AI Product Manager’s Handbook - Second Edition: Build, integrate, scale, and optimize products to grow as an AI product manager

Whether you're a seasoned professional or a newcomer to the world of AI product management, this is your definitive guide. Embark on a transformative journey into the future of intelligent product management. Key Features Chart a successful career path in the AI product management field Packed with real-world examples, practical insights, and actionable strategies Navigate the complexities of AI product development and evolve your existing products Book Description This book will provide you with a detailed roadmap for successfully building, maintaining, and evolving artificial intelligence (AI)-driven products, serving as an indispensable companion on your journey to becoming an effective AI PM. We'll explore the AI landscape, demystify complex terms, and walk you through infrastructure, algorithms, and deployment strategies. You’ll master essential skills to understand the optimal flow of AI processes, learn about the product development life cycle from ideation to deployment, and familiarize yourself with commonly used model development techniques. We'll discuss the intricacies of building products natively with AI, as well as evolving traditional software product to AI products. Regardless of your use case, we’ll show you how you can craft compelling stories to captivate your audience. We'll help you find the right balance between foundational product design elements and the unique aspects of managing AI products, so you can prioritize wisely. We’ll also explore career considerations for AI PMs. By the end of this book, you will understand the importance of AI integration and be able to explore emerging AI/ML models like Generative AI and LLMs. You’ll discover open-source capabilities and best practices for ideating, building, and deploying AI products across verticals. 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 for aspiring and experienced product managers, as well as other professionals interested in incorporating AI into their products. Foundational knowledge of AI is expected and reinforced. If you are looking to better understand machine learning principles and data science methodologies, you will benefit from this book, particularly if you’re in a role where the application of AI/ML directly influences marketing outcomes and business strategies. Preface Lay of the Land – Terms, Infrastructure, Types of AI, and Products Done Well Understanding the Infrastructure and Tools for Building AI Products Definitions – what AI is and is not Introducing ML and DL The old – exploring ML A brief history of DL The new – exploring DL Invisible influences ML versus DL – understanding the difference ML DL Learning paradigms in ML Supervised learning Unsupervised learning Semi-supervised learning Reinforcement learning LLMs, NLP, GANs, and generative AI Succeeding in AI – how well-managed AI companies do infrastructure right The order – what is the optimal flow and where does every part of the process live? Step 1 – Definition Step 2 – Data availability and centralization Step 3 – Choose and train the model Step 4 – Feedback Step 5 – Deployment Step 6 – Continuous maintenance Storing and managing data Database Data warehouse Data lake (and lakehouse) Data pipelines Managing projects – IaaS Deployment strategies – what do we do with these outputs? Shadow deployment strategy A/B testing model deployment strategy Canary deployment strategy Example The promise of AI – where is AI taking us? Summary Additional resources References Model Development and Maintenance for AI Products Understanding the stages of NPD Step 1 – Discovery Step 2 – Define Step 3 – Design Step 4 – Implementation Step 5 – Marketing Step 6 – Beta testing Step 7 – Launch Model types – from linear regression to neural networks OKRs Objectives and key results Metrics and KPIs Training – when is a model ready for market? Deployment – what happens after training? Testing and troubleshooting Ethical retraining – the ethics of how often we update our models The current state of accountability Implementing ethical standards in your organization Summary Additional resources References Deep Learning Deep Dive Types of neural networks Multilayer perceptrons Case study Radial basis function networks Self-organizing maps Convolutional neural networks Recurrent neural networks Long short-term memory networks Deep belief networks Exploring generative AI models Generative adversarial networks Autoencoders Diffusion models Transformer models Emerging technologies – ancillary and related tech Explainability – optimizing for ethics, caveats, and responsibility Guidelines for success Summary References Leave a Review! Commercializing AI Products The professionals – examples of B2B products done right The artists – examples of B2C products done right The pioneers – examples of blue ocean products The rebels – examples of red ocean products The GOATs – examples of differentiated disruptive and dominant strategy products The dominant strategy The disruptive strategy The differentiated strategy Summary References AI Transformation and Its Impact on Product Management Money and value – how AI could revolutionize our economic systems Examples and use cases Limitations and uneven adoption Product perspective Sickness and health – the benefits of AI and nanotech across healthcare Examples and use cases Product perspective Goods and services – growth in commercial applications Examples and use cases Product perspective Government and autonomy – how AI will shape our borders and freedom Basic needs – AI for Good Summary Additional resources References Building an AI-Native Product Understanding the AI-Native Product Stages of AI product development Phase 1 – Ideation Phase 2 – Data management Phase 3 – Research and development Phase 4 – Deployment AI/ML product dream team AI PM AI/ML/data strategists/architects Data engineer Data analyst Data scientist ML engineer Frontend/backend/full stack engineer QA/testing engineer UX designer/researcher Customer success specialist Marketing/sales/go-to-market team Investing in your tech stack Productizing AI-powered outputs – how AI product management is different AI customization Selling AI – product management as a higher octave of sales Case study AI product development cycle Team breakdown Tech stack AI outputs Summary GTM strategy and verticalization References Productizing the ML Service Basics of productizing AI versus traditional software product management How are the products different? Scalability Profit margins Uncertainty How are the products similar? Agile development Data How does the role of an AI PM compare with a traditional PM? B2B versus B2C – productizing business models Domain knowledge for B2B products – understanding the needs of your market Experimentation with B2C products – discover the needs of your collective Using AIOps/MLOps Consistency and AIOps/MLOps – reliance and trust Performance evaluation – testing, retraining, and hyperparameter tuning Feedback loop – relationship building Case study Summary References Customization for Verticals, Customers, and Peer Groups Domains – orienting AI toward specific areas Understanding your market Understanding how your product design will serve your market Building your AI product strategy Verticals – examination of some key domains Fintech Chatbots and virtual assistants Fraud detection Algorithmic trading and predictive analytics Healthcare Imaging and diagnosis Drug discovery and research Marketing – segmentation Manufacturing – predictive management Education – personalized learning Cybersecurity – anomaly detection and user and entity behavior analytics Thought leadership – learning from peer groups Case Study The market Product design and strategy Thought leadership Summary References Product Design for the AI-Native Product Product design elements 101 Understanding the end user Defining the problem Experimentation Validation Iteration Aesthetics Documentation What makes the AI-native product design process special? User obsession Machine learning Explainability Choosing your priorities wisely Ensuring clarity Adding complexity Branding What’s the story you’re telling? Set the stage Characters Progression Knowledge Call to action Case study Summary References Benchmarking Performance, Growth Hacking, and Cost Value metrics – a guide to north star metrics, KPIs and OKRs North star metrics KPIs and other metrics OKRs and product strategy Hacking – product-led growth The tech stack – early signals Customer data platforms (CDPs) Customer engagement platforms (CEPs) Product analytics tools A/B testing tools Data warehouses Business Intelligence (BI) tools Growth-hacking tools Managing costs and pricing – AI is expensive Case study North star metrics KPIs OKRs Growth hacking Summary References Managing the AI-Native Product The head – Managing alignment Vision Good vision statements Bad vision statements Communication The heart – Managing people and values Safety Empowerment The guts – Managing the rest Case study Summary References Integrating AI into Existing Traditional Software Products The Rising Tide of AI Evolve or die – when change is the only constant Changes in the Fourth Industrial Revolution Cultural and structural changes Working with an AI consultant Working with a third party The first hire The first AI team Fear is not the answer – there is more to gain than lose (or spend) Anticipating potential risks How LLMs are evolving and the rise of open source LLM capabilities Case study Implementation Risks Summary References Markers of success Join us on Discord Trends and Insights Across Industry Highest growth areas for AI integration Applied/embedded AI – applied and integrated use cases Ethical AI – responsibility and privacy GenAI – immersive applications Autonomous AI development – TuringBots Low-hanging fruit – quickest wins for AI enablement Riding the GenAI wave Summary References Evolving Products into AI Products Ideation – what’s possible, what’s desirable, and what’s probable List 1 – value List 2 – scope List 3 – reach Case study Value Scope Reach Data management – the bloodstream of the company Preparation and research Ensuring quality partnerships Benchmarking and defining success Competition – love your enemies Product strategy – building a blueprint that works for everyone Product vision Product strategy Product goals The product roadmap Red flags and green flags – what to look for and watch out for Red flags Green flags Summary Additional resources The Role of AI Product Design The evolution of product design Ideation: Managing expectations Data management: Strategizing and integrity R&D: Mapping the user experience journey Deployment: Are you ready to scale? Expansion: What makes the evolved AI product special? Decisions and insights Automation and adaptability Personalization and learning Choosing your words carefully Product language fit Accessibility and inclusivity Building with trust and security Bias Accountability and explainability Security Case study Integrating AI into ProjectABZ: A project management tool created by ABCDZCo Ideation and research Design and development Marketing and communication Summary References Managing the Evolving AI Product The head – managing alignment Strategic alignment Feedback loops The heart – managing the people and values The guts – managing data, infrastructure, and ongoing maintenance Infrastructure and data Maintenance Case study AI transformation for ProjectABZ Management alignment People alignment Operational alignment Results and outcomes Summary Managing the AI PM Career Starting a Career as an AI PM Bolstering your knowledge in theory and practice Theory Practice What an AI PM looks like today The importance of communities Choosing your AI PM specialization Case study Summary References What Does It Mean to Be a Good AI PM? A job family of many hats Technical proficiency Technologist AI expert Technical translator Data steward Data strategist Quality controller Analyst Business acumen Strategist Revenue driver Partnership builder Innovator Market researcher Competitor analyst Communication Project manager Change agent Stakeholder manager Educator Risk assessor Leadership Visionary Ethicist Team leader Storyteller Motivator Knowledge sharer Problem solving Customer advocate Regulatory complier Facilitator Data-driven decision maker Adaptability manager Conflict resolver The AI whisperer and the role of communicating accessibly Common challenges and opportunities as you’re leveling up in your career The importance of self-care Case study Summary Maturing and Growing as an AI PM Projecting – what’s your ideal AI PM roadmap? Level 1 – building a foundation Level 2 – strategic growth Level 3 – specializing and leading Level 4 – a light for others Learning – staying informed and inspired Thought leadership Certifications and degrees Professional development Networking – deepening your involvement with the professional community Growing – the student becomes the teacher Embracing challenges Reflecting Establishing a feedback loop What’s next? The world is our oyster Case study Projecting Learning Networking Growing What’s next? Summary Other Books You May Enjoy Index