The AI Product Playbook

Index

• A
• A/B test
  • AI-experiences PM, 126
  • baseline metrics, 231
  • conducting, 248
  • designing, 119
  • explained, 251–259
  • MLOps, 267, 274
• accessibility tools, 68
• accountability, 219, 292
• accuracy
  • classification problem, 53
  • confusion matrix, 234
  • factual, 77
• action (A), 64, 114
• activation function, 95–96
• active learning, 83
• actual value, 232–233
• adaptability, 43, 123, 148, 305–306
• adversarial debiasing, 290
• age, 287
• agent, 63
• agentic AI, 45
• AGI (artificial general intelligence), 45
• agreement rate, 38
• AI (Artificial Intelligence)
  • applications, 238–239
  • capability alignment, 224–225
  • capability matching, 194–202
  • ethical considerations, 281–299
  • feature storming, 222
  • first mindset, 226
  • foundational concepts, 4–16
  • human expertise and, 34–38
  • life cycle, 101–110
  • Machine Learning compared to, 41–44
  • metrics, 29–34
  • portfolio, 304–305
  • powered feature reverse engineering, 225
  • Product Managers, 112
  • scenario planning, 223
  • solutions, 261–279
  • tools, 172–173
  • types of, 45
• AI feature storming, 222
• AI scenario planning, 223
• AI-builder PM
  • challenges, 152–154
  • compared with AI-experiences PM and AI-enhanced PM, 177–183
  • day in the life, 149–152
  • day-to-day activities, 141–144
  • introduction to, 112
  • key responsibilities, 138–141, 157
  • proficiency levels, 144–149
  • relationship to other roles, 154–156
• AI-enhanced PM
  • challenges, 173–175
  • compared with AI-builder PM and AI-experiences PM, 177–183
  • day in the life, 169–172
  • day-to-day activities, 162–165
  • introduction to, 112
  • key responsibilities, 160–162, 184