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The Future of PMs:
Everyone Will Build AI

Marily Nika
Product Lead, Meta (AI & Product Educator)
APR 2024
The Shift

Every PM Will Be an AI PM

PMMLExpertAI-Enhanced Products
"Every product manager will need to get comfortable with having a partner that's a research scientist. This is not ChatGPT, it's actual PhD researchers building models into your product."
  • The future has AI baked into every product by default
  • Personalization, recommendations, automation require ML expertise
  • PMs must learn to work with research scientists, not fear them
  • The mindset shift: embrace uncertainty in research timelines
Don't Do AI

The Shiny Object Trap

IDENTIFY REAL PROBLEM THEN REACH FOR AI ⚠ NOT FOR MVPs ⚠Validate with prototypes first
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ROI from shiny object trap
  • Problem-first approach: Is there pain point? Real user need?
  • No MVP with models: Don't waste data scientists' time training on an unvalidated idea
  • Use existing data: Leverage adjacent product data or synthetic data first
  • Define success clearly: What does "solving the problem" actually look like?
Marily's definition "The generalist PM helps their team build the right product. The AI PM helps their team solve the right problem."
ChatGPT as Your Assistant

AI Tools PMs Use Today

  • Mission Statements: Feed ChatGPT your draft; it makes them universally understood by execs, juniors, everyone
  • User Segments: Ask it for personas it'd never occur to you to imagine, complete with motivations & pain points
  • Feature Ideation: Generates novel AI-enhanced feature ideas for your existing product
  • Boilerplate Copy: Frees you from tedious writing to focus on strategy
The prompt approach

"Rewrite this mission statement to be understood by anyone, from kids to C-suite." Start with your thinking, let AI polish it.

Not replacement

These tools amplify your judgment. You're still the PM. You direct the work. Tools make you faster and smarter.

Playbook

Getting Started: PM in AI

  • Learn the fundamentals: take a coding course (Coursera, Stanford intro to AI)
  • Shadow ML researchers on your team 1 hour/week to understand what they do
  • Start with data you already have: what existing product data can you leverage?
  • Think problem-first: "What user pain point could AI solve?" not "How do I use AI?"
  • Pair with a data scientist to scope a small proof-of-concept, not an MVP
Learn to code Career Foundry, General Assembly, Coding Dojo offer bootcamp-style coding education. Even a few weeks gives you confidence to talk to ML experts.
Contrarian

AI PM Myths Vs Reality

✗ ChatGPT will replace PMs INSTEAD → ✓ It frees PMs to do strategic work. Tools amplify, not replace, human judgment.
✗ AI is only for big tech INSTEAD → ✓ Any PM with user data can identify an AI opportunity: fraud, personalization, automation, recommendations.
✗ You need massive datasets to start INSTEAD → ✓ Simple models work with hundreds of examples. Start small, prove the concept, scale the data.
✗ You need to code to be an AI PM INSTEAD → ✓ Learning fundamentals builds confidence. You'll never train models, but you'll speak the language.
Based on Marily Nika's episode on Lenny's Podcast. All ideas on this page are from the episode.Watch on YouTube
GO DEEPER IN THE EPISODE
03:20 How Marily stays informed about the latest developments in AI04:46 What is overhyped and underhyped in AI right now05:59 How Marily uses ChatGPT for work08:25 Why product managers will be AI product managers in the future11:16 How to get started using AI14:12 When not to use AI15:47 How much data do you need for AI to work properly?17:01 When should companies develop their own AI tools?18:35 What an AI model is and how it is trained21:25 How Google demonstrated the ability of AI to translate a conversation in real time23:02 Why AI will not replace PMs23:48 A case for learning to code26:21 Where to learn to code27:40 How to become a strong AI PM29:25 Challenges that AI PMs face31:16 Getting leadership on board with investing in AI33:10 How PMs will work with data scientists and AI35:29 Marily’s AI course39:12 AutoML and how a renewable-energy company used it to improve its turbine maintenance procedure40:31 How Marily built her course and the modifications she has made42:53 Why you should create your own course44:08 Lightning round
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