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Create your first working AI feature, and learn when AI is not the right solution.

Start by seeing an AI product as a complete system rather than a single model call, and by asking whether the problem suits AI at all. You will specify testable behavior, build one bounded working feature with low- and no-code tools, and handle voice as a serious product surface. The course ends with a working prototype, an honest account of its limits, and a demonstration.

Give a system real knowledge, real tools, and real limits

Extend your feature into a system that draws on approved information and takes action through connected tools — the pattern behind most enterprise AI and back-office automation today. You will decide how much independence the system should have, then write the operating agreement that governs it: what it may decide alone, how its work is verified, when it escalates, and who is accountable when it is wrong.

Prove it works with evidence, not instinct.

AI systems cannot be tested once, because the same input can produce a different answer. You will define what quality means for a specific product and decision, build representative test data, combine human and automated scoring, read traces to find root causes, and deliberately attack the system to find failures before users do. The course ends in a defended recommendation: ship, pilot, revise, or stop.

Run it after launch, where most of the PM’s job actually happens

A system that passed evaluation still has to survive contact with real use. You will instrument it, model what it costs to deliver a successful outcome, release it in stages, detect the silent quality decline that AI products are prone to, rehearse an incident, and ship a real improvement. This is the part of product management that almost no curriculum covers.

We follow the semester schedule as outlined in ÃÛÌÒÊÓƵ’s official Academic Calendar. The next semester of this program will begin in January 2027. Specific class times for this certificate will be announced shortly before the start date.

The program is intended for practicing and aspiring product managers, technical professionals moving into product roles, founders, consultants and analysts. It is especially relevant for professionals seeking AI education for product management or looking to strengthen their AI product strategy skills.

No advanced programming experience or computer science degree is required. You should be comfortable working with data and basic technical concepts, such as spreadsheet formulas, simple scripts and APIs. The program takes a low- and no-code-first approach, with supported pathways and starter materials for assignments.

You’ll learn to identify appropriate uses for AI, develop working AI features and agents, connect models to approved data and tools, evaluate system performance, and establish limits and accountability. You’ll also explore the cost, reliability and governance considerations involved in operating AI products after launch.

Yes. A limited number of partial scholarships still remain for admitted AI Product Management Essentials students. Apply by the final deadline to be considered for one of these awards.

Carnegie Mellon alumni are eligible for a scholarship to the Graduate Certificate in Foundational AI & ML for Business worth up to 20% of tuition.