AI Roles & Responsibilities — RACI-light Template

Purpose

This RACI-light template helps teams clarify who owns outputs, who reviews and approves work, and how AI participates in collaborative workflows. Use it to avoid ambiguous handoffs, missed reviews, or compliance gaps when an AI tool contributes drafts, summaries, decision support, or data preparation.

When to use

• Introducing an AI assistant into an existing process (e.g., meeting notes, specs, customer responses).
• Designing new workflows that include AI-generated drafts or automated steps.
• Documenting governance for regulated or safety-critical outputs.

How to use this template

1. List the task or deliverable you want to govern (one row per task).
2. For each task, assign roles and specify the AI's responsibility level (if any).
3. Set acceptance criteria, required quality checks, and review frequency.
4. Record guardrails (data sources, privacy, bias checks, logging) and escalation paths.
5. Review the RACI at a cadence appropriate to risk (see suggested review frequencies below).

Role definitions (RACI-light for human + AI)

• Human Owner: Ultimately accountable for the task’s output; must ensure quality and sign-off when required.
• Human Approver: Performs final acceptance or formal approval before release or action.
• Human Consulted / Reviewer: Provides subject-matter input or validation (may be required for complex or regulated items).
• Human Informed: Kept updated about outcomes but not responsible for execution.
• AI Agent: The AI’s assigned role for the task — e.g., Draft, Summarize, Extract, Score, Suggest Edits, Flag Issues, or None.
• Audit Owner: Responsible for periodically auditing the task’s AI use, logs, and quality controls.

Suggested fields to capture per task

• Task name
• Brief description
• Human Owner (name/role)
• Human Approver (name/role)
• AI Agent (None | Draft | Summarize | Extract | Score | Suggest Edits | Other)
• Audit Owner (name/role)
• Acceptance criteria — explicit pass/fail checks or quality thresholds
• Required quality checks (e.g., factual verification, compliance review, data lineage check)
• Review frequency (e.g., per output, weekly, monthly, on change)
• Escalation path (who to contact for uncertain AI outputs)
• Notes / constraints (data sensitivity, allowed data sources, templates to use)

Suggested guardrails

• Label AI-generated content clearly at the point of use.
• Require a named human Owner to review and accept AI drafts before any external release or decision.
• Define allowed data sources and prohibit use of unapproved datasets for sensitive tasks.
• Log inputs, outputs, model version, and time-stamps for traceability.
• Apply bias, safety, and privacy checks where relevant; document exceptions.
• Use conservative AI roles for high-risk tasks (AI = assist/draft, not approve/decide).
• Define a fallback: when AI confidence is low or warnings occur, route to a human reviewer.
• Maintain a periodic audit (owner specified) to validate AI performance and guardrail effectiveness.

Suggested review cadence (guidance)

• Low-risk, routine outputs (e.g., meeting notes): review monthly and randomly spot-check outputs.
• Moderate-risk outputs (internal specs, product docs): review after major releases and at least quarterly.
• High-risk or regulated outputs (customer notifications, regulatory filings, clinical content): require human approval for every output and weekly or event-driven audits.

Sample completed example — Weekly Planning Meeting

Sample completed example — Product Spec Review

When AI should not be assigned an approval role

AI may assist with drafting, summarizing, or scoring, but should not be the final approver for outputs that affect compliance, legal standing, patient safety, or significant financial decisions. Always require a human Approver for high-impact outputs.

Practical tips

• Start small: pilot the template on a few low- or medium-risk tasks and iterate based on audit findings.
• Keep assignments explicit: include names and backups to avoid unowned work when someone is absent.
• Document model/version used and change it in the RACI when you upgrade the AI capability.
• Store the RACI alongside the process or in a shared team hub and revisit it after the first incidents or near-misses.

Next steps for teams

1. Copy this template into your team workspace and fill out tasks for your top 5 AI-involved processes.
2. Run a 30-day pilot, collect audit findings, and update guardrails where necessary.
3. Consider converting the template to an interactive matrix so assignments and audit results are saved and reportable.

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