Learning Ops Roles & Responsibility Matrix

Purpose

This RACI-style template helps teams clarify who owns key learning-ops activities so experiments get run, lessons are captured, and improvements scale. Use it to stop work from becoming ad-hoc or orphaned and to make learning a predictable organizational capability.

How to use this template

1. Customize the function columns to match your workflow (examples below).
2. List roles on the left and mark each cell with R (Responsible), A (Accountable), C (Consulted), or I (Informed). Use S for Support where helpful.
3. Agree the matrix with the team; keep it visible in your knowledge repo and review it each quarter or when responsibilities change.

Typical function columns

• Analytics: run analysis, produce dashboards, measure experiment outcomes
• Data engineering: maintain data pipelines, quality, instrumentation
• Experiment design: propose hypotheses, design interventions, define success metrics
• Knowledge capture: document findings, write short-ready artifacts, create playbooks
• Facilitation / run cadences: run huddles, experiment reviews, and learning rituals
• Governance: set learning ops policy, priortization guardrails, compliance checks

Common role examples (short definitions)

• Team Lead: owns team priorities and ensures learning aligns with goals
• Product Manager: defines experiments and backlog priorities; translates customer needs
• Knowledge Manager: curates learning artifacts, maintains the knowledge repo
• Analyst: performs analytics, evaluates experiment results
• Learning Engineer: designs experiments, builds learning workflows, helps with tooling

Sample filled matrix — small product team

Interpretation notes for the sample

• The Team Lead is Accountable for making learning a priority and for governance; they are informed on analytics and capture activity.
• The Product Manager is Responsible for designing and prioritizing experiments tied to product outcomes.
• The Knowledge Manager owns durable capture and reuse (may be a part-time role at small scale).
• The Analyst owns measurement and outcome evaluation.
• The Learning Engineer often connects experiment design, instrumentation, and facilitation—especially important as you scale experiments.

Hiring priorities by team size

• 1–5 people: Combine responsibilities. Team Lead or Product Manager may handle capture; outsource analytics or use shared analysts. Focus on tooling and simple templates.
• 6–20 people: Hire or designate an Analyst and a Learning Engineer (or part-time). Create a Knowledge Manager role (part-time) to prevent lost lessons.
• 20+ people or multiple teams: Make Knowledge Manager and Learning Engineer full-time. Establish a Learning Ops owner for governance and cross-team coordination.

Practical tips & common pitfalls

• Don’t assign too many Accountables — one accountable per function keeps decisions clear.
• Ensure every experiment has a named Responsible and an owner for capturing the result.
• Keep the matrix visible and tie it to cadences (experiment review meetings, huddles).
• Adapt RACI to local vocabulary (RASCI, DACI) but keep clarity about who captures learning.
• Track hiring decisions to gaps in the matrix so roles fill real work, not titles.

Where to go next

Use this matrix as a living artifact: copy, adjust column names, and store it beside your experiment playbooks. Consider adding a simple checklist for each experiment that identifies who will capture outcomes and where the artifacts will live.