The playbook
AI Marketing Workflows: Template and Field Guide
This AI marketing workflows template turns a broad task into a record another person can use. It captures task, input, output, then connects that information to a decision. Copy the table into your working document or system and create a separate record for each item in scope.
Put the task in context
Give AI a bounded task, accepted source material, and a clear output specification. Check facts, customer context, and proposed actions before release. Record which parts are drafts and which actions have actually executed so a fluent explanation is not mistaken for completed work.
Copyable worksheet
| Field | Entry instructions |
|---|---|
| task | Record the task associated with this client or workflow record. Use a stable reference so later updates can be matched. |
| input | Record the input associated with this client or workflow record. Keep the value specific enough for another owner to interpret. |
| output | Record the output associated with this client or workflow record. Keep the value specific enough for another owner to interpret. |
| review criterion | Record the review criterion associated with this client or workflow record. Keep the value specific enough for another owner to interpret. |
| Decision field | Entry instructions |
|---|---|
| Observed problem | Describe the actual condition and where it was observed. |
| Recommended action | State the smallest action that resolves the documented problem. |
| Evidence | Link to the record, observation, or test supporting the action. |
| Owner and next review | Name the accountable person and when the decision should be revisited. |
How to complete the worksheet
Begin with task. Use the same reference throughout the record so notes are not attached to the wrong item. Then specify the task accepted sources and expected output. This establishes the information required before the worksheet becomes a request for action.
Next, check factual claims brand fit and customer context. Use the observed problem and evidence fields to explain why that step is needed. A useful entry describes an actual condition, such as judging AI output only by how polished it sounds, rather than a broad instruction to “improve performance.”
Finally, log material changes and evaluate useful completed work. Put test factual accuracy task completion and downstream usefulness in the action field only when it addresses the observed condition. If the problem is different, change the action. The template is a structure for judgment, not a reason to apply the same fix everywhere.
Illustrative completed decision
| Part | Illustrative entry |
|---|---|
| Problem | Judging AI output only by how polished it sounds |
| Action | Test factual accuracy task completion and downstream usefulness |
| First check | Specify the task accepted sources and expected output |
| Follow-up check | Log material changes and evaluate useful completed work |
| Outcome to define | accepted useful AI-assisted outputs |
Adapt the fields without losing meaning
You can add fields required by your team, but each should change a decision or preserve necessary context. Keep factual observations separate from recommendations. If input changes, record the new value and its effective date instead of silently rewriting the context of an earlier decision.
Use the record in a handoff
Ask the receiving owner to explain the next action using only the worksheet. Missing context becomes visible quickly during that exercise. Repair the record before applying the structure to a larger batch. Keep accepted useful AI-assisted outputs connected to the item so the team can later assess whether the action was useful.
Prefer a small set of shared definitions across marketing, sales, and delivery. If a qualified lead or accepted deliverable means something different to each team, the disagreement will reappear in reports and client conversations.
Skip the manual steps. Let AgentixHUB run this for every client.
Start now