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Building AI-Powered Products: A Guide for Product Managers

Starweaver via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

2 weeks, 3 hours a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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Learn AI-Powered Products for Product Managers and master how to scope, build, and launch successful AI features with confidence. This intermediate course teaches you to identify AI-worthy opportunities, assess data readiness, and choose the right solution pattern—prompt-only, RAG, fine-tuning, or agents. Ideal for experienced PMs, technical PMs moving into AI/ML, and founders building AI-first products, it replaces AI hype with practical frameworks, hands-on labs, role-play activities, and a capstone project resulting in a complete AI Product Brief. Gain in-demand skills in AI feature scoping, metrics interpretation, trustworthy AI UX, and governance readiness. You'll learn to translate precision, recall, and F1 scores into product decisions, define launch-readiness criteria, and design for trust through transparency and human oversight—while covering compliance essentials like the EU AI Act and NIST AI RMF. Taught by Karlis Zars, this course delivers reusable PRD templates, opportunity assessment tools, and launch scorecards to help you ship AI features that create real business and user value.

Syllabus

AI Product Thinking, Scoping, and Solution Design
- This module helps learners shift from traditional product thinking to AI product thinking. It introduces how AI-powered products differ from conventional software, how to identify strong AI opportunities, and how to validate whether a problem is actually worth solving with AI. Learners then move into scoping AI features, assessing data readiness, defining inputs and outputs, and choosing the right solution pattern such as prompt-only, retrieval-augmented generation, fine-tuning, or agents.
Measuring, Designing Trust, and Shipping AI Products Responsibly
- This module focuses on what happens once an AI feature moves toward launch. Learners explore how to measure AI performance in product terms, interpret ML metrics such as precision, recall, accuracy, and F1, and define practical thresholds for when a model is good enough to ship. The module then shifts into trust and risk, including UX for non-deterministic outputs, transparency design, human-in-the-loop patterns, bias, fairness, privacy, and governance. Finally, learners examine how to launch, monitor, and scale AI products through phased rollouts, shadow testing, post-launch monitoring, cost awareness, and roadmap planning.