Turns scattered AI usage into a system that scales your marketing function.
AI has arrived person by person in most marketing teams today. And this resulted in ten people using AI in ten different ways. There was no cohesiveness in prompts and they all made ten sets of judgment calls about what’s safe to paste where. This is the realistic state of AI and marketing today.
The use of AI by marketers is already happening whether it’s been planned for or not. It shows up in shipped work long before it shows up in any policy doc. This playbook starts by auditing that reality and ends with you building a system for it.
Once you know what’s happening, the next step is deciding what AI agents for marketing teams should own. This playbook is built around three ideas:
That’s what separates a real AI marketing strategy from a pile of individually clever prompts. It’s also how you evaluate the best AI for marketing that your specific team will use. Since the right AI agent stacks for marketing depend on which tasks you’ve defined, instead of a race for most features.
Inside, you’ll find concrete AI use cases for marketing, from first-drafting case studies to running AI for marketing campaigns, plus the ways to run them safely.
In practice, it’s a handful of recurring tasks like first drafts, briefs, campaign copy variants, strategy suggestions etc, handed to an agent. It’s several narrow jobs done consistently with an expert army.
An AI agent for marketing is essentially an autonomous system that functions independently to plan and execute multiple steps without your active intervention or participation. ChatGPT, or any other LLM is typically a conversational AI that answers queries and generates texts, images, videos, and more based on your active participation.
Start with an audit of current use of AI by your marketing team, then score each recurring task against a list of questions. Then build a strategy for it, and guardrails, before assigning it to an agent. An AI strategy consists of what and how AI will work, its job descriptions, context files, and review lanes for maximum accuracy and safety.
There isn’t one best AI for marketing across every team. This will depend on the jobs you’ve defined. Pick your first three tasks using the scoring method in this playbook, then choose tools that fit those specific jobs.
Repetitive, checkable tasks like first drafts of case studies, weekly newsletter drafts, campaign copy variants, and FAQ content. Positioning, pricing, and crisis communication should stay human.
Route every output through three lanes like green, amber, red, based on its risk. Anything with a number in it goes to the red lane and needs a second, named approver before it ships. Based on your industry, set up these rules and enforce them strictly.
A workable stack for marketing needs five context files: voice, audience, offers, competitors, style, a shared prompt library, a one-page brief template, and a monthly scorecard to make sure everything is running well.
