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Product Manager + AI

AI product discovery, metrics, economics, and lifecycle

A path for product managers covering AI feature discovery, capabilities, evaluation, AI UX, unit economics, experimentation, safety, and lifecycle management.

Finish the course with a governed AI product dossier, not an “AI-first” slide deck: problem definition, capability fit, an evidence-backed prototype, an evaluation contract, unit economics, risk boundaries, rollout, telemetry, rollback, and sunset criteria.

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8–14 weeks3 modules10 lessons2 assessments

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How to complete the course and retain a real result

1. Start with the problem, not the model

For every topic, define the user or job, frequency, current-process baseline, failure impact, and the criterion that proves AI is actually better than simpler automation.

2. Define evidence before the prototype

Before a demo, write down representative cases, acceptance thresholds, forbidden outcomes, and the business metric. Otherwise the prototype mainly proves that a prototype exists.

3. Experiment through evals

Compare every model, prompt, context, or tool change against the same evaluation contract and baseline. Run online experiments only after offline quality and safety gates pass.

4. Launch as an operating system

Canary, budget, monitoring, human review, incident path, rollback, and sunset are part of product design. “Operations will handle it after launch” usually means handing them a surprise.

Course rules

Do not merely read it — prove it

  • Do not start discovery by choosing a model. Start with the problem, baseline, evidence, and failure impact.
  • Do not use an A/B test to discover whether the system is safe at all. High-severity failures must be filtered by offline gates.
  • Measure cost per successful task including retries, review, and failure handling. Token price alone tells a product manager very little.
  • Do not copy vendor-reported adoption, accuracy, or revenue uplift into your forecast without validating attribution and transfer assumptions.
  • Every high-impact production correction updates the eval set, and every model, prompt, context, or tool change goes through a version-bound release decision.

Module 1

Shared AI core

Model limits, structured outputs, and evaluation for product decisions.

OutcomeThe product manager understands what can be promised to users and how those promises can be verified.

  1. AI literacy and model limits

    Core

    Capabilities, hallucinations, context limits, privacy, and responsible use.

    Material →

  2. Prompt and context engineering

    Core

    Instructions, examples, constraints, context, and output verification.

    Prerequisites: AI literacy and model limits

    Material →

  3. Structured outputs and evaluation

    Core

    Response schemas, deterministic checks, test cases, and acceptance criteria.

    Prerequisites: Prompt and context engineering

    Material →Resource →

Checkpoint after module

Product evidence baseline

  • ✓ the problem and user segment are defined without tying them to one model
  • ✓ the success metric has a baseline and owner
  • ✓ an unacceptable outcome is defined for high-impact failure

Scenario transfer lab

Transfer lab: from an AI case to a problem and evidence contract

Use the OpenAI inbound-sales case as a reference business process. Do not copy reported metrics into your own business case. For another company, build a problem tree, baseline funnel, evidence plan, and a list of reasons why the same workflow might not transfer.

Deliverable

One-page problem/evidence contract: user or job, baseline, target outcome, non-goals, representative cases, unacceptable failures, and attribution plan.

ChatGPT/OpenAI AI case →AI² Business →Knowledge map →Product Manager + AI roadmap →
  • ✓ a vendor-reported metric is not reused as your own baseline
  • ✓ at least three transfer-risk hypotheses are documented
  • ✓ the business metric is separated from the model-quality metric
  • ✓ a ground-truth source for the outcome is defined

Module 2

AI product discovery

Problem framing, capability fit, and value hypotheses.

OutcomeThe AI feature has a clear problem, a defined user, and measurable success metrics.

  1. Capability fit and problem selection

    Core

    Identify where AI creates value and where it introduces unnecessary risk.

    Material →

  2. AI UX, trust, and uncertainty

    Core

    Feedback, explainability, approvals, failures, and user control.

    Material →

  3. Project: AI feature discovery dossier

    Projectpractice + assessment

    Define the problem, prototype, metrics, risks, economics, and experiment plan.

    Prerequisites: Capability fit and problem selection, AI UX, trust, and uncertainty

    Material →Practice →Assessment →

Checkpoint after module

Validated discovery package

  • ✓ the prototype is tested on representative cases
  • ✓ quality and safety acceptance thresholds are defined before testing
  • ✓ human review and authority boundaries are specified for consequential actions

Scenario transfer lab

Transfer lab: AI feature discovery with kill criteria

Choose a consumer or internal AI feature and run discovery so the team can not only approve the idea but also kill it early. Include capability fit, a prototype, eval set, human-review boundary, abuse cases, and a simpler non-AI alternative.

Deliverable

AI feature discovery dossier plus an AI-vs-deterministic-automation comparison table and pre-registered go/iterate/kill criteria.

AI² Consumer →AI product discovery →Discovery dossier practicum →Human in the loop →
  • ✓ a non-AI baseline exists
  • ✓ representative eval cases cover the happy path and high-impact failures
  • ✓ the kill criterion is defined before test results are known
  • ✓ human review is tied to risk rather than an arbitrary traffic percentage

Module 3

Metrics, economics, and lifecycle

Quality, safety, cost, experimentation, and operations.

OutcomeThe AI feature is governed through measurable gates from prototype to sunset.

  1. Quality and safety metrics

    Core

    Task success, groundedness, latency, cost, failure rate, and trust.

    Material →

  2. AI unit economics

    Core

    Cost per task, model routing, budget, and margin.

    Material →

  3. Evaluation-driven experimentation

    Core

    Offline evaluations, online tests, release gates, and rollback.

    Material →

  4. Milestone: governed AI feature launch

    Milestonepractice + assessment

    Metrics, safety, cost, rollout, monitoring, and an incident plan.

    Prerequisites: Quality and safety metrics, AI unit economics, Evaluation-driven experimentation

    Material →Practice →Assessment →

Checkpoint after module

Governed launch readiness

  • ✓ unit economics are measured per successful task, not only token cost
  • ✓ rollout includes canary, rollback, and stop criteria
  • ✓ production feedback flows into the eval and regression backlog

Scenario transfer lab

Transfer lab: eval → economics → staged rollout → incident

Turn a working prototype into a launch decision. Calculate cost per successful task, set quality and safety gates, and design canary, telemetry, escalation, rollback, and the post-incident regression loop.

Deliverable

Launch control sheet: eval baseline, release thresholds, unit-economics budget, rollout stages, telemetry-dashboard specification, rollback trigger, and incident-to-regression workflow.

Production evaluation →Cost optimization →AI incident response →AI² Government →
  • ✓ quality, safety, latency, and cost have separate thresholds
  • ✓ cost is normalized per successful task
  • ✓ the canary has an explicit rollback trigger
  • ✓ a production incident or high-severity correction creates a regression case

Capstone contract

Capstone: governed AI feature launch dossier

Design one AI feature from problem framing through the production lifecycle. Another team should be able to make a go/no-go decision without faith in model magic and without “we will handle the risks after beta”.

What to submit

  • — problem brief with user or job, baseline workflow, non-goals, and measurable business outcome
  • — capability-fit decision: AI, deterministic automation, or hybrid with explicit trade-offs
  • — representative evaluation dataset, quality and safety acceptance contract, and regression policy
  • — prototype evidence pack with traces, failure taxonomy, and human-review decisions
  • — unit economics: cost per attempt, cost per successful task, review cost, expected volume, and budget guardrail
  • — risk and authority matrix covering data classes, consequential actions, approvals, audit, and escalation owner
  • — staged rollout plan covering shadow, draft, canary, general availability, telemetry, rollback, and kill criteria
  • — 30-day operating plan covering metric review, incident loop, eval refresh, model or prompt change control, and sunset trigger

When it is ready

  • ✓ business success, model quality, and safety metrics are not collapsed into one vanity score
  • ✓ the go/no-go decision uses pre-defined thresholds and representative evidence
  • ✓ a high-impact action does not gain more autonomy without authority and control evidence
  • ✓ unit economics remain within budget at expected volume and human-review rate
  • ✓ rollout can be stopped or rolled back using a clear runtime signal
  • ✓ vendor-reported metrics are marked as external context rather than causal proof of your own ROI

Final practice and assessment

The course ends with a practical artifact and an acceptance rubric. The result is considered complete after the acceptance criteria are met, not merely after reading the materials.

Project: AI feature discovery dossier →Milestone: governed AI feature launch →Open roadmap →