Checklist

AI Collaboration Roles & Hand-off Checklist

A practical, fillable checklist to define human and AI roles, hand-offs, verification steps, and governance checkpoints for collaboration workflows. Use it to capture assignments, required transparency, data sensitivity, acceptance criteria, and escalation paths so teams can safely and consistently include AI in work.

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AI Collaboration Roles & Hand-off Checklist

Use this checklist to capture clear role definitions, hand-off points, verification steps, data governance checks, and acceptance criteria whenever AI tools participate in team workflows. Completing this form creates a reusable template for the workflow and helps preserve accountability, reduce hidden errors, and surface governance risks.

Short name for the process or deliverable (e.g., Meeting notes, Draft research brief, Customer response).
One or two sentences describing what this workflow does and why AI is being used.
Choose the appropriate classification to guide data handling, storage, and verification requirements.
If yes, follow your organization's privacy and compliance review before enabling AI assistance.
Who is responsible for producing the initial content or output (name or role).
People or roles expected to edit, adjust, or refine AI-generated content.
Person or role who must verify accuracy, fairness, and compliance before publication or action.
Describe what the AI will do (e.g., summarize meeting audio, draft an internal memo, extract citations) and any specific tools or models used.
Select the types of outputs the AI will produce.
Describe when responsibility moves between actors (e.g., after AI draft is produced -> human editor reviews; after editor approves -> verifier publishes). Include event triggers or expected artifact names.
Short, specific wording that must accompany any AI-influenced output (e.g., 'This summary was generated with AI assistance and reviewed by [role]'). Provide the exact phrasing teams should use.
Select governance controls required for this workflow. Add details in the notes field below if needed.
Concrete checks the verifier must perform (e.g., factual accuracy of all dates, no uncited diagnostic claims, alignment with policy X). Make criteria specific and testable.
Step-by-step actions verifier or editor should perform (e.g., run source-check, confirm citations, run bias checklist). Consider linking to supporting tools or scripts.
Who to contact if the verifier finds a problem they cannot resolve (names/roles and response SLA).
Provide 1–3 short examples showing how roles map to typical tasks (e.g., Meeting notes: Recorder = human; AI = draft summarizer; Editor = team lead; Verifier = compliance officer).
Assess the likely harm if the AI output is incorrect. Higher risk requires stronger verification and governance.
If yes, AI output cannot be published or acted on without explicit verifier sign-off.
Store standard prompts, model settings, or guidance that operators should use to produce reliable outputs. Keep this up to date as tools or policies change.
How often this workflow and its controls should be reviewed and updated.
Links to policies, playbooks, templates, or related checklists.
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