Building AI-Powered Products (AI column)
I read something interesting. book Marily Nika, AI Product Lead (Google, ex‑Meta) Founder of the AI Product Academy. Marily talks about what a separate discipline of AI product management looks like and how to determine what exactly we build, how we measure the quality of a product, how much it will cost in production and why users will not leave in a week. This book came out in February. 2025 I tried to give out a playbook that connects the idea → product value → architecture → operation → metrics → risks. It addressed issues of type How to Choose AI Scenarios That Bring Value, Not Because It's Fashionable How to Think About Measuring Success: Offline Metrics, Human Eval, Monitoring in Product How to evaluate trade-offs: quality vs latency vs cost of inferencing vs risk How to build the work of product managers, engineering, data science and research, so that expectations coincide How to deal with ethics and compliance: data, privacy, hallucinations, responsibility for models
The content of the book is preface. 8 chapters and appendix.
Chapter 1. The Role of AI Product Managers Here, the author talks about the role of AI products managers and how the evolution of AI has evolved: traditional AI → GenAI → the future AGI. What are the special features of AI products, including probabilistic nature, data dependency, drift models, interpretability/explainability requirements, automated solutions, scalability and the impact of this on UX? It also describes what set of skills is required for AI products managers
Chapter 2. The AI Product Development Lifecycle Here the author tells about the frame AI Product Development Lifecycle (AIPDL) and performs in stages: Types of AI products: 0‑to‑1 (product) vs 1‑to‑n (AI features in the existing) (reminds me of historyZero to One" Peter Thiel.)
- Stage Ideation. (Brainstorming and Prioritization through RICE Opportunity: product-market fit + business-viability (ROI/monetization/risk/regulatory)feasibility and desirability, Concept & Prototype: Hardcode prototype, integration verification, domain expertise, value from day one
- Testing & Analysis and Rollout
Chapter 3. Essential AI PM Knowledge In this chapter, the author talks about the basic practices of product management, the classic build vs buy evaluation, leadership and communication, communication with the engineering team, basic technical concepts and so on. So basically, product management. 101.
Chapter 4. The AI PMs Day‑to‑Day Here. AI PM: execution → AI/ML PM → strategic leadership. She goes on to give examples of people who have worked in these roles in different companies. Here is a story about building cross-functional interactions in teams.
Chapter 5. Strategic Thinking in AI Here. Talk about strategy and the “right questions” before you write code.
- When AI may not be the answer, Disruptive vs sustaining and how not to lose the innovation’s dilemma AI strategy build vs buy (solution matrix + hybrid approaches), Data strategy: synthetic vs real-world data Selection of approach: fine-tuning vs RAG vs grounding + decision framework Product reviews as a tool for obtaining buy-in from management.
Chapter 6. Setting Goals and Measuring Success Here. Measurability: product health, system health, and proxy metrics. How to assemble OKR.
Chapter 7. AI Tools for Product Managers Review the classes of AIPDL support tools and collaboration tools.
Chapter 8. Building AI Agents Chapter about agent products, what it is and how to "construct" the agent for the product: vertical vs general-purpose, activation, autonomy, feedback/learning.
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