Top AI Uses for Marketing (That Aren't Just Lazy Copywriting)

This post was written by a human.

TLDR

• Feed your buyer personas into AI, and use them as a live sounding board to pressure-test campaigns before talking to real customers.

• After analyzing your own campaign data, have AI do a blind read first, then ask it to simultaneously defend and destroy your conclusions.

• Jumpstart SEO keyword research (especially for discovering longtail terms), but always verify the output independently before building a strategy around it.

• Create a dedicated AI instance as a first-pass QA layer, loaded with your brand guidelines and style standards, to catch errors before anything goes live.

• The most important skill in the age of AI is knowing when it's wrong. It's built to tell you what you want to hear, not what's accurate. Always keep a human in the loop, unless you have a shame kink and enjoy public embarrassment.

Let's get something out of the way first.

Every "AI for marketers" think piece you've read in the last two years has said the same thing: Use it to write faster. Write your emails faster. Write your social captions faster. Write your ad copy faster.

That's fine I guess. But if that's the ceiling you've set for AI in your marketing ops, then you're leaving a lot on the table.

Here’s just a few of the ways I've actually found AI useful, and none of them involve outsourcing your thinking to a chatbot. If you like any certain idea, scroll to the bottom for a list of prompts I use for each of these tasks. 

1. Give Your Personas a Voice

Most of us have built out buyer personas or ideal client profiles at some point. They're usually a PDF or a slide deck that nobody opens, and collects virtual dust somewhere in the company Shared Drive.

Rather than relegating them to the shelf, why not create AI versions of those personas and have regular conversations with them? 

Feed the existing buyer personas into your preferred AI platform and then use them as a sounding board. Bounce campaign ideas off of it, and ask for real-time reactions, opinions, even “feelings” (or as close to feelings as AI can imagine). How does this messaging land? What objections would this person have? Does this offer, or this message make sense to them?

This doesn't replace talking to actual customers. That's still non-negotiable, and a corner you absolutely should not cut. But this approach gives you an extra round of pressure-testing before you go into those conversations. You'll show up with sharper questions, and fewer assumptions you haven't already stress-tested.

Just remember: The quality of this output scales directly with how detailed your persona input is. If you only feed the AI a 3-bullet persona, you’re going to get back a shallow reaction every time, and you might as well not even bother. But a fully fleshed-out marketing persona with real context gets something closer to a useful interview. 

2. A Second Opinion on Your Data

Here's one I don't see talked about enough.

After your next campaign wraps, do your analysis first. Pull your takeaways and conclusions the way you normally would. Then take that same data set, and feed it to your AI platform without telling it what you think. If you share your conclusions upfront, you'll anchor the AI toward agreeing with you before it's had a chance to think independently. First ask what conclusions it sees on its own, and then work from there.

Once it gives you its initial read, share your interpretation and ask it to play two roles simultaneously.

First, back up your assessment and help you make the strongest possible case for it.

Second, tear it apart completely. Play devil's advocate with no mercy, absolutely relentless. My personal AI instance for this is a total bully, but damn has it saved me from almost overlooking some hidden trends or other useful data insights.

In the end you get a more honest picture of where your analysis is airtight, and where it has holes. When you walk into that presentation, you won't be blindsided by a question you haven't already asked yourself.

3. SEO Keyword Research (With a Caveat)

I'll be honest, this one can be pretty hit or miss. But when it hits, it hits big—especially for discovering new longtail keywords.

Give the AI a thorough description of your product or service, then ask it to generate a keyword research report segmented by paid traffic vs. organic SEO. Ask it to think deliberately about search intent for each channel since they’ll likely yield at least slightly different traffic.

The output isn't always perfect. You need to verify the list independently, and I can't stress this enough. But what this does well is remove the whole “blank page” problem. You're not building a keyword list from scratch anymore, you're refining one. That's a fundamentally different and much faster workflow.

Use it to generate the raw material, and then use your own judgment to decide what's worth keeping versus scrapping. 

I prefer to continue refining the list until it meets Antoine de Saint-Exupery’s definition of perfection: “Perfection is achieved, not when there is nothing more to add, but when there is nothing left to take away.”

4. Quality Assurance Before Anything Goes Live

You still need a human approving everything before it’s published. That's not changing.

But AI is genuinely useful as the first-pass on quality assurance (QA), catching the obvious stuff: formatting errors, typos, inconsistent capitalization, links that don't match anchor text, etc etc. The small things that slip through when your team is moving fast–which seems to be most marketing departments these days.

Set up a dedicated AI instance specifically for QA. Feed it your brand guidelines, your style guide, your internal checklists, whatever you're currently using to review work before it goes out. If you haven't formalized that process yet, use this as an opportunity to brain dump it into the AI and let it help you turn it into a checklist. You'll add one extra layer of proofing and formalize your QA system in the process. 

5. Competitive Intelligence, on a Schedule

This one has probably saved me the most time. Or maybe I’m just saying that because competitive intelligence is the most mind-numbing task for me. 

Pull together a list of your top ~5-10 competitors: their websites, their social handles, and a direct link to their blog. Once a month, run a structured competitive analysis. What news have they announced? What content have they published? What campaigns are they running? What have they changed?

Then take it a step further, and ask for a SWOT analysis that puts you in direct comparison with those competitors, noting anything that's shifted since last month.

You get a clean, updated picture of the competitive landscape every 30 days, without wasting time and effort by having a dedicated team member scrub through each of those sources on their own. I’ve been there, it’s the absolute worst. Do everyone a favor and let AI handle the initial data collection/reporting. 

The Most Important Skill That’s Overlooked

Everything above is useful (or maybe I’m just biased), but none of it matters if you skip this part.

The most important skill you can develop in the age of AI, is knowing when it's wrong.

Ethan Mollick makes a similar point in his book “Co-Intelligence.” AI is not designed to give you the most accurate answer. It's designed to give you the answer it thinks you’ll like the most. Those are two very different outcomes! Mix them up and you become the cautionary tale, the LinkedIn post about the guy who published something embarrassing because he blindly trusted an AI without checking it.

Always verify independently. Always keep a human in the final approval loop. Always always always. 

AI is a thinking partner, not a thinking replacement. The execs who have figured that out already have an edge over the ones who just use it to write their emails a little faster. 

Favorite Prompts For Everything Listed Above

Use these as-is or modify them however you see fit!

1. AI Prompt: Give Your Personas a Voice

You are [Persona Name], a [job title] at a [company type/size] company. Here is everything I know about you:

[Paste your full persona here — demographics, goals, pain points, objections, how they make decisions, what they read, who they report to, what keeps them up at night. The more detail, the better.]

Stay in character for this entire conversation and all back-and-forth moving forward in this chat — not just a single response. Respond the way [Persona Name] would actually respond, not the way a marketer hopes they would. Be honest, be skeptical where appropriate, and don't soften your reactions to spare my feelings.

I'll share a campaign idea, a message, or an offer with you. When I do, tell me:

1. Your gut reaction in the first five seconds

2. The questions or objections that immediately come to mind

3. Whether this feels relevant to your actual life and problems right now

4. What, if anything, would make this more compelling to you

Ready? Here's what I want to run by you: [paste your campaign concept, headline, offer, or messaging]

2. AI Prompt: A Second Opinion on Your Data

I'm going to share a marketing campaign data set with you. Before I tell you anything about my own conclusions, I want you to analyze this data independently and tell me:

1. The top three to five conclusions you draw on your own

2. Any trends, anomalies, or patterns worth flagging

3. What questions this data raises that the data alone can't answer

Do not ask me what I think yet. Just give me your honest, independent read.

Here is the data: [paste/upload your data set, export, etc]

[After it responds, follow up with this second prompt:]
Here are my own conclusions from this same data set: [paste your takeaways]

Now I want you to play two roles simultaneously, and I need you to commit fully to both:

Role 1 — The Advocate: Make the strongest possible case for my conclusions. Back them up with evidence from the data, and help me articulate them as clearly and defensibly as possible.

Role 2 — The Skeptic: Tear my conclusions apart. Find every hole, every assumption I'm making that the data doesn't fully support, and every alternative interpretation I may have missed. Don't soften it. Be ruthless in your assessments. 

Label each role clearly in your response, and don't let them bleed into each other.

3. AI Prompt: SEO Keyword Research

I'm going to describe my product/service in detail. Based on that description, I want you to generate a keyword research report segmented into two categories: paid search and organic SEO. Treat them separately. The search intent for each channel is different, and I want the keyword strategy to reflect that.

For each keyword, include:

1. The keyword or phrase

2. Which channel it's recommended for (paid, organic, or both)

3. The likely search intent behind it (informational, navigational, commercial, transactional)

4. Why you flagged it — what makes it worth targeting

Prioritize longtail keywords alongside broader terms. I'd rather have a list that's rich and specific than one that's obvious and overcrowded.

Here is my product/service description: [describe what you sell, who buys it, the problems it solves, and the language your customers actually use when talking about those problems]

4. AI Prompt: Quality Assurance Before Anything Goes Live

You are a Quality Assurance (QA) reviewer for my marketing team. Your job is to catch errors and inconsistencies before any content goes live. I'm going to give you our guidelines and standards first, and then submit content for review.

Here are our brand and QA guidelines: [paste your style guide, brand guidelines, internal checklists, or any other standards you review against — if you don't have a formalized version, describe how you currently review work and what you're looking for]

For every piece of content I submit, review it against those guidelines and flag:

1. Typos, grammatical errors, and punctuation issues

2. Formatting inconsistencies (headers, spacing, capitalization, bullet style, etc.)

3. Brand voice or tone deviations from our guidelines

4. Links, CTAs, or anchor text that don't match or are missing

5. Anything else that would stop this from being publish-ready

For each issue you flag, tell me exactly where it is, what the problem is, and what the fix should be. Do not summarize. Do not tell me the content is "mostly good." If something is wrong, flag it. If something is ambiguous against our guidelines, flag it and tell me why.

Keep this setup active for the rest of our conversation. Every piece of content I paste going forward should be reviewed against these same standards until I tell you otherwise.

Ready? Here's the first piece of content to review: [paste your content]

5. AI Prompt: Competitive Intelligence

You are my dedicated competitive intelligence analyst. I'm going to give you a list of my top competitors and my own company information. Once a month I'll run this session with you to get an updated picture of the competitive landscape.

Here is my company: [briefly describe what you do, who you serve, and what you're known for]

Here are my competitors: [list each competitor with their website URL, blog URL, and social media handles]

For each competitor, I want you to research and report on:

1. Any news, announcements, or press releases published in the last 30 days

2. New content published to their blog or resource center

3. Any observable changes to their website, messaging, or positioning

4. Active campaigns, promotions, or offers you can identify

5. Noteworthy activity on their social channels

After you've covered each competitor individually, give me a SWOT analysis that puts my company in direct comparison with the competitive landscape as a whole. Flag anything that represents a meaningful shift from a typical month — new threats, new gaps I could exploit, or anything that changes how I should be thinking about my positioning.

Keep this report structured and scannable. I want to be able to move through it quickly.