We need to talk about AI content creation.
Everyone’s doing it. Fewer people are doing it well.
I spent last year’s AI Lab-a-thon building content multiplication systems for Wiley’s marketing campaigns. The promise was simple: create once, deploy everywhere, reach more people with less effort. Sounds brilliant, right?
Here’s what I learned: AI doesn’t multiply your impact. It multiplies whatever you feed it.
Feed it mediocre content? You get mediocre content at scale. Feed it generic messaging? You get white noise that nobody remembers. Feed it content without strategic intent? You’re just adding to the internet’s landfill.
The Real Question Isn’t “Can AI Do This?”
It’s “Should this content exist at all?”
Before I even think about multiplying content across channels, I ask three questions:
- Does this solve a real problem for our audience? Not “does this fill a content gap in our calendar.” Not “do we need something for LinkedIn this week.” Does this actually help a publishing director make a better decision? Does it answer a question that keeps editorial teams up at night?
For Research Exchange, that meant focusing on content around manuscript screening, research integrity, and AI-enhanced editorial workflows, the topics our audience are genuinely wrestling with. Not more generic “transform your publishing process” fluff.
- Can a human defend every claim in this content? AI is brilliant at structure and synthesis. It’s terrible at nuance and truth.
When we developed our webinar follow-up content on data harmonisation, I could have let AI generate all the nurture emails. Instead, I used it for structure and first drafts, but every insight, every example, every claim came from real conversations with our colleagues and webinar panellists who understand our solutions inside-out.
If you can’t look a prospect in the eye and defend what’s in your content, don’t multiply it. You’re just scaling inauthenticity.
- Does multiplication actually serve the strategy? Not everything needs to be everywhere. Some content deserves long-form depth. Some needs to be a single, perfectly-timed message.
We hit our campaign goals for last year. That wasn’t because we created more content than everyone else. It was because we created the right content in the right places for the right people.
Sometimes that meant turning one webinar into a LinkedIn carousel, email series, and thought leadership blog post. Other times it meant leaving a single case study to do its job without diluting it across twelve formats.
The Framework: Before You Multiply Anything
Here’s how I decide if content deserves multiplication:
Stage 1: Does it pass the pub test? If you couldn’t explain this content’s value to a colleague over coffee without using marketing jargon, it’s not ready. Real value is simple to articulate.
Stage 2: Is the core message strong enough to adapt? Good content has one clear insight that can flex across formats. Weak content tries to say everything and ends up saying nothing. If you can’t summarise your key message in one sentence, you don’t have content worth multiplying.
Stage 3: Do the channels actually matter to your audience? I don’t care if TikTok is trendy. Our publishing directors aren’t there. But they are in their inboxes, on LinkedIn, and reading industry publications. Multiply where your audience actually lives, not where marketers tell you to be.
Stage 4: Can you maintain quality at scale? This is where most AI content strategies fall apart. You can generate 50 social posts from one whitepaper. But can you ensure all 50 are accurate, on-brand, and genuinely useful? If not, you’re better off with five great posts than 50 mediocre ones.
The “AI Content Slop” Trap
You know it when you see it:
- Listicles with generic tips that could apply to any industry
- “Thought leadership” that sounds like it was written by a committee of robots
- Social posts that use phrases like “unlock potential” and “drive innovation” without saying anything specific
- Content that’s technically correct but utterly forgettable
AI makes it so easy to create this stuff. And the internet is drowning in it.
The antidote? Specificity. Examples. Real stories. Actual data from your campaigns. The stuff only you can write because only you’ve lived it.
When we launched Research Exchange’s “Built by Wiley. Powered for you” messaging, AI helped us adapt it across channels. But the positioning itself, the understanding that our partners wanted recognition for their work, not just our technology, that came from human insight. You can’t automate empathy.
What This Means If You’re Earlier in Your Career
If you’re a few years into marketing and wondering how to keep up with the AI content arms race: don’t.
Instead, become the person who:
- Understands your audience better than anyone else
- Can spot the difference between content that looks good and content that works
- Knows when to use AI and when to trust your own brain
- Builds systems that scale quality, not just quantity
The marketers who’ll thrive in 2026 and beyond aren’t the ones creating the most content. They’re the ones creating content people actually remember.
AI is a tool. Use it like one. Don’t let it use you.
Your Turn
What’s one piece of content you’ve seen recently that made you stop scrolling? I guarantee it wasn’t AI slop. It was something specific, something real, something that only that person or brand could have created.
That’s what we should be multiplying.
The views expressed in this article are solely my own and do not represent the opinions or positions of my employer.
LAB Marketing 