Marketing OS Playbook

Map the operating loop

Choose one repeatable growth workflow, such as researching a customer question, producing an asset and learning from its performance. Map each step, owner, input and handoff before introducing AI.

Look for repeated work and unclear approvals. A useful system makes sources, decisions and responsibility visible; producing more assets alone does not create a stronger operating model.

Audience and problem:
Research inputs:
Asset and channel:
Owner:
Review checkpoint:
Distribution plan:
Learning to capture:

Create a reusable brief and review gate

Use a brief that names the audience, desired action, evidence, constraints and tone. Keep approved facts and source links close to the draft. Ask AI to identify missing evidence instead of inventing it.

A person should approve public claims, customer references and consequential messages. Record what changed during review so the next iteration improves the brief rather than repeating the same mistakes.

• Verify product and customer claims.
• Confirm rights to use images and quotations.
• Review links, tracking and the landing-page promise.
• Remove confidential inputs before publication.
• Name the person accountable for the final asset.

Close the learning loop

Choose a primary outcome and a small set of supporting measures. Distinguish attention from useful action: a popular post may produce few qualified conversations.

Review results on a fixed cadence. Keep what worked, investigate weak handoffs and retire assets that no longer match the product. Turn successful briefs, review notes and experiments into reusable templates.

Hypothesis:
Primary outcome:
Supporting measures:
Observed result:
What we learned:
Next change:
Template to retain: