AI for Product Managers
Decide what AI should build, and how you will know it worked.
Use AI to sharpen discovery and decisions, write briefs that teams and coding agents can build from, and ship AI features with clear outcomes, guardrails and evidence.
AI TEACHING PERSPECTIVES · Meet the team
AI shortens the distance between an idea and working software. That makes the product manager’s decisions — which problem, for whom, what is out of scope, what counts as success — more valuable, not less.
This course shows you how to use AI in your own product work without inventing evidence, how to write briefs precise enough for engineers and AI coding agents, and how to scope, measure and launch features that are themselves powered by AI.
Use the controls inside the presentation to start. Scratch answers stay only while this page is open. This activity does not automatically complete a lesson or pass an assessment. If media stops after a long break, reload this page.
One step closer.
With every module.
6 modules · 0 published lessons
Open a module to explore its lessons.
01LEARNING MODULE · ≈3 hoursThe PM’s role when AI writes the code0 lessons · Week 1
See how AI-assisted delivery changes where product decisions matter, and which decisions must stay with you.
Objectives
- Explain why ambiguity now costs more, not less, when code is cheap to produce.
- Identify the decisions a product manager owns in an AI-assisted team.
- Recognise good and poor uses of AI in everyday product work.
Topics
- Speed versus direction: what agents accelerate and what they cannot decide
- The intent gap seen from the product side
- Decisions PMs own: problem, scope, priority, success and acceptable risk
- Working with engineers who delegate to coding agents
- Where AI helps a PM — and where it quietly replaces judgment
Concepts
Intent gap
The distance between what a stakeholder meant and what the ticket actually says. An AI agent closes it by guessing, confidently.
Decision owner
The person accountable for a product decision. If nobody is named, the implementer — human or agent — decides by default.
Rework loop
The cycle of building, discovering the request was misread and building again. Fast generation makes the loop faster, not shorter.
Exercise
Take a real ticket from your backlog and list every decision it silently leaves to the implementer.
Deliverable
A decision audit naming an owner for each open decision.
Lesson material is being prepared for this module.
02LEARNING MODULE · ≈4 hoursDiscovery and research with AI0 lessons · Week 1
Use AI to synthesise interviews, feedback and market signals while keeping every insight traceable to real evidence.
Objectives
- Cluster and summarise qualitative research with quotes as evidence.
- Separate what customers said from what the model inferred.
- Ask AI for disconfirming evidence, not only for confirmation.
Topics
- Synthesising interview notes, support tickets and survey answers
- Quote-backed insights and source references
- Invented personas, fabricated statistics and other hallucination risks
- Competitor scans and their limits
- Privacy and data minimisation when sharing customer data with a model
Concepts
Evidence trail
The link from an insight back to the quotes, tickets or data points that support it.
Synthetic insight
A plausible-sounding finding produced by a model with no source behind it. Useful as a hypothesis, never as evidence.
Data minimisation
Sharing only the customer data a task needs, and removing personal details before it reaches a model.
Exercise
Synthesise twenty feedback items about course registration into insights, each backed by quotes.
Deliverable
An insight map with source references and a list of claims you rejected as unsupported.
Lesson material is being prepared for this module.
03LEARNING MODULE · ≈4 hoursProblem briefs teams and agents can build from0 lessons · Week 2
Turn a solution-shaped request into a problem brief with outcomes, non-goals and acceptance criteria that engineers and AI agents can test.
Objectives
- Write a problem statement that does not prescribe a solution.
- Define observable outcomes and success measures.
- Write non-goals and acceptance criteria that can be verified.
Topics
- From ticket to problem brief
- Users, jobs, observable outcomes and success measures
- Non-goals, constraints and dependencies
- Acceptance criteria in given–when–then form
- Handing a brief to a spec-driven engineering team
Concepts
Problem brief
A short document describing who has the problem, what changes for them when it is solved and how success will be measured.
Non-goal
Something deliberately out of scope, written down so that neither a reviewer nor an agent quietly adds it back.
Acceptance criterion
A single, observable, pass-or-fail statement. If two reasonable people could disagree on whether it passed, it is not yet one.
Exercise
Rewrite the ticket “We need a Stripe checkout button on the registration page” as a problem brief.
Deliverable
A one-page brief with outcomes, non-goals and five testable acceptance criteria.
Lesson material is being prepared for this module.
04LEARNING MODULE · ≈4 hoursScoping AI-powered features0 lessons · Week 2
Decide when a feature needs a model at all, and specify how it should behave when the model is wrong, slow or expensive.
Objectives
- Decide whether a feature needs AI or a simpler rule.
- Specify an acceptable error level and fallback behaviour.
- Estimate cost, latency and operational trade-offs.
Topics
- Deterministic versus probabilistic features
- Quality bars and acceptable error
- Fallbacks and handover to a human
- Cost per interaction, latency and rate limits
- Build, buy or call an API
Concepts
Quality bar
The level of correctness a feature must reach before launch, stated as examples and measures rather than adjectives.
Fallback path
What the product does when the model fails, refuses or is unavailable.
Unit economics
The cost of one AI interaction compared with the value it creates.
Exercise
Scope a “course advisor” assistant that helps visitors choose a course.
Deliverable
A feature scope with quality bar, fallback rules, cost estimate and the cases where it must hand over to a person.
Lesson material is being prepared for this module.
05LEARNING MODULE · ≈4 hoursMeasuring AI features0 lessons · Week 3
Define how the team will know an AI feature works, before launch and after it.
Objectives
- Define offline evaluation criteria together with engineering and QA.
- Choose outcome, quality and guardrail metrics.
- Plan a staged rollout with clear stop conditions.
Topics
- Evaluation sets as product requirements
- Outcome, quality and guardrail metrics
- Human ratings and their biases
- Experiments and staged rollouts
- When to stop, roll back or retrain
Concepts
Evaluation set
A fixed collection of realistic inputs with expected behaviour, run against every version of the feature.
Guardrail metric
A measure that must not get worse while you optimise the main outcome, such as complaint rate or refusal rate.
Staged rollout
Releasing to a growing share of users, with gates that decide whether to continue.
Exercise
Write an evaluation and launch plan for the course advisor.
Deliverable
Twenty evaluation cases with expected outcomes, a metric tree and rollout gates.
Lesson material is being prepared for this module.
06LEARNING MODULE · ≈4 hoursResponsible launch and stakeholder alignment0 lessons · Week 3
Prepare an AI feature for launch: risks, transparency, ownership and honest communication.
Objectives
- Run a lightweight risk review for an AI feature.
- Write clear user-facing transparency text.
- Communicate AI capability and limits honestly to stakeholders.
Topics
- Risk review: misuse, bias, privacy and safety
- Regulatory awareness, including the EU AI Act’s risk-based approach (orientation, not legal advice)
- Transparency and user control
- Setting expectations with leadership
- Who owns the feature after launch
Concepts
Risk register
A list of identified risks with likelihood, impact, owner and mitigation.
Transparency notice
Plain-language text telling users that AI is involved, what it does and where its limits are.
Accountable owner
The named person who decides when the feature is changed, paused or withdrawn.
Exercise
Prepare the launch review for the course advisor.
Deliverable
A risk register, a transparency notice and a one-page stakeholder update.
Lesson material is being prepared for this module.
The course,
in context.
Explore the approach, expectations and practical details. These are public course notes, separate from your learning modules.
PUBLIC COURSE GUIDEWho this is for
- Product managers and product owners working with teams that use AI coding tools.
- PMs responsible for features that use language models.
- Founders, delivery leads and designers who write requirements or briefs.
Before you begin
- Experience writing tickets, user stories or product briefs.
- No programming required.
- Access to a general-purpose AI assistant of your choice.
- Helpful but optional: a real backlog from your own work to practise on.
What you will learn
- Use AI in discovery without mistaking generated text for evidence.
- Write problem briefs with outcomes, non-goals and testable acceptance criteria.
- Decide when a feature needs AI and specify its fallback behaviour.
- Define evaluation sets, success metrics and guardrail metrics with your team.
- Plan a staged, responsible launch and communicate it honestly.
Applied assignments
Decision audit and insight map
Find the decisions a real ticket leaves open, and turn raw feedback into evidence-backed insights.
Problem brief
Outcomes, non-goals, constraints and acceptance criteria for the registration case.
AI feature scope and evaluation plan
Quality bar, fallbacks, costs, evaluation cases and rollout gates for an AI course advisor.
Your capstone project
Scope, measure and launch an AI course advisor
Capstone brief
Start from a one-line request from leadership: “Add an AI assistant that helps visitors pick a course.” Define the problem and outcomes, decide what the assistant must never do, specify fallbacks, write the evaluation set and success metrics, and prepare a launch review your engineering, QA and legal colleagues could act on.
Assessment
A 60-minute scenario review: improve a weak product brief, spot an AI feature with no fallback, and choose metrics and launch gates for a given case.
What you will produce
- Problem brief template
- AI feature scoping canvas
- Evaluation and metric plan
- AI launch review checklist
The method, explained
From features to decisions
AI coding agents can turn a vague ticket into working software in an afternoon. What they cannot do is decide whether that software solves the right problem. The product manager’s work moves upstream: fewer hand-offs of finished designs, more precise statements of intent.
Three habits this course builds
Decide before you delegate. Every ticket hides decisions about scope, edge cases and success. Make them explicit, name an owner, and write them down where engineers and agents can see them.
Evidence before enthusiasm. AI is an excellent research assistant and an unreliable witness. Use it to sort, cluster and question your evidence, and keep every insight linked to its source.
Measure what the model does, not what the demo shows. An AI feature is only as good as its evaluation set. Treat evaluation cases as requirements, agree them with engineering and QA, and gate the launch on them.
How the case runs
Throughout the course you work on one realistic case: the course-registration and payment flow of a small learning platform, plus an AI-powered assistant that helps visitors choose a course. Developers meet the same case in Spec-Driven Development, so teams that learn together practise on the same material. You start with an ambiguous request, rebuild it as a problem brief, scope an AI feature on top of it, and finish with a launch plan that names risks, metrics and owners.
What this course is not
It is not a prompt-writing course and it does not teach you to code. It teaches the product decisions that make AI-assisted delivery work, and the vocabulary to discuss them confidently with engineering, QA and leadership.
Course glossary
Problem brief
A short description of who has a problem, what changes when it is solved and how success is measured.
Non-goal
A behaviour deliberately excluded from scope and written down.
Acceptance criterion
An observable, pass-or-fail statement about the product.
Quality bar
The correctness an AI feature must reach before launch, expressed as examples and measures.
Evaluation set
Fixed, realistic inputs with expected behaviour, run against every version of a feature.
Guardrail metric
A measure that must not get worse while the main outcome improves.
Fallback path
The product’s behaviour when the model fails, refuses or is unavailable.
Compare the approaches
How product teams hand work to AI-assisted delivery
| Approach | What drives the work | Works well for | Breaks down when |
|---|---|---|---|
| Ticket-driven | A one-line request in the backlog | Small, well-understood changes | The agent fills gaps with guesses nobody reviewed |
| Solution-led roadmap | A preferred feature chosen up front | Commitments that are already settled | The real problem was different from the assumed one |
| Outcome-driven discovery | A measurable change for a user group | Choosing what to build next | Outcomes are never translated into testable behaviour |
| Spec-driven hand-off | A brief with outcomes, non-goals and acceptance criteria | Teams that delegate implementation to agents | The brief is treated as finished and never updated |
| Evaluation-led AI features | An agreed evaluation set and guardrail metrics | Features powered by language models | Evaluation cases do not reflect real users |
These approaches combine. Outcome-driven discovery decides what to build, the spec-driven hand-off makes it buildable, and evaluation-led delivery proves an AI feature is good enough to ship.
Teaching perspective
This course is developed with our AI teaching personas and reviewed by the experienced software developers behind the lab. The personas are creative identities for AI-assisted perspectives, not human instructors.
- Lumen — Product & UX Strategist: whose problem are we solving?
- Atlas — Systems Architect: what must be true before we build?
- Prism — Data & Model Analyst: what does the evidence actually support?
Human responsibility stays human: we decide what belongs in the course, test the examples and correct what is wrong.
Your effort.
Made official.
Complete the required lessons and pass any required assessments to earn your AI for Product Managers certificate of completion.
Download your PDF and share a public verification link. Each issued certificate has its own code and QR code. This is an AI Pioneers completion certificate, not external accreditation.
Loading certificate preview…
Generated with the same template as the awarded certificate. If the preview is unavailable on your device, use “Open sample PDF” above.
Experiences.
In their own words.
Reviews are submitted by enrolled learners and checked before publication. Ratings include approved reviews only. Critical feedback is welcome; spam, abuse and unrelated content are not.
No published reviews yet. Your experience could help the next learner.