Training Session · Product Strategy & AI

AI Product Management

Intensive

AI changes every part of the product process — from discovery to delivery. This intensive gives PMs the frameworks to lead AI-powered products with confidence, not guesswork.

Audience Product Managers and Product Leaders
Format Intensive Workshop
Session Overview

What This Session Covers

Four areas that separate PMs who ship AI features from PMs who lead AI products.

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AI Product Thinking
How AI products differ from traditional software — probabilistic outputs, feedback loops, data dependencies, and why specs alone don't cut it.
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Working with ML Teams
Translating product requirements into ML problem statements — how to collaborate effectively with data scientists and engineers without writing a line of code.
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Metrics & Measurement
What success looks like for AI features — why accuracy alone misses the point and how to design evaluation frameworks stakeholders trust.
⬡
Responsible Deployment
Bias, hallucinations, and failure modes — building guardrails and escalation paths into AI product decisions from day one.
Session Agenda

The Four Blocks

Each block builds a distinct layer of PM capability for AI products — from mental models to hands-on application.

Block 01

What Makes AI Products Different

Probabilistic vs deterministic systems — how AI outputs behave and what that means for product design
The data flywheel — why AI products compound over time and how to design for it
Common failure modes: model drift, feedback loops, distributional shift
Block 02

Scoping & Prioritizing AI Features

Framing product problems as ML problems — turning a user need into a data problem statement
The build-vs-buy decision: when to use foundation models, fine-tune, or train from scratch
Prioritization frameworks for AI features — when the technology is uncertain, how do you stack rank?
Exercise Map a product problem to an ML framing — define inputs, outputs, success criteria, and failure modes for a feature you'd want to build
Block 03

Metrics, Evaluation & Trust

Designing evaluation frameworks: offline metrics, online metrics, and the gap between them
A/B testing AI features — why standard experimentation frameworks break down and what to do instead
Communicating model performance to stakeholders who don't speak statistics
Key Insight Accuracy is not a product metric. The right question is: does the model's output drive the user behavior you want?
Block 04

Shipping Responsibly

Identifying bias and fairness concerns before they reach production
Designing for graceful failure — what happens when the model is wrong and the user notices
Post-launch monitoring: what to instrument, what to watch, and when to pull the plug
Exercise Perform a pre-mortem on an AI feature — list every way it could fail in production and design a mitigation for each
Key Takeaways

What You Leave With

Three frameworks PMs can apply the day after this session.

Frameworks

What You Leave With

Three frameworks PMs can apply the day after this session:

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The AI Scoping Canvas
A structured template for translating any product problem into an ML problem statement with defined inputs, outputs, and success criteria.
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The Evaluation Rubric
A layered metrics framework that connects model-level performance to user-level outcomes and business impact.
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The Failure Mode Map
A pre-mortem template for identifying, categorizing, and mitigating AI-specific failure modes before they reach production.
What You'll Leave With

Skills Covered

AI Product Strategy ML Problem Framing Evaluation Design Probabilistic Thinking Feature Prioritization A/B Testing Stakeholder Communication Responsible AI Model Monitoring Data Flywheel Design Failure Mode Analysis

Bring this session to your product team.

Available as a half-day or full-day workshop. Get in touch to discuss your team's needs.

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