Blending strategic frameworks, real-world case studies, and cutting-edge AI insights, this guide equips product managers with the tools to navigate challenges, drive innovation, and build scalable, high-impact AI products.
Free with your book: DRM-free PDF version + access to Packt's next-gen Reader*
Key Features
- Gain insights into AI product discovery, market fit, and execution through structured frameworks.
- Learn to translate complex AI capabilities into real-world solutions that drive value.
- Understand ethical AI, bias mitigation, and compliance to build responsible AI products.
Book Description
AI is rapidly transforming product management, presenting new challenges and business opportunities. As AI-driven solutions become more complex, product managers must bridge the gap between technological capabilities and business needs. This book provides a detailed roadmap for successfully building and maintaining AI-driven products, serving as an indispensable companion on your journey to becoming an effective AI product manager. In this second edition, you'll find fresh insights into generative AI, and responsible AI practices with the most relevant tools for building AI-powered products.
Authored by a leading AI product expert with years of hands-on experience in developing and managing AI solutions, this guide translates complex AI concepts into actionable strategies. Whether you're an aspiring AI PM or an experienced professional, this book offers a structured approach to defining AI product vision, leveraging data effectively, and aligning AI with business objectives. With new case studies and refined frameworks, this edition provides deeper insights into ethical AI, cross-functional collaboration, and deployment challenges.
By the end of this book, you’ll be equipped with the knowledge to drive AI product success with key techniques for identifying AI opportunities and managing risks in a rapidly evolving landscape.
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What you will learn
- Plan your AI PM roadmap and navigate your career with clarity and confidence
- Gain a foundational understanding of AI/ML capabilities
- Align your product strategy, nurture your team, and navigate the ongoing challenges of cost, tech, compliance, and risk management
- Identify pitfalls and green flags for optimal commercialization
- Separate hype from reality and identify quick wins for AI enablement and GenAI
- Understand how to develop and manage both native and evolving AI products
- Benchmark product success from a holistic perspective
Who this book is for
This book is tailored for aspiring and experienced product managers, AI strategists, and business leaders aiming to integrate AI into their products. A foundational understanding of AI is expected and reinforced throughout the book. It is particularly valuable for professionals looking to bridge AI and business strategy, optimize AI/ML applications, and drive data-informed decision-making. Engineers, designers, and executives seeking to align AI capabilities with user needs and market demands will also benefit from the insights and real-world case studies on building scalable AI products.
Table of Contents
- Understanding the Infrastructure and Tools for Building AI Products
- Model Development and Maintenance for AI Products
- Deep Learning Deep Dive
- Commercializing AI Products
- AI Transformation and Its Impact on Product Management
- Understanding the AI-Native Product
- Productizing the ML Service
- Customization for Verticals, Customers, and Peer Groups
(N.B. Please use the Read Sample option to see further chapters)
Irene Bratsis is a director of digital product and data at the International WELL Building Institute (IWBI). She has a bachelor's in economics and international relations from Simmons University. After completing various MOOCs in data science and big data analytics, she completed a data science apprentice program with Thinkful. Before joining IWBI, Irene worked as an operations analyst at Tesla, a data scientist at Gesture, a data product manager at Beekin, and head of product at Tenacity. Irene volunteers as NYC chapter co-lead for Women in Data, has coordinated various AI accelerators, moderated countless events with a speaker series with Women in AI called WaiTalk, and runs a monthly book club focused on data and AI books.
