What It Actually Takes to Be a Great AI PM in 2026
Join Teja Bandaru (Product Leader · PayPal) for a free live session.
Teja Bandaru
Product Leader · PayPal
⭐ 4.75 / 5
The PM role hasn't disappeared. It's been rewritten.
AI hasn't eliminated the PM job — it's raised the bar for what a good PM looks like. Companies hiring AI PMs now expect you to prototype independently, write specs that AI systems can execute, and define quality before a single line of code is written. This 5-week course builds the full toolkit for the modern AI PM: how to think about AI products, how to spec them, how to evaluate them, and how to ship them with confidence.
What You'll Learn
Independent Prototyping Workflow
Build and validate a working AI prototype using Claude Code or v0 without engineering support — producing a tested proof-of-concept that replaces weeks of back-and-forth spec cycles.
AI Product Specification
Write specs for AI features that engineering can actually build from — covering system behavior, edge cases, failure modes, and decision rules as clear, testable instructions.
Pre-Implementation Eval Suite
Define what good and bad outputs look like before implementation begins — producing a scored eval suite that makes ship/hold decisions defensible and catches regressions early.
AI Product Roadmap
Prioritize an AI feature backlog using a framework that accounts for model reliability, eval coverage, and failure mode risk — not just user value and engineering effort.
Who Is This For?
This course is designed for:
PMs Transitioning into AI Roles
Who are moving onto AI teams or into AI PM positions and need the full toolkit — how to think about AI products, spec them correctly, and evaluate them before shipping.
PMs Already Shipping AI Features
Who are working on AI products but still applying traditional PM frameworks — and want to operate at the level the role now demands.
Senior PMs Setting the AI Standard
Who need to define what good looks like for their team: what an AI product spec should contain, what eval coverage means, and what 'done' is for a non-deterministic system.
Course Outline
5 weeks · 3 sessions per week
Projects You'll Ship
Leave with real work to show, not just a certificate.
AI Prototype + Validation Brief
A working AI prototype built independently using Claude Code or v0 — with a validation brief documenting what hypothesis it tested, what it confirmed, and what the production version needs to handle that the prototype didn't. The artifact that replaces 'waiting for engineering to validate the concept.'
AI Product Specification
A complete AI product spec for a real feature: system behavior, decision rules, failure modes, edge cases, and quality criteria — written as clear, testable instructions. Portfolio-ready and immediately usable as the engineering handoff for any AI feature you ship.
AI PM Portfolio
Your full AI PM toolkit: pre-implementation eval suite (10/10 examples + quality gate spec), 3-month product roadmap with quality milestones, and a documented ship/hold decision framework. Presented live to the cohort — the portfolio piece that answers what every AI PM interview will ask.
Your Instructors
Teja Bandaru
Product Leader · PayPal
⭐ 4.75 / 5
Teja leads Global Consumer Service Experience at PayPal. Previously at Amazon, he led merchant support for Buy with Prime and drove GenAI adoption initiatives across Customer Support, Marketing, and Sales. He brings hands-on experience shipping AI products at scale — and teaches PMs how to move from traditional product thinking to building with AI.
What Students Say
⭐⭐⭐⭐⭐
"I moved from a traditional PM role to an AI PM role six weeks after this sprint. The portfolio artifacts answered every interview question — the spec, the evals, the prototype. Three offers in a month."
Alex Torres
Senior PM · Databricks
⭐⭐⭐⭐⭐
"The evals-first week changed how my entire team ships AI features. We now write the eval spec before engineering starts. We've caught three regressions in CI/CD that would have been user-facing incidents."
Riley Kim
Staff PM · Stripe
⭐⭐⭐⭐⭐
"The AI spec template is now our team standard. Engineering stopped asking 'what should it do when X?' because the spec already answers it. Review cycles dropped by half."
Morgan Walsh
Product Lead · Rippling
Course Schedule
All sessions are instructor-led and live. Recordings available within 24 hours.
SUNDAY
9:00 AM PDT
Live BuildBuild alongside the instructor. Every session ends with a working artifact — a prototype, a product spec, or an eval suite — produced in the room.
WEDNESDAY
6:00 PM PDT
Critique SessionStructured peer and instructor critique on your weekly deliverable. Bring your real work — the harder the feedback, the better the portfolio piece.
THURSDAY
6:00 PM PDT
Build & ShipFinish and test your weekly deliverable with peer review and instructor feedback before you submit.
Frequently Asked Questions
Related reading
The JD-backed research behind this course — from Dexity Intel.
How to Become an AI Product Manager in 2026 →How far do you want to go?
Start free to experience our offering, choose the program length you would want to commit to.
You build. Nobody demos at you.
Every session is follow-along — you build the thing yourself while a practitioner works beside you. That is why the hours look long: they are yours to build in, with an expert on hand to guide you. None of it is a traditional lecture.
In the session, you'll: