AI Product Management: Defining the Role of the Product Manager in the Age of AI

Author Kim Young-wook · Publisher Hanbit Media · Publication Date 2026-08-10 · ISBN 9791175790926

About the Book

Redefining the Role and Responsibilities of PMs in the AI Era

“How much should I understand AI, and how much responsibility should I take?” This is a question every PM in charge of AI products has likely faced. You don't need to be an engineer, but you must have an intuition for how the system works and why it fails. This book provides clear judgment criteria for that boundary. Starting from how deterministic software fundamentally differs from probabilistic AI systems, it addresses how to handle failure modes that traditional PMs have never experienced—such as hallucinations, vulnerabilities, and cost explosions—from the design stage, and how these judgments necessitate changes in PRDs, metrics, and team collaboration methods.

In 2024 alone, over 12,000 people globally transitioned into ‘AI PM’ roles, and two-thirds of corporate leaders stated that AI capabilities would be a key criterion for new hires. At a time when the PM role itself is being redefined, this book offers practical criteria to PMs suddenly tasked with LLM-based services who are unsure where to start, and an opportunity for experienced PMs to re-evaluate their familiar judgment criteria in the face of AI.

Target Audience

PMs who are suddenly in charge of LLM/AI agent-based products and are unsure where to begin
Senior PMs looking to expand their existing PM experience into AI-era product strategy and operational methods
Machine learning engineers and data scientists who want to collaborate with PMs using more precise language and understand the overall product perspective
Leaders who view the technology, user experience, and business of AI products as a single system


From the Book

“If past PMs were people who decided ‘what features to build,’ PMs in the AI era must be responsible for ‘how the system works, how it fails, and how to design for those errors.’”
--- p.20

“You don’t need to be an engineer. But you must have an intuition for how an AI system works.”
--- p.21

“It’s easy to be overwhelmed by the dazzle of technology itself. But what we must create is not technology, but the moment technology interacts with people.”
--- p.24

“No matter how powerful AI is, it is ultimately our PMs’ job to make it a product for people.”
--- p.374

Table of Contents

[PART 1 A PM's Eye for Understanding AI]

CHAPTER 01 How AI Differs from Traditional Software
_1.1 Determinism vs. Probabilism
_1.2 How Machine Learning Learns
_1.3 Overfitting and Underfitting
_1.4 Three Types of Machine Learning
_1.5 Intuition Every PM Must Have

CHAPTER 02 How LLMs Work
_2.1 Changing Language into Numbers - What are Tokens?
_2.2 The Birth of Prediction Machines - Training to Predict the Next Word
_2.3 The Magic of Scale - When Size Changes, the Type Changes
_2.4 But Why Is It So Good? - Fine-tuning and RLHF
_2.5 How to Remember Context - Context Window
_2.6 What is Inference? - Generation Mechanism
_2.7 Two Ways to Expand LLM Limitations - RAG and Agents

CHAPTER 03 Failure Modes of AI Products
_3.1 Hallucinations
_3.2 Vulnerabilities
_3.3 Cost Explosions
_3.4 Why PMs Must Understand Failure Modes

[PART 2 Designing AI Products]

CHAPTER 04 AI Features are Systems, Not Prompts
_4.1 The Trap of One-off Prompts
_4.2 Designing AI Features as Systems
_4.3 System Prompt Strategy
_4.4 Harness Engineering
_4.5 Practical AI Feature Design
_4.6 Four Stages of AI Product Development and System Design
_4.7 Overlooked Layer in System Design: Bias and Ethics
_4.8 A PM's Intuition for System Design
_4.9 Hokusai's System, a PM's System

CHAPTER 05 PRD for AI Products
_5.1 Traditional PRDs vs. AI PRDs
_5.2 Essential Elements of an AI PRD
_5.3 Eval Plan
_5.4 Pricing Policy
_5.5 What Hamlet Left Us

CHAPTER 06 Designing AI User Experience
_6.1 Why Does AI Make Mistakes?
_6.2 Handling Uncertainty with UX
_6.3 Trust Design
_6.4 Responding to Failure Scenarios
_6.5 What PMs Should Design

[PART 3 Teams and Processes for Building AI Products]

CHAPTER 07 The PM's Role in an AI Team
_7.1 How AI Teams Differ from Traditional Development Teams
_7.2 What PMs Must Decide
_7.3 What PMs Must Delegate
_7.4 How to Effectively Collaborate with ML Engineers and Data Scientists
_7.5 Becoming a PM on an AI Team

CHAPTER 08 Measurement and Metrics for AI Products
_8.1 What's Wrong with Existing KPIs?
_8.2 How to Rewrite Existing Metrics in an AI Context
_8.3 AI-Specific Metrics - What's Not on the Existing Dashboard
_8.4 Integrating Evals into the Product Process
_8.5 How to Build Metrics as a System
_8.6 Metrics are Mirrors, but AI's Mirror is Distorted

CHAPTER 09 AI in Production
_9.1 The Reality of 'AI in Production'
_9.2 Monitoring
_9.3 Responding to Drift and Quality Degradation
_9.4 Post-Deployment is Where a PM's Real Work Begins

[PART 4 Growing as an AI PM]

CHAPTER 10 Accelerating PM Work with AI Tools
_10.1 Writing PRDs with AI
_10.2 Automating Research
_10.3 Workflows that Transform PM Productivity

CHAPTER 11 Building Actual AI Products
_11.1 Why You Should Build It Yourself
_11.2 What to Build
_11.3 Planning - How to Refine AI Product Ideas
_11.4 Building - How to Quickly Launch a Prototype
_11.5 Deployment - Why Release Even if It's Not Perfect
_11.6 What PMs Gain After Building
_11.7 Start Now

CHAPTER 12 Designing an AI PM Career
_12.1 Does the Role of AI PM Actually Exist?
_12.2 Competency Model for AI PMs
_12.3 Portfolio Strategy
_12.4 How to Prepare for the Next Five Years