Automation
How can AI improve marketing workflows?
AI marketing workflows help most with repeated writing and sorting work: campaign recaps, lead notes, content briefs, feedback tagging and report commentary. Make, Zapier or HubSpot moves the data, an AI step drafts or suggests, and a named person approves the output before it is sent to a customer, changes a live CRM field or is published.
What an AI marketing workflow is: input, AI step, review step, destination
An AI marketing workflow is a repeated process in which one step uses AI to summarize, classify or draft, and every other step is ordinary automation. If you cannot name the reviewer and the destination, it is not ready to build.
Most AI steps belong in internal workflows, not customer journeys (see marketing automation vs workflow automation). Every AI workflow has four parts:
| Part | What it is |
|---|---|
| Input | The trigger and the data the AI receives |
| AI step | One narrow task with written instructions |
| Review step | A named person approves, edits or rejects |
| Destination | Where output lands; only approved output reaches customers or live fields |
AI marketing workflows worth starting with
Good first candidates repeat at least monthly, use inputs already stored in a tool, and produce output a person can check against a source.
Campaign recap summaries
A useful recap needs the goal, KPI and target set before launch; campaign performance analysis covers those.
- Input: the campaign brief plus final results from the ad platforms, email tool or CRM.
- AI step: a five-bullet draft (result against target, what worked, what did not, open questions, one next test) that uses only the supplied figures and writes "not in the data" for any gap.
- Human review: the campaign owner checks every number against the source and deletes unsupported conclusions.
- Destination: the team channel and the campaign record.
Lead classification and routing notes
Rules struggle with free-text answers like "Tell us about your project." An AI step can suggest a category and draft a note, while automated lead routing rules still assign the owner.
- Input: the free-text answer plus any form fields the category depends on, such as company type; not name, email or phone.
- AI step: one category from a fixed list you define (new project, support, partnership, spam) plus a two-line summary.
- Human review: the owner confirms or corrects the category; spam and support go to a review queue, never deleted or re-routed automatically.
- Destination: a labelled AI suggestion field and an AI-draft note on the CRM contact; neither changes the owner or lifecycle stage.
Content briefs and first drafts
- Input: an approved topic, audience, key message, expert notes and brand guidelines.
- AI step: a brief (angle, outline, claims to verify) or a first draft of a routine piece such as a social post variation.
- Human review: an editor checks facts, claims and tone; an expert approves anything technical. Nothing publishes from the workflow.
- Destination: a draft document linked from the content calendar.
Customer feedback tagging
- Input: survey responses, reviews or call notes, with unneeded personal details removed.
- AI step: one or two tags from a fixed list (pricing, onboarding, feature request, complaint) and a short quote.
- Human review: a person reads every complaint and spot-checks a sample of the rest each week.
- Destination: an Airtable table that feeds a monthly themes summary.
Reporting commentary
- Input: this period's finished figures from the reporting tool, last period's figures and the targets.
- AI step: three to five sentences on the biggest changes.
- Human review: the report owner checks each figure against the dashboard and adds context the data cannot show, such as a paused campaign.
- Destination: the commentary section of the monthly report.
The automation around the AI step (Make, Zapier, HubSpot, Airtable)
In Make or Zapier the AI call is one step and usually the smallest part of the build; the real work is clean inputs, a review queue and writing approved output back.
| Tool | Typical job |
|---|---|
| Make | Branching workflows: gather inputs, call the AI service, queue the output, write back approved versions |
| Zapier | Simpler step-by-step workflows that a non-technical owner can maintain |
| HubSpot | The system of record: forms, contact and deal properties, and tasks for the reviewer |
| Airtable | The review queue: one row per output, with a status field and the reviewer's edits |
How the review step works
- 01The output is saved as a Needs review draft in the review queue or a labelled suggestion field. It never overwrites a live field and is never sent.
- 02The reviewer gets a task or Slack message linking to the draft.
- 03They approve, edit or reject it, and edits are stored beside the original.
- 04Only Approved triggers the step that sends, posts or changes a live CRM field.
- 05A draft left unreviewed past its deadline alerts the owner instead of going out by default.
Keeping quality, accuracy and data safe
AI output can read confidently and still be wrong, so quality has to come from the workflow design. Settle these before go-live:
- Versioned instructions: keep them in a shared document with the category list and an example of good output.
- Minimum data: send only the fields the task needs; never passwords, payment details, health information or anything your contracts or policies bar from outside services.
- Approved accounts: use a company-approved AI service through a company-owned account, and check how its terms treat submitted data.
- Logs and claims: log each input, output and edit, and remove any statistic or quote you cannot trace to a source.
- Review load: estimate it as always-reviewed items + (sample rate × all other items). Hypothetical example: of 200 tagged responses a month, 30 are complaints, so 30 + (1/10 × 170) = 30 + 17 = 47 reviews a month.
What not to hand to AI
Some decisions stay with a person or with fixed rules. The contract generation automation case study shows the pattern without AI: missing information is flagged for review and final approval stays with the team.
| Task | Keep it with | Why |
|---|---|---|
| Publishing or messaging customers | A person approving each item | A wrong message cannot be recalled |
| Calculating KPIs, spend or revenue | Report and spreadsheet formulas | Figures must be reproducible |
| Assigning owners or lifecycle stages | CRM rules, such as your MQL and SQL definitions | Routing must be auditable |
| Pricing and contract terms | Approved templates and the deal owner | A wrong term can bind the business |
| Deleting or merging CRM records | A person working from a review list | Hard to undo |
| Budget and campaign decisions | The marketing lead | A summary informs; it does not decide |
When AI marketing workflow consulting makes sense
You can build a simple AI step yourself in Make or Zapier. Outside help pays off when:
- The workflow writes to your CRM, so a bad output could change live records.
- It spans three or more tools and nobody owns it end to end.
- Existing automations break quietly and need fixing first.
- You run an agency with more client automation requests than builders.
What to ask an AI marketing automation consultant
Ask these five questions before anyone builds an AI step for you. In my workflow automation consulting work, a named person approves before anything is sent or overwritten, everything runs in your company's accounts, and failed runs send an alert naming the step.
- Review: Who approves each output, and what happens if nobody does?
- Accounts: Will the workflow and AI service run in your company's accounts, so you keep both afterwards?
- Data: Which fields go to the AI service, and which are always excluded?
- Failures: Who gets an alert when a run fails, and does it name the failed step?
- Instructions: Where are the AI instructions kept, and how are changes tested before going live?
Questions this guide answers
What is a good first AI workflow project?
Campaign recaps or feedback tagging. Both use data already in your tools, errors are easy to spot, and nothing reaches a customer.
Should AI publish marketing content automatically?
For most businesses, no. The workflow should create a draft for an editor, and a person approves before anything is published, emailed to customers or written to a live CRM field.
Can I use AI with HubSpot data?
Yes, with limits. Make or Zapier can send selected HubSpot fields to an approved AI service and write the result back as a note or a labelled suggestion property that no workflow acts on until a person approves it. Changes to owner, lifecycle stage or deal amount stay behind rules and human approval.
How do I know whether an AI workflow is working?
Track three numbers: approval rate (outputs approved with light or no edits ÷ outputs reviewed), rejection rate (outputs rejected ÷ outputs reviewed) and minutes saved per month ((minutes per item by hand − minutes per item to review) × items per month). If reviewers rewrite most outputs, fix the inputs or instructions before adding workflows.
Have a repeated task that could use an AI step with a review checkpoint?
Bring one task your team repeats weekly or monthly to a 20-minute call. I'll look at its inputs, where review belongs and whether an AI step is worth adding at all.
Book a 20-minute workflow reviewMichelle Ivanova runs imivs consulting, building marketing operations, CRM workflows and reporting automation in the tools a team already uses.