---
title: "AI marketing workflows: from a recommendation to a reviewed draft"
url: https://metadata.io/resources/blog/ai-marketing-workflows
description: "See how AI marketing workflows produce page drafts and competitor briefs, then build your own in Gil Allouche’s three-part live workshop."
source: metadata.io
---

Judge an AI marketing workflow by the work it produces. A landing-page task should give your team a draft you can edit, with sources you can check. Profound’s September 30 webinar showed how a suggested task became an outline, a human decision and a page-copy draft.

The examples below come from [Building the marketing team of the future](https://www.tryprofound.com/webinars/marketing-team-future). The presenter used a simplified Ramp setup and explained that it was separate from Ramp’s production workspace.

If you’re building workflows for your own team, [join my three-part Agentic GTM workshop](https://metadata.io#agentic-gtm-workshop). We’ll work through the stack and the decisions that need a person.

## What is an AI marketing agent?

An AI marketing agent uses instructions, company context and connected tools to complete a defined task. It might produce a research brief, page draft or campaign draft. You set the actions it can take and decide who reviews the work.

Be specific about the result you expect. “Research this audience” leaves plenty of room for an impressive answer you can’t use. “Prepare a landing-page outline for this audience, with a source for each product claim” gives the reviewer a clear job. Our [agentic GTM guide](https://metadata.io/resources/blog/agentic-gtm) covers the broader approach.

## How can AI be used in marketing?

AI can research an audience, prepare an outline, draft a page or assemble a competitor brief. Profound’s demo showed a task progressing into a draft and a competitor run producing a brief. Those examples give a team actual documents to assess.

What the demo showed

Scroll the table to compare all columns.

| Workflow | Visible result | What still needed checking |
|---|---|---|
| Presentation preparation | The presenter opened an existing branded deck. | The replay did not show its creation or independently measure the claimed generation time. |
| Landing-page drafting | A suggestion became an outline, a human decision and a page-copy draft. | The page was not shown publishing to a live website. |
| Competitor research | The presenter started a run, then opened a brief with source posts and suggested actions. | Slack delivery and the accuracy of each source claim were not independently checked. |
| Company-context syncing | The demo displayed brand context and integration options. | The data-sync screen was empty. It did not show working connections to the workplace sources discussed. |

### The landing-page example

Start around 31:20 in the [full replay](https://embed.sequel.io/event/47eaaa06-d8d2-484b-8147-cc76706cfda9). Rachel Feidelman selects a suggested nonprofit landing-page task. It uses source material to prepare an outline, then asks whether to draft the page.

1. At about 31:20, she starts the task. A brief connection delay appears before the work continues.
2. At about 32:15, the outline appears with proposed sections, FAQs and facts that still need confirmation.
3. At about 33:10, she chooses to draft the page. The interface shows tool activity while the task runs.
4. At about 34:35, she opens the page-copy document and scrolls through it.

The useful handoff is the outline. A marketer can check the direction before asking for the full draft, then compare the copy with the original brief. Before publication, someone still needs to verify product claims and customer examples. The demo did not establish a universal approval policy or prove that every claim in the draft was accurate.

### The competitor-research example

Around 35:40, the presenter starts a competitor research run with a chosen lookback window. The configuration lists its inputs, tools, output and schedule. Around 38:20, she opens the brief. You can compare both in the [replay](https://embed.sequel.io/event/47eaaa06-d8d2-484b-8147-cc76706cfda9).

The instructions distinguish an internal battlecard from evidence for public claims. Keep that distinction in your own workflow. A sales team’s competitive notes can help frame the research, but a published comparison needs current sources readers can check.

## How do you build an AI marketing team?

Choose one workflow and give it an owner. Supply approved company context, the tools it needs and a clear description of the output. Decide who checks sources and approves delivery. Add responsibilities once the team has reviewed enough work to know where the agent needs help.

For a demand generation team, that first workflow might be a landing page for a specific audience. The owner defines the audience and offer. The agent gathers approved information and drafts the page. A marketer checks the positioning and sources before it goes into the CMS.

In the webinar’s Ramp interview, Robert Lipman described the need to speak to different buyers with relevant messages. That helps explain the emphasis on audience context. His account of that work is separate from the simplified workspace used in the demo.

Give someone responsibility for keeping the context current. A draft can sound convincing while using old pricing, a retired feature or a customer story you can no longer publish. Record the source of each important fact and when you last checked it.

## How do you build an AI marketing agent?

Define the input, allowed tools, output and stopping conditions. Add company context and source requirements. Run the task manually and inspect the result before scheduling it. Treat reading, drafting, sending and publishing as separate actions, each with its own permissions.

A competitor brief needs a date range and a competitor list. Decide how it should handle an old page with a new timestamp, or a claim with no supporting source. Put those rules in the instructions so the reviewer can see whether the agent followed them.

Here is an example specification you can adapt:

> Find relevant competitor posts within the agreed date range. For each one, include the source URL, publication date, claim and implication for our audience. Flag missing evidence. Prepare a brief for review. Do not send messages or publish changes.

This example specification is our own. Adapt it to your tools and access rights before using it. Our [MCP recipes](https://metadata.io/developers/recipes/) distinguish read, write and destructive actions for tool-connected advertising work. Check that your workspace has the right connection and permission before running an action.

## How should you measure AI marketing workflows?

Compare accepted outputs, source accuracy, review time and rework with your existing process. Check whether the work reaches its intended destination. For commercial work, use a defined attribution rule to connect it to qualified opportunities or revenue. Draft counts alone won’t tell you whether the workflow helps.

Start with questions the team can answer. Did the brief use current sources? How much editing did the page need? Did anyone publish it? If it supported a campaign, which opportunities can that campaign reasonably claim? Our [sourced versus influenced pipeline guide](https://metadata.io/resources/blog/sourced-vs-influenced-pipeline) explains that last distinction.

The webinar’s ROI discussion starts around 49:35. The speakers discuss usage, time, attribution and a customer example. The session does not independently establish a productivity multiplier or pipeline result.

## What did the webinar leave unproven?

The replay showed drafts, a research brief and configuration screens. It did not establish website publication, Slack delivery, the accuracy of every generated claim or measured business results. The interface had a beta label, and the speakers described some capabilities as roadmap work.

At about 27:12, the data-sync screen showed an empty workspace. The presenter described recurring inputs from workplace tools, but those connections were not shown operating. In the Q&A, the team also said Profound did not currently replace project-management tools.

For your first workflow, pick a recurring task the team understands. Agree on what a usable result looks like, inspect the first output and keep a record of delivery. Use that evidence to decide what to automate next.

## Build your own Agentic GTM engine with me

I’m hosting a three-part live workshop for B2B technology CMOs and marketing leaders. We’ll work through outbound email, LinkedIn, paid advertising, ABM landing pages and gifting, including where the work needs human judgment.

The series covers the stack, the operating model, what worked and what failed. Bring the workflow you want to build and use the sessions to work through the decisions behind it.

- [Part 1: October 8, 2026](https://luma.com/agentic-gtm), 1 to 3 p.m. EDT.
- [Part 2: November 17, 2026](https://luma.com/jd8tm0ij), 11 a.m. to 1 p.m. EST.
- [Part 3: December 8, 2026](https://luma.com/wucyo6fw), 1 to 3 p.m. EST.

All sessions are live on Zoom. Registration is subject to host approval.

[Request a place in part 1](https://luma.com/agentic-gtm)

### Source and review scope

This article covers Profound’s September 30, 2026 webinar. Gemini 3.8 Flash processed the complete 51:21 recording, including audio, moving video, demos and Q&A. Codex checked coverage and reconciled the notes. The observations are from the recording, with no independent product test. Replay times are approximate and use the original recording clock.