Template

AI Roles & Responsibility Matrix

A practical, copy-ready template that defines human and AI responsibilities across common collaboration tasks, including clear handoff points, approval gates, transparency statements, quality checks, and an escalation protocol.

Purpose

This template helps teams define who owns outputs, what quality checks are required, and how handoffs occur when AI tools participate in collaboration work. Use it to avoid blurred responsibility, reduce over-reliance on AI, and create transparent, auditable workflows.

How to use this template

  1. Pick the activities your team performs (sample list below).
  2. For each activity, assign human role owners and describe the AI's allowed contribution.
  3. Specify the handoff point and required human approvals.
  4. Record transparency notes and the required quality checks or validations.
  5. Agree an escalation protocol for ambiguous or high-risk outputs and schedule periodic reviews.

Definitions (use in your tailored copy)

  • AI Assistant — Any automated tool, model, or service that generates, transforms, or suggests content, data, code, or decisions.
  • Owner — The human role ultimately accountable for the output (final decision-maker or content owner).
  • Reviewer — Human who performs quality checks, compliance review, or edits before finalization.
  • SME — Subject-matter expert consulted for domain accuracy.
  • Approval Gate — A required human action (review, sign-off, test) before the output is used, published, or deployed.
  • Handoff Point — The moment where responsibility moves between AI and human or between human roles.

Sample Role Matrix

Copy this table into your team documentation and edit rows to match your workflows.

Activity Typical AI Contribution Human Owner Human Reviewer / SME Approval Gate (Required human action) Handoff Point Transparency Note Quality Checks Escalation Trigger
Document drafting (policies, reports) Generate first drafts, propose structures, suggest citations Team Lead / Document Owner SME, Legal (if required) Owner review and sign-off before publishing After AI draft generation, before external review Note: "Draft generated with AI assistance" plus model/version Check factual claims, citations, regulatory language accuracy Conflicting legal guidance or low-confidence claims
Summarization (meeting notes, research) Condense long text, extract action items Meeting facilitator / Project Owner Participants or designated reviewer Participant confirmation of action items After AI summary produced and before distribution Label as "AI-assisted summary"; attach original source link Verify action items and decisions against recording or notes Missing or incorrect decisions listed as actions
Research & information gathering Perform web scans, collate sources, produce syntheses Research Lead SME for domain validation SME validates critical findings before use After collection and initial synthesis Record source list and retrieval date; model prompts kept Cross-check primary sources; flag uncited claims Lack of primary source for key claim or contradictory sources
Code generation / automation scripts Scaffold code, suggest snippets, refactoring ideas Engineering Owner Peer Review, Security/QA Peer review + automated tests must pass before merge After AI-generated code and before merge/production Note AI contribution; include unit/integration tests Run static analysis, security scans, and unit tests Security vulnerabilities, failing tests, or performance regressions
Customer response drafts Draft reply options, propose tone and structure Customer Support Lead Support QA / Legal for sensitive issues Human review for tone, legal exposure, and correctness Before sending any customer-facing message Indicate AI-assisted draft in internal record; do not disclose to customer unless policy requires Validate facts, account details, promises, and tone Potential legal exposure, promises exceeding policy, or escalation requests
Image or media generation Generate visuals, mockups, or variants Design Lead Brand/Legal Brand approval for external use After image draft and before publishing Declare model and training constraints; confirm licensing Check for brand conformity, misrepresentation, or copyrighted elements Sensitive content, trademark conflict, or likeness issues
Decision recommendations (analytics-driven) Produce ranked options, risk estimates, or scenario analysis Decision Owner / Manager Data Science / Risk Owner validates inputs, assumptions, and model limitations When recommendation is generated and before implementation Include model version, data window, and confidence measures Validate input data, assumptions, and sensitivity analysis Low confidence, data anomalies, or high-stakes consequences

Approval Gate Patterns (pick one per activity)

  • Inform & Review — AI drafts; human reviews and edits before publishing.
  • Approve Before Use — AI may propose options but human approval is mandatory prior to any external use.
  • Test & Approve — AI outputs must pass automated and human QA (e.g., tests for code) before moving to production.
  • Restricted Automation — For low-risk or non-customer-facing tasks, AI may act with monitoring and periodic spot checks.

Escalation Protocol (template)

  1. Trigger detection: Reviewer flags low-confidence, inconsistency, or legal/ethical concern.
  2. Immediate containment: Stop further automated use of the output and mark as "flagged by reviewer" in records.
  3. Notify: Inform Owner, SME, and Compliance within agreed SLA (e.g., 4 business hours for non-critical; 1 hour for critical).
  4. Initial triage: Owner and SME determine if remediation, re-generation with revised prompts/data, or human-only rework is needed.
  5. Resolution & record: Document root cause, corrective steps, and update the role matrix or prompts to prevent recurrence.

Transparency & Recordkeeping

For each AI-assisted output, record at minimum:

  • Model/service name and version
  • Prompt or query used (or a redacted form if sensitive)
  • Date/time of generation
  • Human reviewer initials and approval timestamp
  • Source links used by the AI where applicable

Checklist for Implementation

  • Customize the activity rows to match your workflows and add local roles/titles.
  • Agree required approval gates and SLA for review and escalation.
  • Define minimum transparency fields and where they will be stored (ticketing system, document metadata, audit log).
  • Run a pilot for high-volume or high-risk activities for 30 days, track incidents and false positives/negatives.
  • Schedule periodic reviews (quarterly or after any incident) to update the matrix, prompts, or governance rules.

Suggested Metrics to Monitor

  • Percent of AI-generated outputs that required human edits before approval
  • Time-to-approve for AI-assisted outputs
  • Number of escalations per activity and root-cause categories
  • Compliance or legal flags identified during review
  • User satisfaction with AI-assisted drafts (qualitative feedback)

Versioning, Ownership & Tailoring

Keep this matrix under configuration control. Record a version number, last editor, and tailoring notes for any team or business unit that adapts it. Use this template as a starting point and preserve local changes so audits can connect specific practices to outcomes.

Example — Filled Entry (concise)

Activity: Market research brief
AI contribution: Collate recent articles and produce a 1-page synthesis
Owner: Product Manager
Reviewer/SME: Research Lead
Approval Gate: SME validation of key facts & sources before publishing
Handoff point: After AI synthesis, before stakeholder distribution
Transparency: Attached source list and model ID
Quality checks: Confirm primary sources for top 3 claims
Escalation: If contradictory primary sources found, escalate to Research Lead

Tailoring opportunities

Consider adding specific rows for functions with unique risks (clinical, legal, financial, safety-critical engineering). For regulated domains, embed required regulatory sign-offs and evidence retention policies.

Next steps (recommended)

  1. Copy this template into your team knowledge base or domain collection.
  2. Run a short workshop to populate and agree the matrix with Owners and SMEs.
  3. Implement transparency fields in your document or ticketing system so every AI-assisted item records model details and reviewer approvals.

Note: This template is intended to guide practical governance and reduce blurry responsibility. It is not legal or regulatory advice. For regulated activities consult your compliance or legal team.

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