AI Product Management
Deciding what an AI system should do, and what it must refuse to do.
Who this is for
Product managers responsible for AI features that behave probabilistically.
What you should be able to do
Write a PRD that states the quality bar, the failure behaviour, and how adoption is measured.
Career ladder
The titles this track maps onto. Levels differ between companies — the useful part is the direction, and what each step adds to the one before it.
- Associate PM
- Product Manager
- Senior PM
- Director of Product
- VP Product / Chief Product Officer
Tech stack
What the work is actually done with. Grouped by the job each tool does, so the list reads as a system rather than a pile of names.
- Discovery
- User researchJobs to be doneOpportunity mapping
- Definition
- PRDsAcceptance criteriaFailure-mode specs
- Measurement
- ActivationRetentionTask success rateDeflection
- AI-specific
- Eval setsConfidence thresholdsHuman-in-the-loop design
The delivery flow
The order the work actually happens in. Each step is where a decision gets made and written down, not a chapter heading.
- Problem definition
- User research
- Opportunity sizing
- Quality bar
- Failure behaviour
- Rollout plan
- Adoption metrics
- Review cadence
Reference repository structure
A starting layout for this track. The directories are the ones that get added late and hurt — decisions, evals, policy, lineage — promoted to the top level where they are visible.
product/ ├── discovery/ │ ├── interviews/ │ └── opportunity-map.md ├── prds/ │ └── <feature>/ │ ├── prd.md │ ├── quality-bar.md # what "good enough to ship" means, numerically │ └── failure-modes.md # what it does when it is wrong ├── evals/ # product-owned, not only engineering-owned ├── metrics/ │ └── definitions.md # one definition per metric, agreed once └── rollout/
Reference implementations
Citadel’s open-source repositories for this track — Terraform modules, MCP servers, and reference architectures you can read, fork, and run. Apache/MIT licensed; check each repository for its terms.
- GitHubai-career-intelligence-hubAI Career Intelligence Hub — Product · Project · Program · Delivery | 285+ curated AI resources, MCP ecosystem, agent frameworks, certifications for AI leadership rolesai-career-intelligence-hub on github.com (external site, opens in a new tab)
- GitHubagentforgeAI Agent Engineering Curriculum — 8 enterprise categories, architecture diagrams, hands-on labs | Conversational, Autonomous, DevOps, Data, Security, Industry, Infrastructure, Cross-Cloudagentforge on github.com (external site, opens in a new tab)
Primary sources
The standards, framework documents, and vendor references this track is built against. Go here when you need the authoritative wording rather than a summary — in a security review or an audit, the source is what counts.