Artificial intelligence is rapidly transforming the content creation landscape. Advanced AI language models can now generate coherent articles, blog posts, news summaries, and all sorts of written content on virtually any topic within seconds.
It‘s a revolutionary development with huge potential to streamline content workflows and costs. According to a 2021 survey by Semrush, 12% of marketers already use AI writing tools and 21% plan to in the future. But adoption is not without its challenges.
AI-generated content is not a panacea—at least not yet in its current state. There are pitfalls and limitations you need to carefully navigate to ensure high quality and editorial integrity. Misusing AI writing tools can lead to unintended issues that damage the content‘s usefulness, originality, and reputation.
In this comprehensive guide, I‘ll break down the 8 most common problems that can arise when using AI to write articles based on my expertise in AI and SEO writing. I‘ll share real-world examples, data-driven insights, and best practices to help you harness this game-changing technology while sidestepping the hazards.
Pitfall 1: Algorithmic Biases
One of the most pernicious pitfalls of AI writing is the risk of bias inadvertently creeping into the generated content. AI models learn to write by analyzing patterns in vast datasets of human-written text. If those datasets contain demographic biases, the AI can pick up on and reproduce those biases.
For example, researchers found that GPT-3, one of the most prominent language models, exhibits biases regarding gender, race, and religion based on its training data. It more often associated men with words like "brilliant" and "leader" while correlating women with "emotional" and "home."
This algorithmic bias can lead to AI-generated articles that perpetuate harmful stereotypes and discriminatory language. A ProPublica investigation revealed that crime prediction algorithms used by law enforcement were falsely flagging Black defendants as future criminals at twice the rate of white defendants.
To mitigate bias, AI writing models must be proactively trained on diverse, representative, and ethically curated datasets. Tools like StereoSet can help detect and quantify biases in language models. Human oversight is also critical for identifying and correcting biases in AI-generated content.
OpenAI, the creator of GPT-3, acknowledges this risk. Their CEO Sam Altman tweeted: "We worry about bias and misuse of the tech, but ultimately I believe the benefits will be far greater than the downsides."
Pitfall 2: Lack of Originality
Another major pitfall is that AI-generated content often lacks true originality. While AI writing tools can cleverly rearrange and combine ideas, they don‘t come up with fundamentally new concepts on their own. Their output is based on statistical patterns in pre-existing data.
Mark Schaefer, a renowned marketing futurist, warns about this limitation: "The problem is that machines are not capable of original thought. They can only pattern-match and create content based on what has come before. They can‘t imagine a truly new idea."
As a result, AI-generated articles can sometimes feel generic, templatic, or derivative—rehashing familiar information without adding much unique value or insight. This is especially problematic for thought leadership content marketing that hinges on fresh perspectives.
To counter this, I recommend seeing AI-written drafts as a starting point for human creativity rather than the final product. Use the AI output to quickly generate a foundation of ideas and structures, then have human subject matter experts build upon it with their own analysis, examples, and insights.
Providing the AI with highly specific, information-dense prompts can also help elicit more unique and relevant raw material to work with. The sweet spot is AI-assisted creativity, not pure automation.
Pitfall 3: Factual Inconsistencies
Accuracy is another core challenge with AI-generated content. While AI language models are remarkably proficient at stringing together convincing prose, they don‘t have a native mechanism for fact-checking their output against ground truth.
An AI can confidently make false assertions that seem plausible on the surface. We saw a prominent example of this when Google‘s Bard AI inaccurately claimed that the James Webb Space Telescope took the first pictures of exoplanets, an error that was quickly debunked by astronomers and cost Alphabet $100 billion in market value.
The root problem is that most large language models do not have built-in knowledge bases or information retrieval systems. They generate text based on probabilistic patterns in their training data, which can have a high noise-to-signal ratio and lack up-to-date information.
Anthropic, an AI safety startup, cautions: "Language models are not reliable sources of factual knowledge. Due to their training process, they can generate statements that seem plausible but are not actually true."
To ensure factual accuracy, it‘s imperative to rigorously fact-check and edit AI-generated articles against authoritative primary sources. Explicit instructions to cite reputable references can also help ensure content is grounded in verifiable information.
Pitfall 4: Oversimplification of Complex Topics
AI writing also struggles with highly technical, nuanced, or sensitive topics that require deep domain expertise. Legal contracts, scientific papers, medical advice, and financial reports are prime examples where current AI tools fall short.
While AI writing assistants can help experts in these fields with research and first drafts, the AI alone cannot reliably handle the full complexity and precision required. Subtle errors or omissions can have major consequences.
Dr. Jennifer Schaffer, a medical doctor and clinical professor, shared her concerns about AI-generated health content: "AI language models may oversimplify complex medical information or provide advice that is not appropriately tailored to an individual‘s specific circumstances. This could lead to misdiagnosis, improper treatment, or unsafe recommendations."
Similarly, AI-written legal contracts may not fully capture all the necessary clauses, caveats, and contingencies that a skilled attorney would include. AI-generated financial analysis may not account for the myriad of variables and market dynamics that human analysts consider.
For these high-stakes topics, AI is best used narrowly as an assistive research and writing aid for experts, not as an authoritative content generator. Heavy human oversight and editing by subject matter experts are essential to fill in information gaps and correct misinterpretations.
Pitfall 5: Formulaic Writing
Because AI writing models learn from patterns in existing data, there‘s a tendency for them to pick up on and mimic common sentence structures, turns of phrase, and information sequences. The output can read as quite formulaic and repetitive if not carefully prompt-engineered.
For example, a lot of AI-generated news articles follow a rote structure: a headline, a one-sentence summary, a few paragraphs of background, maybe a quote, and a tidy conclusion. They often lack the narrative flair and surprising hooks that characterize compelling human-written features.
Similarly, AI-written product descriptions can lean heavily on templatic feature-benefit phrases like "This [product] is perfect for [use case] because of its [attribute]." The result is generic, undifferentiated copy that blends into the competition.
Creativity and novelty arise from unexpected combinations of ideas—something AI models can approximate but struggle to nail consistently. Truly memorable writing often artfully breaks conventions rather than playing it safe.
To avoid falling into well-worn content patterns, focus AI writing tools on creative ideation and outlining rather than end-to-end automation. Have human writers remix and expand upon the AI‘s suggestions to find uniquely captivating angles and narratives.
Pitfall 6: Inconsistent Style and Tone
Maintaining a distinct and recognizable brand voice is crucial for effective content marketing. However, AI writing assistants can struggle to reliably conform to highly specific style guidelines and tones across different projects.
Out of the box, most AI writing tools are generalists trained on a broad corpus of web content. Their default outputs can therefore sound quite dry and impersonal—more like "general internet writing voice" than your unique brand.
Ross Hudgens, the founder of Siege Media, observes: "The issue with AI content is that while the writing can be grammatically correct, it lacks the verve of personality. People want to engage with content that has a strong, specific point of view. AI simply cannot replicate the realities of a personal brand."
Considerable prompt engineering and customization is required to tune AI models to your desired writing style. You need to feed them many representative examples of your brand‘s content and give detailed instructions on attributes like vocabulary, sentence structure, emotional tone, and reading level.
Even then, the style and tone may wobble and need to be smoothed out by human editors. AI content is unlikely to be publish-ready right out of the gate without manual review and revision to align it with your brand standards.
Pitfall 7: Missing Human Context and Empathy
Another significant pitfall of AI writing is the lack of human context and emotional intelligence. AI models operate based on statistical correlations in textual data; they don‘t truly understand the real-world experiences and feelings of the people who will read their outputs.
As a result, AI-generated writing can come across as a bit artificial and tone-deaf at times. It may gloss over or omit important contextual nuances that make the content relatable and resonant for the target audience.
For example, an AI writing about job interview tips may focus generically on surface-level advice like "dress professionally" and "practice answers to common questions." A human writer with first-hand experience can enrich that with insider anecdotes, hard-won insights, and an empathetic understanding of job seekers‘ anxieties.
Sonia Simone, the former Chief Content Officer of Copyblogger, emphasizes this human element: "The best content marketing is empathetic. It demonstrates that you understand your audience‘s challenges, desires, and emotional triggers. AI can approximate this, but it‘s not the same as lived experience."
Additionally, readers may feel duped and lose trust if they discover an article that resonated with them emotionally was actually generated by AI. Transparency and human authorship can go a long way to forge genuine connections.
Counteract the empathy gap by having human writers weave their authentic voice and experiences into AI-generated articles. Hybridizing the speed and scale of AI with the warmth and relatability of human storytelling is a powerful combination.
Pitfall 8: Lack of Durability
In today‘s crowded content landscape, articles need to do more than simply regurgitate surface-level information to stand out. They need to deliver original insights, analysis, and ideas that have a lasting impact on readers.
However, current AI writing tools largely excel at summarizing existing information rather than contributing durable new knowledge to a field. Their content may be factually accurate and fluently written but ultimately forgettable because it doesn‘t push the conversation forward in novel ways.
Steph Smith, an entrepreneur and content strategist, wrote about this durability challenge: "What makes content last is insight. Insight that changes how people understand a topic or opens up new ways of thinking. It‘s a high bar that most AI writing falls short of today."
The most enduring content tends to come from deep subject matter expertise and hard-won experience—the kind of original primary source material that AI models can learn from but not conjure up on their own.
This is why thought leaders like Tim Ferriss or Paul Graham remain so popular. Their writing doesn‘t just condense a topic for easy consumption; it reframes core assumptions and offers surprising mental models you didn‘t know you needed.
To create content with long-term compounding value, focus AI tools on research, outlining, and idea generation to accelerate your own expertise-driven writing. Interview internal and external thought leaders to uncover novel insights that an AI isn‘t privy to.
Best Practices for AI-Assisted Writing
Now that we‘ve covered the major pitfalls to avoid, here are some best practices for effectively integrating AI into your writing workflow:
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View AI outputs as a starting point to refine, not publish-ready articles. Fact-check information against authoritative sources and edit for style, tone, and narrative flow.
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Provide AI with specific, relevant, and data-rich prompts to elicit more unique and substantive article outlines and ideas.
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Tune AI models with examples of your brand‘s content guidelines and target audience to better align outputs with your needs.
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Focus AI on research, outlining, and light writing tasks, not replacing thought leadership or subject matter expertise. Hybridize the speed of AI with the depth of human insight.
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Be transparent with readers when AI played a significant role in an article‘s creation to maintain trust and set appropriate expectations.
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Monitor AI writing tools‘ outputs for potential biases, inconsistencies, and quality issues. Implement human oversight systems to catch any errors.
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Continuously provide feedback on AI outputs to improve and customize performance over time. AI writing is a long-term investment.
The Future of AI-Generated Content
Despite the challenges covered here, I firmly believe that AI writing will become an indispensable part of the content creation process in the coming years. The efficiency and scale benefits are simply too great to ignore.
As generative AI technology continues to advance rapidly, issues like biases, factual inaccuracies, and lack of originality will likely diminish. AI writing tools will grow more reliable and sophisticated, with built-in fact-checking, richer knowledge bases, and more controllable outputs.
However, AI will not replace the need for human writers and subject matter experts anytime soon. The best use case in the near-term is AI-assisted writing—leveraging AI to augment and accelerate human creativity, not fully automate it.
I just don‘t see how anyone can expect AI to replace human writers within the next 10 years. Content creation is going to be increasingly collaborative between humans and AI.
— Chris from Anthropic (@anthropicxyz) December 11, 2022
Forward-thinking writers should embrace AI as a powerful tool to level up their work rather than fearing it as an existential threat. Learn its quirks and limitations so you can wield it effectively while sidestepping the hazards.
As Chris from Anthropic put it: "Content creation is going to be increasingly collaborative between humans and AI." Those who learn to harmonize the power of AI with their own creative gifts will have a massive competitive advantage.
The future of content is AI-augmented, not AI-only. Master the dance of human and machine creativity to produce deeper, richer, and more impactful writing than ever before. Be aware of the pitfalls but don‘t let them deter you from the vast potential.
