The best genAI content is verified and scrutinized by humans.
Generative AI can make content marketing faster, cheaper, and better. But using AI creates risks.
First, the benefits. Content marketers can move from idea to outline to draft in minutes. Teams that once struggled to publish consistently can produce more articles, product descriptions, and email newsletters.
AI also lowers the cost of experimentation, enabling tests of topics, formats, and distribution channels. The final bonus might be better content. A study from The New York Times, for example, found that readers often preferred AI-assisted writing to human versions.
Unfortunately, genAI content is imperfect. The most common AI content mistakes are workflow problems from marketers compressing research, writing, editing, and publishing into a single step or even just a few. The result can be content that looks polished but lacks verification, originality, or editorial review.
Moreover, articles occasionally appear with visible prompt instructions or responses, such as “Here is your human-sounding blog post.” These are failures of people, not AI, from copying output without review. The faster the workflow, the easier to copy.
Here are common generative AI mistakes content marketers should avoid.
Mistakes to Avoid
Assuming AI read the page
Content marketers often paste URLs into prompts and expect the model to consume the page exactly as humans. Yet many websites block AI crawlers, place content behind subscriptions, or restrict access in ways that limit retrieval.
When that happens, the model may rely on partial information, metadata, earlier training, or inference. The output may nonetheless sound persuasive, but it is not necessarily complete or reliable.
Content marketers should provide the model with the actual content or confirm that it can read the page.
Trusting AI without fact-checking
Clean text can make weak information seem reliable. A content marketer using AI for research or drafting articles may encounter fabricated statistics, unsupported conclusions, incorrect dates, or invented quotes. The errors become especially problematic in ecommerce content where product claims, market data, and pricing references influence purchasing decisions.
To avoid errors in AI-generated content, marketers should incorporate fact-checking into any AI-assisted workflow. A useful principle is to assume every AI-generated factual claim needs confirmation or a source.
Letting AI replace research
AI can accelerate research but not replace it. A common pattern is asking AI for article ideas, then an outline, and finally a draft. The result adds nothing unique to every other site covering the topic.
Kieran Klassen is an Amsterdam-based software engineer and co-creator of Cora, an AI tool for managing email communication. In an “AI & I” podcast episode last month, he stated, “LLMs are very good at just following steps, doing deep work, working for hours or days, even now. What’s left for flesh-and-blood humans are the steps before and after — the planning, where you frame the problem, and review, where you determine whether the output feels right.”
AI might reveal sources, but humans must still read and confirm.
Publishing reworded ideas without attribution
GenAI typically repurposes and plagiarizes original text rather than quoting it verbatim. Models can absorb common arguments, paraphrase recognizable frameworks, imitate examples, and reorganize ideas without clear attribution.
The resulting article may appear original while remaining heavily dependent on someone else’s thinking. Search engines and other genAI platforms likely digest the plagiarized piece, proliferating the problem.
Originality is essential to meaningful content, and matters even more in 2026.
Generative AI
Generative AI platforms are upending content marketing in mostly positive ways. But it comes with challenges and requires careful implementation. The marketers who benefit most will likely not generate more content but will instead exercise discretion in its use.
