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I review a lot of sales content. For our customers, for prospects evaluating Content Camel, for competitive research. And over the last year, something shifted: I started being able to tell which assets were AI generated within the first paragraph.
Not because I’m some detection genius. Because they all sound the same.
Open any LinkedIn feed and count how many posts start with “In today’s rapidly evolving landscape…” or “Here are 7 ways to…” or “The key to success is…” That’s not content strategy. That’s a content factory producing commodity output.
AI was supposed to help teams create better content. Instead, it’s helping them create more mediocre content faster. And the mediocre content is drowning the good stuff.
This is the quality problem nobody wants to talk about. The AI vendors selling content creation tools have no incentive to tell you. And the companies using those tools don’t want to admit their “AI powered content strategy” is producing the same generic output as everyone else.
Your prospects can spot AI generated content. They might not be able to say how they know, but they feel it. I’ve been cataloging these patterns for a while, and here are the ones that show up the most:
AI never takes a strong position. It qualifies everything:
“While there are many approaches to sales enablement, it’s important to consider that different organizations may have different needs…"
Compare that to a human with experience:
“Most SMB teams don’t need an LMS bundled into their content management tool. If you have 30 reps, buy a content tool and a separate training tool. Don’t pay Seismic prices for Seismic features you’ll never use."
The first says nothing. The second says something specific that might lose some readers and win others. That’s what makes it trustworthy.
AI gives you perfectly symmetrical pros and cons. Three advantages, three disadvantages, all stated with equal weight and zero opinion about which matter more.
But if you’ve actually used a product, you know that one disadvantage might be a dealbreaker for some teams and totally irrelevant for others. You know that the third “advantage” on the list is technically true but doesn’t matter in practice.
Real expertise is asymmetric. Not everything is equally important. AI doesn’t know what to emphasize because it has no experience to draw from. I think this is actually the most damaging tell. It makes your content feel like a Wikipedia summary instead of advice.
AI writes “companies have seen significant improvements in efficiency” because it doesn’t have access to your actual customer data. A human writes “our healthcare customer cut content search time from 20 minutes to 30 seconds in the first month” because they’ve actually seen it happen.
Every time your content says “significant,” “substantial,” “meaningful,” or “impactful” without a number attached, it reads as AI.
AI content describes best practices without ever saying “here’s what I’ve seen work.” It summarizes what “experts say” without being an expert. It lists “considerations” without recommending an actual course of action.
Your prospects are reading content from multiple vendors. The vendor whose content sounds like it was written by someone who has done the thing, not just summarized what other people said about doing the thing, wins their trust.
The teams winning right now aren’t the ones producing the most content. They’re the ones producing content that AI can’t replicate. And I think there are really only four categories that matter:
AI can generate a generic case study template. It can’t tell the story of how Sarah at Acme Corp was skeptical about switching from Google Drive, tried Content Camel for a week, and then told her VP “we’re not going back” after the search analytics showed 15 content gaps nobody knew existed.
Real customer stories with real names, real quotes, and real numbers are the highest-trust content you can create. They’re also the hardest to produce, which is exactly why they’re valuable (we wrote a whole piece on how to actually get them made).
“The pros and cons of AI in sales enablement” is an AI-written article. “AI for sales content: what actually works and what’s hype” is a human-written article. The difference is a point of view.
Your team has experience. Your founder has opinions. Your customers have stories. None of that can be replicated by a language model trained to avoid controversy and present balanced perspectives.
I’ll be honest: I’m biased here, because this is what I try to do with Content Camel’s blog. Take a position. Say what I actually think. Sometimes I’m wrong about things (I used to think content tagging was a solved problem. It really wasn’t). But the willingness to be wrong is itself a trust signal that AI can’t fake.
If you have data nobody else has, use it. Search analytics from your platform. Adoption patterns from your customer base. Feature usage trends. Content engagement benchmarks.
AI can’t generate original data. It can only summarize existing data. So if your content includes numbers that came from your product or your customer conversations, it’s differentiated by definition. Nobody else can produce it.
The “tiered consent framework” for case studies didn’t come from asking AI to “write about case study best practices.” It came from years of trying to get customers to agree to case studies and learning what actually works.
The “3-layer rule” for content library organization came from seeing dozens of teams set up content libraries and watching which ones broke down after 6 months.
Frameworks that emerge from experience are different from frameworks that emerge from summarizing other people’s articles. Your readers can feel it, even if they can’t articulate why.
Here’s how I’d split the work between AI and humans. And I’ll be specific, because I’ve been doing this split for Content Camel’s own content for the past year:
After AI generates a draft, ask yourself: “Would I put my name on this?” If you hesitate, it needs more human work. If you wouldn’t sign it, don’t publish it under your brand.
Here’s the thing I keep seeing, and this is where my bias shows, because it’s the problem Content Camel solves:
When your team produces 5x more content with AI, the management problem gets 5x worse. More assets in the library means more to organize, more to keep current, and more for reps to wade through. The search problem that was annoying with 50 assets becomes a real problem with 250.
So AI is actually useful on both sides of this. AI for creating content is the part everyone talks about. AI for finding and curating content is the part that actually determines whether reps use any of it. Search helps reps find the signal in the noise. Analytics show which assets are getting shared and which are gathering dust.
The best content libraries aren’t the biggest. They’re the most curated. More content is only better if you can find it, measure it, and kill the stuff that isn’t working.
Content Camel gives you AI powered search, content analytics, and aging alerts so the content your team creates (AI assisted or not) actually gets found and used. Try it free.
Related: AI for Sales Content: What Actually Works | How AI Search is Changing Content Discovery | How to Do a Sales Content Audit
Content Camel's analytics show which assets drive engagement and which gather dust. Stop guessing, start measuring.
Content Camel’s analytics show which assets drive engagement and which gather dust. Stop guessing, start measuring.
Content Camel is a sales enablement tool used for sales content management. High-growth sales teams use our system to quickly find and share the right content for each specific sales situation and measure content use and effectiveness.