Now that you can tell the AI capabilities apart, the next move is harder and more useful: deciding which of your actual marketing tasks belong in an AI workflow and which ones absolutely don't. Choosing the right starting point matters. A use case that looks exciting but is hard to verify—like AI-written ad claims—can drain time and create brand risk, while overlooking practical everyday tasks can leave value untapped. This unit gives you a simple lens to sort your week and choose a first win you can defend.

By the end, you’ll be able to:

• Sort recurring marketing work into communication, research, planning, and analysis tasks.
• Evaluate AI candidates using Value, Risk, Repeatability, and Human Review.
• Select a low-risk pilot with a clear success criterion.

Start by looking at a normal week and asking: what kind of marketing work keeps showing up? Most AI-assistable tasks fall into four practical buckets.

Why bother sorting? Because each bucket has a different risk profile and a different verification cost. A draft caption may only need a quick tone check. A market-sizing claim or a performance stat may need a source. If you don’t separate these task types, you may treat every AI output the same way, which is exactly how a confident-but-wrong number slips into a report or a post.

Try This: Take ten minutes this week and list five recurring marketing tasks under each bucket. That list becomes the candidate pool for everything that follows.

Once you have candidates, run each one through the AI Task Fit Screen, four quick checks that tell you whether a task is a fit, a maybe, or a hard no.

Value asks whether the payoff is worth it. Look for tasks where AI can meaningfully reduce time, improve consistency, or help you get unstuck. If AI shaves three minutes off something you do twice a year, the math doesn’t work.

Risk asks what a bad output could cost—and what a careless input could expose. A typo in an internal recap is recoverable; a fabricated stat in an ad, an unsubstantiated product claim, or a misleading customer testimonial is not. Risk also runs in the other direction: never paste unreleased campaign data, customer personally identifiable information (PII), or confidential numbers into an AI tool unless it's been explicitly approved for that use, since anything you submit may be stored or used to train the model.

Repeatability asks whether the task happens often enough to justify the setup. Recurring work—like a weekly recap or a batch of captions—is usually a stronger candidate because you can reuse the prompt, refine the workflow, and compare results over time.

Human Review asks whether a person can actually sanity-check the output in reasonable time. If you can’t tell whether a claim is true or on-brand, you can’t use the tool safely, no matter how good the copy sounds. And for anything that makes a claim or goes out under the brand's name, "human review" often means a second set of eyes from Brand or Legal, not just you — which changes how fast and how cheaply a task can really be reviewed.

The trap most marketers fall into is anchoring on Value and ignoring the other three. High value plus high risk plus weak human review is not a pilot, it's a future brand or compliance incident.

Here’s what that sounds like in practice.

• Natalie: I want our first AI pilot to be writing the product claims for our ads. Huge time save on copy.
• Chris: Value's real, agreed. But run it through the screen with me. What's the cost if it invents a claim we can't back up?
• Could be a compliance problem, or a refund-bait promise. Not great.

Your first AI workflow should be deliberately boring: a task that scores well on all four checks, with low risk if it goes sideways. Recurring internal communication is a classic fit (the weekly campaign performance recap, a content-calendar draft, a first pass at caption variants) because the value is real, the risk is contained, the task repeats weekly, and you can eyeball the output in under a minute.

Then commit to a success criterion before you start, not after. Something concrete you can measure, like "cuts my recap drafting time in half across three consecutive weeks with no metric corrections needed." Vague criteria like "saves time" let you talk yourself into a pilot that didn't actually work.

The takeaway for this unit: pick the task, not the tool, and let the AI Task Fit Screen tell you which task earns the pilot slot. The next step is a live conversation where you'll pressure-test five candidate marketing tasks against the Screen with a manager who's anchored on the flashiest option. Bring the framework by name and walk the checks out loud, that's where the skill actually gets tested.