Hello Reader,
A recent post in r/marketing collected dozens of AI-generated posters from different companies.
Same blocky type, painted illustrations and crowded layout, even when the companies had nothing in common.
At the start of this week the post had 932 upvotes and 250 comments.
People had opinions.
Some defended the posters as an effective way for small organizations to make something presentable. Others said they’d started scrolling past the style because every poster looked the same.
CEOs want more campaigns, more content and more speed, often without accepting lower standards.
Which means your team is expected to produce faster and better, while still protecting the brand and not looking like every other AI poster on the web.
So how do you avoid falling into the AI slop trap when the pressure keeps increasing?
To find out, I talked with Lauren McHugh, the head of marketing at Docket.
Her eight-person team just built an original and delightful campaign for Docket’s new frameless agent, and they did it all in two weeks.
She walked me through how, where and why they used AI, where humans stayed in the loop and why they waited a week after the product was ready to do the actual launch.
Her process reveals four decisions every marketing team and agency needs to make as AI shortens product timelines and adds go-to-market pressure.
Product Is Ready, but is Your Campaign?
When I led marketing for a SaaS company, we’d start planning a major product launch months in advance.
We’d build the positioning, creative, media plan, emails and sales materials around a release date. Marketing would often be ready while engineering was still working through the final details.
It was frustrating, but it also gave us plenty of time to prepare. I realize I took those days for granted.
Now, AI is completely reversing that relationship.
Product and engineering teams can build and release features much faster, which leaves marketing teams with less notice, shorter production windows and more requests competing for support.
How to Market at Hyperspeed
Docket's new agent sits directly on the page instead of inside a separate chat widget. It fundamentally changes how visitors interact with a product and is the first of its kind. Lauren's team wanted the launch to reflect that.
The only catch? They had two weeks to do it.
The first decision the team made was decoupling feature availability from the launch itself. This only gave her team an extra week of working time, but it was enough to create the strategy, draft the assets and run through the approvals.
"Engineering can release all day, but we're not going to make noise about it until we're ready to,” Lauren says.
When I told her I thought that timeline was still remarkably fast, she said her team couldn't have done it without AI.
To give her team an advantage, Lauren built a Claude agent that monitors Docket’s internal communication channels for signs that a product release is getting close. The agent surfaces relevant updates and keeps them posted on product and feature progress.
“It’s really helpful to understand the development cycle, without having to be so deep in the release notes,” she says. “That way we can start executing the minute we know a launch is imminent."
When the agent flagged the new product, Lauren knew it was time to kick off the campaign development.
“We were discussing the next evolution of these agents,” Lauren says. “Some of them sit in widgets and you have to click on it and it's not seamlessly part of the page.”
Her team brainstormed ideas around the agent becoming a seamless part of the page overall.
“We were thinking about calling it a frameless agent and our lead content writer said, “what if we have her jumping out of a picture frame?” and that was it, we knew we had the idea.”
From there, the team used AI as a partner for production and execution.
Identifying signals: They used AI to pull patterns and data points from their research to inform campaign direction.
First-pass ideation: Lauren said they often let AI do the first iteration of brainstorming, and then the team jumped in for refinement.
“Shitty first drafts.” They use AI to generate rough first drafts of campaign materials, then handed back to the humans for editing and refining.
AI supported post-launch monitoring. This included tracking campaign status, performance, and gaps for the next round.
And Lauren's system works.
She reported a 70% increase in website traffic after launch, and the CEO’s LinkedIn post received more than 100 comments.
“We can monitor campaign performance with a dashboard we built with Claude Code and Vercel,” Lauren says. “It’s connected to HubSpot, Google Analytics and Semrush. We even pull in our paid ad performance so we know immediately when a campaign is working.”
What to do when AI slop slips through
Lauren’s team shows how you can speed up campaign production with AI, but it doesn’t always go according to plan.
Content quality is difficult to maintain 100% of the time.
During one busy week, AI-generated copy went directly to Docket’s web developer without the proper review and the developer published it.
Each person thought someone else had completed the necessary check and passed it along the review chain.
Lauren addressed the issue with the full team.
She didn't single anyone out. She framed it as a process problem and set clear expectations for how she expected work to move between roles.
“If you're producing content with AI you need to check that it’s on brand before you send it to leadership for review,” Lauren says. “Everyone uses AI. We want you to use AI, but it shouldn't look like it came from AI when it gets to our desk.”
The four decisions behind a better launch
Lauren's team moved fast, but they made four decisions along the way that protected the quality of the finished work.
You can use the same framework for your own launches.
1. Signal
How do we know what’s getting ready to ship?
Create a clear source for upcoming releases, product changes or production dependencies. That could be a shared channel, weekly review or agent-generated update.
The goal is enough notice to decide what support the release needs.
2. Schedule
When is it ready for prime time?
Consider audience timing, industry events, channel availability, team capacity and the size of the release.
Product availability can come first, but the campaign should launch when the story and market timing are right.
3. Shape
Which parts of the campaign require human judgment?
People should own the customer problem, story, creative direction, brand expression and final selection.
AI can support research, drafts, variations, summaries and reporting. The team still needs someone with enough context and taste to decide what deserves to move forward.
4. Sign off
Who carries responsibility for the finished work?
Name the person who checks the claims, copy, visual treatment and customer experience before publication.
Define what needs to happen before the work reaches the website, media platform, client or sales team.
Lauren's team figured this out together, in the open, by naming the problem and agreeing on new rules. Your team can do the same thing.
Where is AI creating the most rework for your team right now? Hit reply and tell me. I’d love to know where your biggest bottleneck is.