AI Leadership
Building the team, the operating model, and the mandate that lets AI work reach production.
Who this is for
Team leads through to Chief AI Officers accountable for AI outcomes, not AI activity.
What you should be able to do
Stand up an operating model with clear ownership, a funding path, and a governance gate people respect.
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.
- AI Team Lead
- AI Manager
- Director of AI
- VP of AI
- Chief AI 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.
- Organisation
- Centre of excellenceFederated modelPlatform team
- Operating model
- IntakeFunding gatesPrioritisation
- Governance
- Risk appetiteApproval authorityAudit readiness
- People
- Hiring loopsCareer laddersEnablement
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.
- Mandate + sponsorship
- Operating model
- Team topology
- Intake + prioritisation
- Funding gates
- Governance authority
- Enablement
- Outcome reporting
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.
ai-function/ ├── mandate/ │ └── charter.md # what this function decides, and what it does not ├── operating-model/ │ ├── intake.md │ ├── prioritisation.md │ └── funding-gates.md ├── team/ │ ├── topology.md # platform vs embedded vs CoE │ └── ladders/ # levelling, published ├── governance/ │ └── authority.md # who can approve what, at which risk tier ├── enablement/ └── reporting/ # outcomes, not activity counts
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.
- GitHub ★ 2citadel-saas-factory265 autonomous AI agents across 15 business domains. Full-stack SaaS framework with real model routing, RAG pipeline, guardrails, and Chrome extension. FastAPI + Next.js + K3s. Runs on any server. $0/month software cost.citadel-saas-factory on github.com (external site, opens in a new tab)
- 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)
- GitHubagentforge-portalAgentForge - AI Agent Engineering Curriculum & Amazon Bedrock AgentCore Crash Courseagentforge-portal 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.