Successful AI Product Creation (eBook)

A 9-Step Framework

Shub Agarwal (Autor)

eBook Download: EPUB

2025
435 Seiten
Wiley (Verlag)
978-1-394-33785-9 (ISBN)

Infos

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.

SHUB AGARWAL is a product leader and educator with nearly two decades of experience spanning academia, Fortune 50 companies, Silicon Valley startups, and Amazon. He teaches AI and Product Management at the University of Southern California, Los Angeles, and specializes in translating complex AI concepts into actionable product strategies.

Textauszug

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.1 A 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...

Produkt-Details

• Erscheint lt. Verlag: 1.4.2025
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Schlagworte: 9StepAI • 9StepstoAI • AI commercialization • AI governance and ethics • AI model development • AI-powered automation • AI product creation • AI product development • AI product innovation • AI product management • AI product manager • AI roadmap for executives • AI scaling framework • Generative AI product creation • Shub Agarwal
• ISBN-10: 1-394-33785-X / 139433785X
• ISBN-13: 978-1-394-33785-9 / 9781394337859