Choose an AI marketing workflow you can actually evaluate.
Start with a bounded task, an observable baseline and a review standard before increasing output.
Choose a repeated task with a clear owner
A campaign brief, a research synthesis or a first draft can be a useful starting point when the task is repeated and someone knows what good work looks like. Avoid beginning with an undefined instruction to automate marketing. Write down the inputs, expected output, reviewer and next action. If the team cannot agree on the output, the first work is clarifying the process.
Record the current state
Observe representative examples of the existing task. How long does preparation take? Which errors recur? Where does the reviewer spend time? What source material is missing? Keep the baseline specific enough to compare later. Do not substitute a team’s enthusiasm for a measurement of the work, and do not treat every task as equally difficult.
Define an acceptance rubric
For a campaign brief, assess audience specificity, consistency with positioning, supported claims, channel constraints and usefulness of the proposed next step. An evaluator should be able to explain why the output passes or fails. Include hard boundaries: unsupported customer claims, sensitive data leakage and an incorrect offer can matter more than a polished writing style.
Keep publishing as a separate decision
Producing a draft does not authorize its publication. Design the workflow so the reviewer can inspect sources and correct the work before it reaches customers. State who has approval authority and which systems the workflow may write to. This makes it possible to learn from a narrow implementation without quietly removing the team’s existing controls.
Measure the whole task
Compare preparation, generation, review and correction effort together. A faster first draft is only one part of the task. Track the fraction accepted with minor edits, the nature of failures and whether people continue using the workflow. Campaign engagement and pipeline can be relevant later, but changing audience, channel mix and offer can confound a simple before-and-after comparison. Label those limits.
Expand when the evidence supports it
Use observed failures to improve the context and instructions. Re-run representative cases after meaningful changes. Expand to adjacent tasks only when the original workflow has an owner, a review rhythm and a credible maintenance path. The aim is useful capacity and more consistent work, not output volume as an end in itself.
This is practical guidance, not a report of client outcomes. Examples are illustrative.