Leader and Educator
In Successful AI Product Creation: A 9-Step Framework, AI product leader, professor of product management and AI, and industry expert, Prof. Shub Agarwal delivers the ultimate playbook—a comprehensive, step-by-step guide to Building, Scaling, and Integrating AI and Generative AI into real-world products. Drawing from over two decades of experience, this comprehensive guide bridges the gap between AI technology and business impact, ensuring you can navigate the AI revolution with confidence.
Featuring Forewords by:
- Ted Shelton, Chief Operating Officer at Inflection AI (co-founded by Reid Hoffman)
- Dr. Jia Li, Co-Founder and Chief AI Officer at LiveX AI; Founding Head of R&D at Google Cloud AI; AI Professor at Stanford
What You'll Learn:
- Complete 9-Step AI Product Creation Framework: Master the entire AI product lifecycle from discovery and experimentation to scaling, governance, and AI model lifecycle management.
- 20+ Real-World Case Studies: Learn from successful AI implementations across healthcare, finance, e-commerce, retail, manufacturing, and big tech companies like Google, Meta, Amazon, and Apple.
- Traditional AI vs. Generative AI: Understand when to use each approach, how to leverage models like GPT and transformers, and key differences in adoption strategies.
- AI Model Performance and Ethics: Address challenges like bias, fairness, model drift, and regulatory compliance.
- Practical Tools and Templates: Access decision-making frameworks, checklists, and internal diagrams that guide seamless execution.
Who Should Read This Book?
- AI Product Managers and Tech Leaders: A strategic and tactical guide for AI integration.
- Entrepreneurs and Founders: Leverage AI for competitive advantage and scalability.
- Business Executives and Decision-Makers: Understand AI's potential for growth and optimization.
- Students and Aspiring AI PMs: Develop industry-ready skills through real-world case studies.
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Seitenzahl: 391
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Title Page
Preface
Foreword by Jia Li
Bridging the Gap: From Demos to Impactful Products
A Unique Perspective
Why This Book Matters
Foreword by Ted Shelton
Introduction: Creating Successful AI Products—A Nine-Step Framework
The Evolution of AI Product Management
A Personal Journey Through AI
The Nine-Step Framework
Who This Book Is For
Our Journey Ahead
Part I: Strategic Foundation
Chapter 1: Mapping Problems to Business Goals for AI Products
Understanding the Role of AI in Business Problem-Solving
The Importance of Aligning AI Solutions with Business Goals
Problem Analysis Framework
Developing a Framework for AI Implementation Decisions
Practical Examples of AI Solutions in Action
The Revolutionization of Generative AI
Endnote
Chapter 2: Curiosity to Learn AI Use Cases and Emerging Technical ML Concepts
The Foundation of Machine Learning
A Walk Through the AI Landscape
Deep Learning and Generative AI—The Frontiers of Innovation
The Model Training Process, Demystified
Advanced AI
Real-World AI: Bridging Theory and Practice
Chapter 3: Experimentation Mindset and Room in the Roadmap to Innovate
The Experimentation Mindset
Key Aspects of an Experimentation Mindset
Experimentation in AI Projects
Integrating Experimentation into the AI Product Roadmap
Real-World Case Studies
Traditional AI vs. Generative AI
Part II: Implementation & Integration
Chapter 4: Integrating the MDLC with the SDLC
Understanding the MDLC
Stages of SDLC
Synchronizing the MDLC and SDLC for Seamless Integration
Ensuring Effective Communication and Collaboration Between Teams
Best Practices for Integrated Development and Deployment
Overcoming Common Challenges in Integrating the MDLC and SDLC
Case Studies of Successful MDLC and SDLC Integration
Traditional AI vs. Generative AI
Chapter 5: Scaling Research to Production
Importance of Developing a Research Mindset
Strategies for Developing a Research Mindset
Transitioning from Research to Production
Understanding the Research
Developing Prototypes and Iterative Testing
Generative AI and Traditional AI within Scaling Research to Production
Traditional AI vs. Generative AI
Case Studies of Scaling AI Research to Production
Optimizing Supply Chain Management with Predictive Analytics
Endnote
Chapter 6: Acceptance Criteria in the World of AI
Understanding Acceptance Criteria in AI
Defining Functional Requirements and Performance Standards
Managing Data Quality, Scalability, and Compliance
Developing a Ramp-Up Plan for AI Deployments
Traditional AI vs. Generative AI
Case Studies of Acceptance Criteria in the World of AI
Part III: Sustainable Excellence & Innovation
Chapter 7: Patience and Plan to Surpass Human-Level Performance
The Importance of Patience in AI Development
Strategic Planning for AI Implementation
Understanding the Innovator's Dilemma in AI
Key Strategies for Achieving and Surpassing Human-Level Performance
Innovating with Generative and Traditional AI
Traditional AI vs. Generative AI
Case Studies: Overcoming Initial Underperformance in AI
Chapter 8: Model Explainability, Interpretability, Ethics, and Bias
Understanding Explainability in AI Models
The Significance of Model Interpretability
Ethical Considerations in AI Models
Addressing Bias in AI Models
Balancing Performance, Explainability, and Fairness
Case Studies: Model Explainability, Interpretability, Ethics, and Bias
Traditional AI vs. Generative AI in Model Explainability, Interpretability, Ethics, and Bias
Chapter 9: Model Operations: Model Drift Management
Understanding Model Drift
Key Components of Model Operations
Strategies for Monitoring and Managing Model Drift
The Role of Continuous Data Collection and Retraining
Automation in Model Drift Management
Incorporating Model Operations into the Product Roadmap
Traditional AI vs. Generative AI in Model Drift Management
Case Studies: Model Operations
Chapter 10: AI Is the New UX: Transforming Human Interaction
The Evolution of Intelligence-First Product Management
The Role of an AI-UX Product Manager
AI as the Invisible Interface
Multimodal Interactions
Business Insights: Chat with Data
Balancing Generative AI and Traditional AI in Model Operations
Case Studies: Real-Life Applications of AI as the New UX
Conclusion: The Dawn of Intelligence-First Product Creation—A New Chapter in Human Innovation
Chapter 11: Understanding Generative AI for Product Management
Introduction to Generative AI
Practical Applications of Generative AI
Endnote
Glossary
Acknowledgments
About the Author
Index
Copyright
Dedication
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Old vs. New AI: mapping problems to business goals for AI products...
Chapter 2
Table 2.1 Four types of ML
Table 2.2 Keras benefits
Table 2.3 Core differences between traditional AI and GenAI, providing AI pr...
Chapter 3
Table 3.1 Embracing generative AI: experimentation and flexibility redefinin...
Chapter 4
Table 4.1 Integrating the MDLC with the SDLC: bridging traditional AI and ge...
Chapter 5
Table 5.1 Key differences between old and new AI: scaling research to produc...
Chapter 6
Table 6.1 Confusion matrix
Table 6.2 Acceptance criteria in the world of AI: old vs. new AI
Chapter 7
Table 7.1 Evolution of AI: from visionary, incremental, and scenario-based s...
Chapter 8
Table 8.1 Model explainability, interpretability, ethics, and bias
Chapter 9
Table 9.1 Model operations: model drift management (core principle: AI model...
Chapter 11
Table 11.1 Generative AI evaluation framework: key dimensions for assessing ...
List of Illustrations
Introduction
FIGURE I.1 A nine-step framework for AI product creation, organized into thr...
Chapter 1
FIGURE 1.1 Aligning AI with business goals boosts performance across efficie...
FIGURE 1.2 The problem analysis framework: a structured approach for AI chal...
FIGURE 1.3 AI solution feasibility framework: a step-by-step guide to evalua...
FIGURE 1.4 AI-powered agricultural transformation: from traditional farming ...
FIGURE 1.5 AI fashion revolution: mass to personalized design
Chapter 2
FIGURE 2.1 Evolving models within the AI taxonomy. This diagram illustrates ...
FIGURE 2.2 A mind map illustrating different categories of ML within AI. It ...
FIGURE 2.3 Examples of high-level APIs
FIGURE 2.4 Three essential ML frameworks every AI product manager should be ...
FIGURE 2.5 Neural networks and deep learning: from fundamentals to advanced ...
FIGURE 2.6 Deep learning algorithms: key applications and evaluation metrics
FIGURE 2.7 Advanced AI technologies: GenAI, large language models, and AGI
FIGURE 2.8 ML workflow: a cyclical process from data preparation and feature...
FIGURE 2.9 Levels of AI: hierarchical classification showing NAI, SAI, and A...
FIGURE 2.10 Hierarchy of AI technologies: GenAI within deep learning, ML, an...
FIGURE 2.11 The purpose of GenAI
FIGURE 2.12 Tesla's advanced manufacturing AI architecture: from high-speed ...
FIGURE 2.13 End-to-End medical GAN system: bridging technical implementation...
FIGURE 2.14 Multilayer AI chatbot architecture: from customer query to intel...
FIGURE 2.15 Barcelona's TensorFlow-powered urban intelligence system: from I...
Chapter 3
FIGURE 3.1 AI product creation roadmap: from research to launch
FIGURE 3.2 AI product experimentation timeline: progressing from initial exp...
FIGURE 3.3 AI-powered supply chain optimization: a framework for efficiency ...
FIGURE 3.4 AI content moderation pipeline systematic experimentation from mo...
FIGURE 3.5 AI content creation experimentation framework systematic testing ...
Chapter 4
FIGURE 4.1 Visual representation of the integration between the MDLC and the...
FIGURE 4.2 Key stages of the MDLC, illustrating the end-to-end process from ...
FIGURE 4.3 Stages of the SDLC, outlining the process from requirement analys...
FIGURE 4.4 Collaboration dynamics between the MDLC and SDLC over a 12-week p...
FIGURE 4.5 ML and software engineering in enterprise systems in manufacturin...
FIGURE 4.6 Architecting success: 45 percent higher engagement through ML–sof...
FIGURE 4.7 Dual pipeline integration: ML–software fusion
Chapter 5
FIGURE 5.1 From concept to creation: the journey of transforming research in...
FIGURE 5.2 From spark to scale: the end-to-end journey of bringing an idea t...
FIGURE 5.3 AI recommendation system pipeline from research to production, sh...
FIGURE 5.4 Research-to-production AI journey from academic research to deplo...
Chapter 6
FIGURE 6.1 A stepped visualization showing the gradual ramp-up progression f...
FIGURE 6.2 Confusion matrix showing true positives (TP), false positives (FP...
FIGURE 6.3 Evolution of acceptance criteria across software development para...
FIGURE 6.4 AI-driven fraud detection: a risk-balanced implementation approac...
FIGURE 6.5 From metrics to value: building AI acceptance criteria into e-com...
FIGURE 6.6 Metrics to operations: building AI acceptance criteria for predic...
FIGURE 6.7 Balancing art and automation in creative AI
Chapter 7
FIGURE 7.1 AI evolution framework: the journey from initial deployment to su...
FIGURE 7.2 Data reality check: data scientists spend 60 percent of their tim...
FIGURE 7.3 Strategic planning for AI implementation
FIGURE 7.4 The parallels between AI evolution and the classic innovator's di...
FIGURE 7.5 Balancing generative AI and traditional AI: generative AI excels ...
FIGURE 7.6 Three-phase evolution from initial AI learning to superhuman perf...
FIGURE 7.7 Beyond human curation: how recommendation engines evolve to uncov...
FIGURE 7.8 Phased AI implementation drives performance, reliability, and eff...
Chapter 8
FIGURE 8.1 Conceptual framework of AI understanding: bridging model decision...
FIGURE 8.2 The path to trust: model interpretability bridges the gap between...
FIGURE 8.3 Illustration of systemic bias in AI: how biased data models, infl...
FIGURE 8.4 Balancing AI: optimizing performance, explainability, and fairnes...
FIGURE 8.5 From challenges to outcomes: building responsible AI credit asses...
FIGURE 8.6 Building trustworthy AI healthcare solutions through precise pred...
FIGURE 8.7 Ensuring performance, fairness, and transparency for safe autonom...
FIGURE 8.8 Empowering personalized shopping experiences through explainable ...
FIGURE 8.9 Framework for revolutionizing fashion design through explainable ...
FIGURE 8.10 Comparing model explainability: traditional AI's transparent dec...
Chapter 9
FIGURE 9.1 Illustration of model drift: over time, changes in data patterns ...
FIGURE 9.2 Nine strategies for monitoring and managing model drift
FIGURE 9.3 Integrating the AI product roadmap and model operations: tools li...
FIGURE 9.4 Early detection through performance triggers helped the e-commerc...
FIGURE 9.5 Drift monitoring triggers model updates and ensures timely, accur...
FIGURE 9.6 Framework for managing generative AI drift: align the audience, e...
Chapter 10
FIGURE 10.1 AI as the new UX: transforming from traditional systems to intui...
FIGURE 10.2 AI revolutionizes business insights by enabling natural language...
FIGURE 10.3 AI as the new UX: revolutionizing design with real-time collabor...
FIGURE 10.4 AI as the new language of business: from data to dialogue. (Visu...
FIGURE 10.5 AI as legal evolution catalyst: from process to intelligence. (S...
FIGURE 10.6 AI as the new UX: transforming healthcare through intelligent in...
FIGURE 10.7 AI as the new language of safety: from reaction to prevention fr...
FIGURE 10.8 Transforming education with AI: personalized learning paths enha...
FIGURE 10.9 Amara's Law: we tend to overestimate the impact of technology in...
Chapter 11
FIGURE 11.1 Transformer model architecture with encoder and decoder componen...
FIGURE 11.2 Generative AI technology ecosystem overview. This diagram illust...
FIGURE 11.3 Generative AI training pipeline two-phase training process showi...
Guide
Cover
Table of Contents
Title Page
Copyright
Dedication
Preface
Foreword by Jia Li
Foreword by Ted Shelton
Introduction: Creating Successful AI Products—A Nine-Step Framework
Begin Reading
Glossary
Acknowledgments
About the Author
Index
End User License Agreement
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Praise for Successful AI Product Creation
“Shub's book is a much-needed resource in the space where AI product creation remains elusive. It provides a comprehensive step-by-step framework and insightful case studies, addressing both generative AI and traditional AI. This guide is invaluable for experienced and aspiring AI product managers and product creators, offering practical strategies to bring AI products to life.”
—Pascal Bornet, best-selling author on AI; formerly with McKinsey & Company
“In Successful AI Product Creation: A 9-Step Framework, Shub A. brings his two decades of experience as an AI practitioner and researcher to bear on a user-friendly, step-by-step guide for anyone seeking to better operate at the intersection of product management and AI. A major pain point for business leaders in the wake of the AI/ML explosion has been figuring out how to make AI core to their company's strategy and products. Through detailed models, rich case studies, and helpful comparisons to more traditional software product management, Shub delivers a practical ‘how to’ that should help alleviate that pain.”
—Noah Askin, Professor at UC Irvine and INSEAD's Product Leadership Programme
“If you want to create successful AI products, Shub's book is a must-read. He shares incredible insights drawn from his many years of doing this. His book makes these easily accessible to help you navigate the complexities and challenges.”
—Jim Berardone, Professor of the Practice of Product Management, Carnegie Mellon University School of Computer Science
“Shub, a visionary leader with a proven track record in AI, shares his battlefield-tested strategies in his new book. This seminal guide equips product leaders and entrepreneurs with the tools to build innovative AI products, streamline team interactions, and foster a culture of AI-driven product development. This book is poised to become the definitive resource for navigating the AI product landscape.”
—Mac Gainor, Founder and CTO, TipHaus; former Director of Engineering at a Silicon Valley-based Generative AI startup
“Shub's book is an essential resource for AI product managers in both startups and large firms. It blends academic rigor with industry expertise in a unique and engaging way, providing a clear framework and real-world case studies. This book is a must-have educational resource, rich with visuals, illustrations, and memorable quotes, for anyone aiming to excel in AI product development.”
—Alan Cheslow, Product Leader, Meta
“Shub's book is a masterclass in AI and Generative AI product management.”
—Ariel Kedem, CPO, ZeroToWow
“Traditional ML/AI has been around for decades. With the emergence of Generative AI (and ChatGPT as poster child), the software stack will be rewritten across industries over time. As a Product Manager and a thought leader, when building features or products leveraging AI, you need a solid foundation of the concepts and to learn the nuances vs. the traditional mindset. This book helps you start building that foundation! It elucidates the concepts, and more importantly, highlights the high-level nuances with Gen AI. Highly recommend!”
—Bhushan Suryavanshi, Founder of an AI startup; former PM Lead at Amazon, Zynga, and multiple startups
“The technological advancement of AIML has a profound impact on the efficiency of UX, a profession that requires constant awareness of the latest trends and innovation. Shub's emphasis on Good Design and how business leaders should utilize AI for their customers' experience makes his book a wonderful read for anyone who is interested in entering the AIML market. By incorporating AIML using Shub's methods, UX designers can ideate faster and visualize their ideas more accurately to create fuller storytelling for their stakeholders. On the other side, the end users will be able to enjoy an elevated user experience with ML customization, making products with complex user scenarios simpler and faster to use.”
—Mary Shao, UX Design Leader at Apple; formerly with Amazon
“Shub A.'s book is far more than just a guide—it is a catalyst for transformative 'aha' moments. Brimming with practical insights and a clear, actionable framework, it empowers product leaders to build groundbreaking AI products in a rapidly evolving landscape where AI is the driving force of innovation.”
—Charanya “CK” Kannan, Co-Founder and CEO, stealth startup; former Chief Product Officer, Talkdesk
“Shub provides a practical framework for accelerating AI product deployment in startups and large organizations. As an AI entrepreneur, I found Successful AI Product Creation invaluable. A must-read to scale impactful AI solutions—use it, share it, and drive innovation!”
—Natalie Gil, co-founder, Darshana; angel investor; MIT Sloan Fellow; former executive with AWS, Goldman Sachs, and Microsoft
“As a product leader at Google and CEO of Fountain9, I've navigated the real challenges of AI adoption firsthand. This book provides a clear, actionable blueprint for implementing generative AI—an essential guide for anyone committed to driving AI transformation.”
—Niki Khokale, former head of Product Strategy & Analytics at Google; co-founder and CEO, Fountain9
“A practical guide for anyone seeking to navigate the complexities of AI product development. It offers a battle-tested framework distilled from two decades of experience. This book will equip readers with the intuition to build AI solutions that deliver real economic value while remaining responsible and sustainable. It is a must-read for creating AI products with the potential to transform industries.”
—Jai Rawat, Serial Entrepreneur; Startup Advisor; Investor; Board Member, IIT Kanpur
“Shub’s book is a breath of fresh air amidst the current AI climate of doom and hype. It is optimistic yet grounded in reality, and offers deep insight into how product leaders can not only adapt but thrive in the new age of AI. Shub provides a method to the madness in the form of useful frameworks and thoughtful real-world examples that make the concepts tangible. Overall, the book is well-structured, easy to follow, and an essential resource for AI product leaders.”
—Soumya Batra, Co-Author, Llama Models; Ex-Tech Lead, Applied Research Scientist at Meta; Ex-Microsoft; MS in Language Technologies, Carnegie Mellon
“In the age of AI, product managers are poised to lead transformative change. Shub not only delivers an enduring strategic framework for navigating product management but also skillfully adapts core principles—like stakeholder management—for the evolving demands of AI-driven products. His use of real-world case studies brings these concepts to life, providing concrete examples that make complex ideas accessible and actionable. Through crisp frameworks and practical insights, Shub equips PMs to not just keep up with change but to lead it with confidence and clarity.”
—Cy Khormaee, AI Faculty at UCLA; Ex-Director of Product at Google; Investor & Advisor; Harvard MBA
Shub Agarwal
Successful AI Product Creation
A 9-Step Framework
Preface
Today's businesses are caught in a paradox. The rapid democratization of AI tools and technologies has made AI development more accessible than ever. Yet creating successful AI products remains a complex challenge that eludes even the most experienced teams. The gap lies not in the availability of technology but in the absence of a systematic approach to AI product creation.
AI development fundamentally differs from traditional software development. Its probabilistic nature, its hunger for data, and its ability to learn and adapt demand new frameworks and methodologies. This is not about replacing existing product development wisdom, but about building specialized knowledge for the AI age.
Our goal is simple: to transform AI product creation from an art of chance into a discipline of methodology.
The future of product creation is being rewritten by AI—and we need AI product creators. Our success in this new era depends not just on mastering the technology, but on following a disciplined approach to bringing AI products to life.
Foreword by Jia Li
As someone who has been at the forefront of AI innovation—from serving as Head of R&D at Google Cloud AI, to leading AI research at Stanford, to founding/leading successful AI companies—I have witnessed firsthand both the tremendous potential and the significant challenges in bringing AI products to life. When asked to write the foreword for this book, I was struck by how timely and necessary this contribution is to our field.
The release of Successful AI Product Creation: A 9-Step Framework arrives at a pivotal moment in the evolution of artificial intelligence. This is both the most promising and the most challenging time to create AI products. AI technology is scaling at an unprecedented rate, fueled by AI Agents and tools that offer unmatched efficiency and innovation. Yet this rapid progress demands a steep learning curve—one that businesses and individuals must master to translate potential into meaningful impact.
Bridging the Gap: From Demos to Impactful Products
Throughout my career in AI, I've observed that although millions of AI demos showcase remarkable capabilities, only a small fraction evolve into fully realized products that generate real-world value. The gap between prototype and successful productization is where many efforts falter. Shub Agarwal's book provides a clear, actionable roadmap for overcoming these challenges, guiding readers through the essential steps to build AI products that succeed in the marketplace.
A Unique Perspective
Shub brings an unparalleled perspective to this subject. As an early pioneer in AI, he has over two decades of experience driving innovation across industries including technology, retail, and financial services. His work has led to multiple U.S. and international patents, and his leadership has reshaped how Fortune 50 companies and startups approach AI adoption.
In today's AI-driven world, interdisciplinary expertise is more critical than ever. Building successful AI products requires a blend of technical knowledge, business strategy, and user-focused design. Shub embodies this approach. As a senior product executive and faculty member at the University of Southern California's graduate program in Product Management and AI, he bridges the gap between industry and academia, equipping future leaders to thrive in this fast-evolving field.
Why This Book Matters
This book goes beyond abstract theories and buzzwords. It delivers a structured, actionable framework for AI product creation that addresses both the opportunities and challenges of the current landscape. By focusing on practical implementation, Successful AI Product Creation empowers readers to move from innovative ideas to impactful outcomes.
As I turned the pages of this book, I found myself learning and reflecting at every step. Shub's insights felt not only deeply relevant but also inspiring. The blend of technical expertise and strategic vision shared here made me reconsider what it truly takes to succeed in building AI products today. This book doesn't just teach—it motivates and equips you to take action.
Whether you are an entrepreneur, product manager, engineer, academic, or business leader, this book provides the tools and strategies needed to navigate the complexities of this era and seize its extraordinary opportunities. Shub Agarwal's insights and expertise make this guide an indispensable resource for anyone looking to succeed in the transformative world of AI.
Jia Li, Ph.D.
Co-Founder, Chief AI Officer & President, LiveX AI
IEEE Fellow
Founding Head of R&D, Google Cloud AI
Foreword by Ted Shelton
We are living through a profound shift in how we organize, work, and unlock human potential. Unlike past technological revolutions, this one doesn’t just change how we do things—it changes who we are capable of becoming. The emergence of AI and automation doesn’t need to replace human ingenuity; instead, it can amplify it—if we make the right choices. That’s why this book matters.
When I first read this book, I knew it stood apart. There are many books on AI, automation, and the future of work, but few that take such a balanced, insightful, and deeply human approach. Rather than focusing only on disruption or utopian possibilities, this book offers something far more valuable: a guide to action. It reminds us that many of the challenges we face today—navigating transformation, aligning technology with human well-being, and structuring work and society for long-term success—are not new. History has given us lessons on how to adapt to change, and this book brings those lessons to life in a way that is both rigorous and accessible.
What makes this book truly exciting is how it explores what is different this time. AI is not just another industrial innovation; it is a new kind of intelligence, one that requires us to rethink fundamental questions about human potential, decision-making, and collaboration. This book doesn’t just describe these shifts—it equips readers with the frameworks to navigate them.
What’s particularly impressive is how it bridges audiences. Some readers will be drawn to its technical depth, using it as a resource to stay ahead in this rapidly evolving space. Others will focus on its insights into leadership, human creativity, and organizational transformation. Wherever you fall on this spectrum, you will walk away more informed, more prepared, and—perhaps most importantly—more optimistic about what is possible.
Working as a consultant for some of the largest companies in the world and currently in my role at Inflection AI, I've had the opportunity to witness technology's enduring power to reshape industries and redefine human potential. We are living at a time of a historic opportunity when we will shape the future and define how technology and humanity flourish together.
This book is a crucial guide for anyone who wants to understand and shape the future. We are not just witnessing change; we are responsible for steering it in the right direction. And with the insights in these pages, we have a much better chance of getting it right.
Ted Shelton
Chief Operating Officer, Inflection AI
Former Expert Partner, Bain & Company
Introduction: Creating Successful AI Products—A Nine-Step Framework
The Evolution of AI Product Management
Creating successful AI products requires a new breed of product manager—one who combines a deep understanding of AI technologies with strategic leadership and user empathy. These roles span from AI product managers to AI engineering managers, both bearing responsibilities to develop AI products. As the field matures, these roles are being differentiated into two distinct groups: AI product creators and AI product operators.
Although AI's potential is vast, the systematic knowledge needed to consistently deliver successful AI products remains elusive. This book presents a battle-tested nine-step framework for successful AI product creation, distilled from two decades of hands-on experience, proven success across industries, academic research, and educational teaching.
From my early days as an AI researcher, fascinated by algorithms that mimic human intelligence, to my journey into product leadership and academia, I have witnessed firsthand the transformative power of AI. AI's impact is evident in its ability to improve efficiency, reduce human error, and increase productivity across various industries. For instance, in manufacturing, AI-powered robots perform tasks with unprecedented precision, and in customer service, chatbots handle routine inquiries, allowing human agents to focus on more complex issues. Yet despite these advancements, I've observed the persistent challenges product managers face in aligning AI's capabilities with business goals and executing AI capabilities in a way that is sustainable, builds breakthrough products, and pivots from software development thinking to AI-first thinking.
A Personal Journey Through AI
I stumbled into the AI world as a wide-eyed researcher, fascinated by algorithms that could cluster Google search results into meaningful patterns for human consumption. Working alongside colleagues, we dreamed of converting SQL into natural language and watched in amazement as robots learned through reinforcement in our labs. Little did I know that this early fascination would shape my entire career trajectory—from research labs to Fortune 50 companies, from Silicon Valley startups to university classrooms.
The real impact of AI crystallized during my time in industry. At a leading retail brand, I found myself in the president's office, facing hostile buyers who believed our AI-driven recommendations were “distracting” their customers. I still remember the tension in that room—being new to the company, surrounded by angry buyers, nervous about this pivotal moment. Armed with A/B testing data and revenue metrics—this was before “AI” even entered our corporate vocabulary—I watched skepticism transform into enthusiasm as the numbers revealed massive revenue impacts. That meeting shifted from hostility to excitement about scaling our recommendation engine, teaching me an invaluable lesson about the importance of measurable business impact alongside technical excellence.
Similar stories played out at Home Depot, where our “Frequently Bought Together” feature initially met strong resistance. But once launched, the ML-driven recommendations revealed insights human eyes had missed, diving deep into the catalog to uncover patterns nobody had spotted. The skeptics became our biggest champions, with business units practically begging for early access to our unfinished capabilities.
Amazon brought a different challenge entirely. Instead of chasing revenue, we leveraged computer vision to remove counterfeit products from the marketplace—a move that would deliberately reduce selection and impact short-term revenue. It was a powerful reminder that AI's value extends beyond immediate profits to building long-term trust and brand integrity.
Later, at a start-up, we were doing generative AI before it was cool, building AI assistants so convincing that users would try to ask them on dates. We grappled with AI management, ethics, and behavior control—issues that would later dominate global headlines. Each experience added new layers to my understanding of what it takes to create successful AI products.
Now, as I teach AI and business communications at USC, I'm struck by how the fundamental intuitions behind AI haven't changed, even as the technology has exploded. The frameworks I've developed through years in Silicon Valley start-ups, Fortune 50 companies, and academia still hold true. Although the landscape constantly evolves, it becomes much easier to grapple with this changing world if we can develop and maintain strong intuition about AI's capabilities and limitations.
The Nine-Step Framework
Let me walk you through our framework's journey, as illustrated in Figure I.1. This structured framework guides you through nine essential steps of successful AI product creation. Each step builds naturally on the previous ones, creating a comprehensive approach that you'll return to again and again as you develop AI products. This isn't just a high-level overview—we'll get into the weeds of day-to-day implementation and challenges, making this framework a practical companion for your daily development work.
The methodologies detailed within these pages have been rigorously tested and refined, ensuring their relevance and effectiveness across the diverse landscape of AI applications. From the precision-driven world of machine learning to the human-like understanding of natural language processing, and from the creative frontiers of generative AI to deep learning, the principles laid out here are robust and adaptable. Furthermore, the frameworks are detailed, not just high-level, and we get into the weeds of the day-to-day implementation and challenges. So I will expect readers to come back to these frameworks and details as they go through daily development. This is why not only AI product managers but also their counterparts, including engineering leaders and business leaders in this space, will find it useful.
We begin with mapping business problems to AI opportunities, developing the crucial skill of identifying where AI can create genuine value. You'll then build your understanding of AI use cases and essential machine learning concepts, creating a strong technical foundation. This pillar culminates in developing an experimentation mindset, teaching you to create space for innovation while maintaining practical constraints.
Moving forward, you'll master the critical integration of the model development life cycle (MDLC) with the traditional software development life cycle (SDLC)—a key challenge in AI product creation. You'll learn to scale AI projects from research environments to production systems and establish clear acceptance criteria that acknowledge AI's unique characteristics.
FIGURE I.1A nine-step framework for AI product creation, organized into three strategic pillars: strategic foundation, implementation and integration, and sustainable excellence and innovation. These steps align across business value, technical excellence, and user impact dimensions, driving AI as the new user experience paradigm.
The journey continues through planning for surpassing human-level performance while maintaining responsibility and ethics. You'll tackle the complex challenges of model explainability and bias, ensuring that your AI solutions build trust through transparency. The framework culminates in mastering model operations, where you'll learn to manage model drift and ensure sustained excellence in production.
This systematic progression, visualized in Figure I.1, will be your constant companion throughout the book. Each step builds naturally on the previous ones, creating a comprehensive approach to AI product creation. The framework culminates Chapter 10, where we explore how this systematic approach enables AI to become the new user experience paradigm—fundamentally transforming how humans interact with technology. The final chapter then expands into the creative frontiers of generative AI, equipping you with essential intuition to harness this transformative technology that is reshaping the boundaries of what AI can achieve.
Who This Book Is For
This book is meticulously designed for a diverse yet specialized audience poised at the forefront of technological innovation in the AI space:
AI product managers/creators:
Those at the helm of crafting AI-driven products will find this framework indispensable for navigating the nuanced challenges specific to AI product development. For the purpose of this book, I use these terms interchangeably: AI product creators today encompass not just product managers but also engineering and data science executives and others responsible for creating AI products.
Entrepreneurs in the AI domain:
Visionary founders can leverage this book as a roadmap to transforming innovative ideas into tangible, market-ready products.
Senior leaders:
Executives and decision-makers will gain strategic insights into effectively integrating AI into their product ecosystem.
Engineering and data science executives:
Technical leaders will gain comprehensive understanding of both strategic vision and implementation challenges in AI product development.
Academics and students:
Educators and learners will find a structured curriculum enhancing their understanding of AI's practical applications.
Technology enthusiasts:
Anyone fascinated by the intersection of artificial intelligence and product innovation will appreciate the practical guidance on realizing AI's promise.
Our Journey Ahead
As we stand at the dawn of the AI era, we have an extraordinary opportunity—and responsibility—to shape this technology's trajectory. My goal is simple yet ambitious: to help you develop the intuition needed to build AI solutions that deliver real economic value, execute with predictability, and remain responsible and sustainable. These aren't just theoretical frameworks—they're proven, practical approaches forged in the crucible of real-world challenges and deep reflection.
As you navigate through these nine steps, you will not only build a foundational understanding of creating AI products and integrating AI into existing offerings but also develop the foresight to anticipate trends, the agility to adapt to the dynamic AI landscape, and the vision to lead with innovation. This book affirms that with the right approach, anyone involved in AI can help shape a future of success, discovery, and creation.
Let's begin this journey of creating AI products that don't just work—they transform.
Part IStrategic Foundation
Value-First Focus with Strong Tech Innovation
Chapter 1Mapping Problems to Business Goals for AI Products
As product managers, our primary directive is to drive and fulfill business goals.1 In the field of artificial intelligence (AI), this becomes even more crucial as we navigate the cutting-edge intersection of technology and strategic business outcomes. This chapter delves into why understanding and aligning AI capabilities with business objectives is beneficial and essential for product managers in steering their organizations toward success. We will explore the indispensable role of AI in resolving business challenges, underscore the importance of ensuring that AI solutions are in harmony with business goals, and introduce a strategic framework for implementing AI decisions effectively. Additionally, we illuminate these concepts with real-world examples of AI applications, demonstrating their transformative impact across different sectors. This foundation equips product managers with the insights needed to harness AI effectively, ensuring that it is a powerful lever to achieve business ambitions and drive organizational progress.
The product manager's job is to clearly define the business problem and assess whether AI provides a unique advantage over existing solutions (human expertise or rule-based systems) while delivering tangible business value.
Understanding the Role of AI in Business Problem-Solving
People frequently associate AI with significant accomplishments, such as curing cancer or solving climate change. Everybody dreams up the biggest problems possible and attempts to solve them with AI. Or there's the flip side: not knowing what to do with AI and avoiding it accordingly. Hence, according to McKinsey, just 20 percent of surveyed executives use AI-related technologies in their businesses. Although emerging technologies are great, we can't realize their full potential and benefits until we utilize them to solve business problems or create new business opportunities.
When we talk about crafting the path for AI within the business sphere, it's essential to start with a fundamental premise: AI is not an end but a means to tackle complex business challenges. The role of an AI product manager begins with a crucial skill that may seem straightforward yet is profound in its implications—mapping problems to specific business goals. This process is less about the deep complexities of AI technology and more about leveraging this potent tool to drive strategic outcomes. The journey of mapping problems to business goals with AI starts with a comprehensive problem analysis. Understanding this premise allows AI product managers to sift through AI's myriad possibilities and anchor their focus on applications that align with their organization's strategic objectives. This alignment is crucial for several reasons. First, it ensures that applying AI technologies directly contributes to achieving key business outcomes, whether enhancing customer experience, streamlining operations, or unlocking new market opportunities. By starting with clear business goals, AI initiatives are more likely to receive the necessary support and resources from organizational stakeholders, from top management to frontline employees who will interact with AI systems daily.
Moreover, this focus on problem-solving and goal alignment fosters a culture of innovation and pragmatism. Instead of chasing AI for AI's sake, teams are encouraged to think critically about their challenges and the best tools for addressing them. This could mean deploying AI to automate mundane tasks and free up human creativity for more complex problems or using AI to analyze data in previously impossible ways, revealing insights that can drive more informed decision-making. In essence, AI becomes not just a technological investment but a catalyst for rethinking how business is done.
The Importance of Aligning AI Solutions with Business Goals
Aligning AI solutions with business goals is fundamental to the success of any AI initiative within an organization. As illustrated in Figure 1.1, aligning AI with business objectives drives substantial benefits across key business goals. In contrast, AI initiatives developed purely for the sake of technology, without a clear connection to business objectives, often fail to deliver meaningful impact. Throughout my journey across tech, fintech, banking, retail, and other industries—spanning both traditional companies and Silicon Valley start-ups—I've observed that one of the most significant challenges business executives face is effectively aligning AI initiatives with core business objectives. This strategic alignment is crucial because it ensures that investments in AI technology directly support the overarching objectives of the business. Ensuring that AI projects are tightly linked to business goals allows organizations to avoid the pitfalls of pursuing technology for technology's sake and instead leverage AI as a powerful tool to solve real-world business challenges. It is proven that businesses that effectively map problems to their strategic goals are better positioned to gain a competitive advantage because they can respond to challenges and opportunities in a more structured and purposeful manner.
FIGURE 1.1Aligning AI with business goals boosts performance across efficiency, cost, engagement, innovation, and revenue metrics
At the core of this alignment lies the critical role of AI product management, acting as the bridge between the potential of AI technologies and the strategic needs of the business. This starts with a deep understanding of the business's core objectives: market expansion, customer engagement improvement, or operational efficiency. It's about identifying the “why” behind each goal and mapping how AI can serve as a “how” to achieve these ends. For instance, if the goal is to enhance customer engagement, deploying AI-driven chatbots for 24/7 customer service might be identified as a strategic solution, directly linking AI capabilities with business aspirations. The rapid evolution of AI has revolutionized the way businesses approach problem-solving. Industries like healthcare, finance, and transportation have been transformed with the implementation of AI, solving problems that were once deemed too complex or time-consuming. For example, AI-powered diagnostic tools have improved the accuracy and speed of disease detection. Even though it is not a panacea for all business problems, the impact of this new technology also affects many other aspects.
The importance of aligning AI with business goals stems from the unique capability of AI to analyze data and generate insights at a scale and speed beyond human capacity. However, these technological capabilities risk underutilization or misdirection without a clear connection to business goals. When AI solutions are aligned with business objectives, they have the potential to transform business operations by automating routine tasks, uncovering new insights from data, and enabling more personalized customer experiences. This can lead to significant competitive advantages, such as increased efficiency, reduced costs, and improved customer engagement. AI enables businesses to make data-driven decisions by analyzing vast amounts of data quickly and accurately: this empowers organizations to base their strategies on real insights rather than intuition or incomplete information. In addition, AI-driven predictive analytics can forecast future trends, customer behavior, and market dynamics so that businesses can anticipate demand, identify potential issues, and make proactive decisions to stay ahead of the competition.
On top of that, this AI alignment is essential for securing the necessary support and resources from across the organization. It helps to ensure that AI projects are prioritized according to their potential to impact the business's bottom line. It also facilitates clearer communication about AI initiatives’ purpose and expected outcomes, making it easier to rally cross-functional teams around these projects and foster a culture of innovation and collaboration. AI automates repetitive and time-consuming tasks, freeing employees to focus on more creative and strategic problem-solving, increasing overall productivity, and reducing operational costs. It also allows businesses to offer highly personalized experiences to their customers. Through customer data analysis, AI can suggest products or services tailored to individual preferences, enhancing customer satisfaction and engagement. Powered by AI, chatbots and virtual assistants offer 24/7 customer support, answer queries, address problems, assist customers efficiently, enhance customer service, and minimize response times.
AI can optimize supply chain management by predicting demand, managing inventory, improving logistics, reducing costs, minimizing waste, and ensuring that products are available when and where needed. The algorithms can detect fraudulent activities in real time by analyzing transaction data and identifying unusual patterns; this way, businesses can protect themselves from financial losses and maintain the trust of their customers. In manufacturing and production, AI-powered systems can inspect and identify defects in real time, ensuring product quality and reducing the number of defective items that reach the market. Regarding human resources, AI can streamline the hiring process by analyzing resumes, conducting initial screenings, and identifying the most qualified candidates. AI can also assist in talent management and employee engagement.
AI can predict demand fluctuations and optimize inventory management, preventing overstocking or understocking, reducing costs, and improving customer satisfaction. In addition, AI-driven content-generation tools can create written content, such as reports, articles, and product descriptions, saving time and resources for businesses. In the financial sector, AI assesses and manages risk by analyzing complex datasets and identifying potential threats or opportunities. It can assist in product development by analyzing market data and consumer preferences, helping businesses create products more likely to succeed in the market. AI can accelerate research and development efforts in various industries, from pharmaceuticals to materials science, by analyzing large datasets and simulating experiments.
