AI in Content Marketing: How to Get the Pros Without the Cons

Three out of four marketers say AI is more important to their strategy this year than it was last year. But only about a third of consumers say they’re using more of it themselves. Worldwide, more people say they feel concerned or skeptical about AI than excited or impressed by it.

That gap is the whole story. Marketing teams are speeding up. Audiences are pumping the brakes. And the brands catching heat right now are the ones who mistook speed for the finish line. They got content production down the stretch fast, but strategy never made it out of the starting gate.

There’s a name for what happens with that approach: AI slop. Here’s what that actually is, what it can cost you, and how to use AI without becoming a cautionary tale.

What Is AI Slop, and Why Is It Costing Brands Trust?

AI slop is content built for volume instead of value: generic, unedited, unproven, and obviously generated in 0s and 1s rather than written and processed by a human. It reads like it exists to fill a content calendar, not to answer real questions, solve real problems, or say anything that hasn’t already been said the same way a thousand times before.

People notice, and they’re not shy about it:

• Only about 1 in 4 consumers say they like or love AI-generated marketing emails or advertising.
• Nearly half say they dislike or hate AI in customer service specifically.
• 82% would rather talk to a human agent, even when the AI gives them the exact same answer in the same amount of time. 

This isn’t a fluke. A separate survey from GrowthLoop lands on almost the same 82% figure. 

The preference for humans over AI holds across every generation, not just the ones you’d expect. Gen Z picks a human over AI by 73% to 8%. Boomers pick human by 91% to 3%. If you were counting on younger audiences to be the forgiving ones, the data says otherwise — fewer than 1 in 4 Gen Z or Millennial consumers even say they’re excited about AI in the first place.

“AI empowers smarter marketing, but human creativity and empathy are essential for genuine connection. Just because we can generate endless content doesn’t mean it’s meaningful or memorable — marketing that feels personal, relevant, and real remains the ultimate differentiator.” — Priya Gill, Global Head of Marketing, SurveyMonkey

A few brands have already learned this the hard way:

• McDonald’s Netherlands pulled an AI-generated holiday ad within days of releasing it, after backlash over how flat, “creepy,” and lifeless the visuals looked. 
• Coca-Cola ran into similar trouble with its own AI-made Christmas ad, which viewers read as a “soulless” shortcut dressed up as creativity rather than an actual campaign. “Gravity Falls” creator Alex Hirsch put it more bluntly, joking that Coke’s iconic red came from “the blood of out-of-work artists.” Coca-Cola hasn’t tried it again. 
• J. Crew ran into its own version of this when an AI-generated Instagram campaign drew criticism. And Aerie turned the moment into a big opportunity, launching an explicitly anti-AI campaign built around real, human models. One brand’s stumble became another brand’s entire pitch.

Where AI Actually Earns Its Keep in Content Marketing

None of this means AI belongs in the trash. Used well, it’s a genuinely useful tool for the unglamorous parts of the job — business leaders report real improvement across the board, from content creation and SEO (93%) to analytics and insights (92%) to market research (90%). 

Stuck on campaign angles for next quarter? A content team can run fifteen headline directions past AI in an afternoon, then pick the two that actually sound like the brand and build from there. That’s a real time-saver, but the judgment call must remain yours.

The same goes for data. A mid-size retailer running last quarter’s campaign numbers through AI might catch that email opens spike on Sunday nights instead of weekday mornings, the opposite of what the team assumed going in. That’s a real scheduling fix, not a vague “AI gives you insights” claim.

And for production speed, AI can turn one case study into a blog post, three social captions, and an email draft in a single sitting, freeing up time for the parts of the job that actually need a human: strategy, editing, and making sure the final version sounds like your brand instead of everyone else’s.

Marketers already know this. 82% now use AI for content creation, but 61% say measuring its actual business impact is their biggest barrier to scaling it further. Useful and proven aren’t the same thing yet, and that gap is exactly why the next section matters. 

How Google, AI Platforms, and LinkedIn Actually Treat AI Content

Here’s the nuance that gets lost in most of the AI-and-SEO conversation: Google doesn’t penalize content for being AI-written. It penalizes content that’s low-effort, low-value, and unoriginal at scale, whether a person or a machine produced it. Google’s own guidance is direct on this point: generating pages with AI tools “without adding value for users” violates its policy on scaled content abuse.

Google also added a fourth pillar to its quality guidelines a few years back: Experience, alongside Expertise, Authoritativeness, and Trustworthiness. Raters now weigh whether the person behind a piece of content has actually done the thing they’re writing about, not just researched it. 

That distinction matters more in AI search than it ever did in traditional search. 56% of consumers now regularly read AI-generated summaries when they search, and 70% say those summaries make it easier to find what they need. 

But ease of access isn’t the same as confidence: Only 30% say they trust AI-generated search results a lot or completely; half say they usually verify the answer themselves, and 17% assume the answer is wrong by default. Unverified, unedited AI content isn’t just a quality problem anymore. It’s a trust problem with a search engine sitting in the middle of it.

We’ve written before about the specific ways this shows up in our White Hat and Black Hat SEO breakdown:

• Synthetic E-E-A-T (a fake author bio or a stock-photo “expert” standing in for real experience) 
• Authority inflation (writing “industry-leading” or “experts agree” with nobody named and nothing to back it up) 
• AI content flooding search results with thin, near-identical pages that overwhelm retrieval systems instead of helping anyone

None of those tactics are new exactly, but AI made them easier to produce — and easier for search systems to catch.

This same expectation of real, verifiable expertise is exactly why LinkedIn Articles have become a bigger part of search strategy than most B2B brands realize. Well-documented, consistent, human-attributed content earns citations in AI search more than a generic company blog post.

It’s also why LLMs so often cite Reddit over polished brand pages when answering real questions: Reddit is the single most-cited domain across ChatGPT, Gemini, Perplexity, and Google’s AI Overviews, ahead of every other site tested. Even the robots trust verifiable human experience over content that reads like it’s set to factory settings.

The Errors AI Still Makes (and What They Cost)

Deloitte proved what happens when nobody grades AI’s homework. The firm delivered a $440,000 AI-assisted report to the Australian government that turned out to contain fabricated citations and a made-up quote attributed to a real judge. Deloitte had to partially refund the contract once the errors surfaced. Now, of course, nobody purposely published fabrications. But nobody checked the data closely enough before it went out the door.

Most AI mistakes are quieter than that, but they’re the same problem in miniature. Here’s what it actually sounds like when nobody edits the output:

In today’s fast-paced digital landscape, AI is revolutionizing the way brands unlock their full potential. It’s not just a tool — it’s a game-changer, designed to seamlessly elevate your content strategy and drive results at scale. When it comes to staying ahead of the curve, leveraging cutting-edge AI solutions isn’t just an option. It’s a necessity.

Notice what just happened there? A few tells gave it away, and once you know them, they’re hard to unsee:

• The vocabulary has gone stale from overuse. Think echo-chamber words like “unlock,” “elevate,” “leverage,” “cutting-edge,” or “move the needle” — a phrase so overused now, I think we can safely assume the needle has launched itself somewhere well past an ear-splitting eleven. These words aren’t wrong, but they are worn down to sand, to the point where readers roll their eyes and assume AI wrote it, even when a person actually did.
• It also reaches for heavier words when a plainer one would do. A recent Economist analysis of leading AI models found they increasingly favor Latinate words (“significant,” “increasingly,” “consequences”) over plainer, Anglo-Saxon equivalents — what George Orwell called “pretentious diction,” decades before AI turned it into a genre.
• Every sentence runs about the same length, with nothing short and punchy to break the rhythm. The same Economist research found this pattern across every major model: long, uniform sentences that rarely interrupt themselves the way a human writer instinctively does.
• It reaches for a rhetorical crutch instead of a real detail. “Not just a tool — it’s a game-changer” is the “not X, but Y” construction AI defaults to when it has nothing specific to say.
• Nothing is attributed to anyone. Or worse, it’s attributed to something that doesn’t exist. That tracks with the Economist’s finding that AI tools quote far fewer real experts than human writers do. (I’ve personally experienced this error firsthand: On one blog project, every source URL an AI tool handed over looked completely legitimate, but not one of them actually worked on review. Chasing down real replacements took longer than writing that section 100% from scratch would have.)

That’s the tell: a polished, buttoned-up tone standing in for actual substance. It’s an easy trap to fall into, and it’s exactly the safe, standard copy AI defaults to when nobody’s editing and human contribution is absent.

How to Use AI Without Losing Your Voice (or Your Audience)

The fix isn’t complicated, even if it takes real effort:

• Edit everything AI produces before it moves toward “publish.”
• Verify every fact against a real, current source, the same way you’d want a junior writer’s work checked.
• Swap generic claims for specific ones: a real number, a real client, a real result, not “numerous” or “leading” or “best-in-class.”
• Protect a voice that sounds like an actual person made the call — not one written on an assembly line.
• Take the time to learn the tool you’re using: a class, a webinar, a video, a how-to guide. You don’t have to start from zero. Plenty of people have already gone through the blood, sweat, and tears with these systems.

One more thing worth saying: If your brand doesn’t have a documented voice and style guide to train and check AI output against, that’s worth fixing before the AI problem is. It’s hard to protect a voice nobody’s actually written down. (This happens to be exactly the kind of gap our Brand Strategy team spends its time closing.)

Solving the Human + AI Equation

Here’s what the difference actually looks like. Say you’re a family-owned HVAC company. The AI-first version of your homepage copy reads: “We’re dedicated to delivering seamless, best-in-class comfort solutions designed to elevate your home experience.” Technically true. Forgettable, and it could belong to any HVAC company in the country. 

More than forgettable, actually. Customers clock copy like that as AI-written on sight, and here’s why that’s a problem: People consistently rate AI content as less authentic, whether or not they could tell you exactly why.

The human version says something like: “When your AC dies at 9 p.m. on the hottest night of the summer, we pick up the phone.” Same company, same service, completely different level of trust, because it says something only a business that’s actually lived that moment would think to say. Better still, back it up with a real review from a real customer — proof beats a promise every time.

A voice that actually sounds like you needs a partner who’s already solved for the variable most brands get wrong: knowing the difference between fast content and good content — and why one is so much more valuable than the other.

Ready to build a content marketing and AI strategy that speeds things up without erasing what took years to build? We’ve done the math. Skip the learning curve, and reach out to Ironistic below.