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1. Roles & Responsibilities

Purpose

Overview of all roles in an AI project and their responsibilities per lifecycle phase.

In AI projects the boundaries between business and IT blur. That is why we define roles based on responsibility, not job title.


2. The Core Team (The Squad)

Role Ownership Focus
AI Product Manager Objective Card "Are we solving the right problem?" — coordinates the project process, translates business needs into AI instructions
Tech Lead Technical Model Card "Is it robust and scalable?" — selects model, builds pipelines, safeguards stability
Guardian (duo) Risk Pre-Scan "Is it safe and fair?" — veto rights on hard boundaries. Staffed by Privacy & Legal Officer + AI Quality Ethicist

For High Risk projects, explicit approval from both Guardian members is required at Gate Reviews.

AI Product Manager ≠ Product Owner

The AI Product Manager is a coordinating role — comparable to a combination of Scrum Master and project coordinator. The AI PM safeguards the process, the gates and the quality of the lifecycle. Scope ownership (which problem do we solve, which use case takes priority) lies with the Business Sponsor or CAIO. This prevents the classic conflict where one person manages both scope and planning/budget.


3. Supporting Roles

Role Focus When to deploy
Data Engineer Data quality Always — ensures data arrives clean at the model
AI Tester (QA) Reliability From Phase 2 — adversarial testing and Golden Set management
Adoption Manager Change From Phase 4 — ensures people use the tool (ADKAR)
Context Builder Knowledge mgmt For RAG systems or multiple knowledge sources — manages what the model sees [so-44]
AI Security Officer Security For High/Limited Risk — OWASP LLM Top 10, red teaming, incident response [so-45]

4. Strategic Level

Chief AI Officer (CAIO) — Programme sponsor. Determines the strategy, allocates budget and decides at the Gates whether a project continues or stops.


5. Deep Dives