AI writing tools have reshaped how content gets produced. Marketing teams use them to pump out blogs. Agencies lean on them for client work. Founders use them to outline posts, decks, and investor updates. They speed things up, give you a head start, and reduce the pain of the blank page.
But for all their utility, there are blind spots most teams arenât catching.
And those blind spots carry a cost â factual errors, bland messaging, voice mismatches, and credibility hits that compound over time.
This piece outlines the most common risks of AI-generated writing and offers a clear path for catching and correcting them before they reach your audience.
1. Factual Errors That Sound Convincing
AI doesnât “know” anything. It doesnât verify facts. It predicts what words should come next based on patterns in its training data. That means it will fabricate quotes, invent statistics, or confidently assert untrue claims if it thinks thatâs what a human would expect to see.
Take CNET as a cautionary tale. In early 2023, they quietly published dozens of AI-generated finance articles. The writing looked polished on the surface. But it didnât take long for readers to notice problems. Dozens of those articles were later corrected or pulled due to factual inaccuracies, plagiarism, and misleading financial advice.
The issue wasnât just that AI got facts wrong. Itâs that the writing passed through multiple hands without anyone questioning what had been asserted. This illustrates a broader systemic problem: editorial teams assuming surface-level polish equals substance.
Without human intervention, these outputs can easily introduce falsehoods into public discourse â and once published, those errors arenât just embarrassing. Theyâre brand-damaging.
2. Surface-Level Thinking and Generic Takes
AI tools donât generate insight. They remix whatâs already been said. That means the average AI draft is just that â average. No tension, contrarian edge, or story. Just a safe, surface-level take that fades into the noise.
Recent research from Marketing Insider Group found that AI-generated content âlacked the nuances and depth of human-written workâ â solid on structure, empty on substance.
This happens because most prompts ask AI to summarize conventional wisdom, not challenge it. But useful content isnât just accurate, itâs differentiated. If thereâs no clear POV or lived insight layered in, the writing will mirror the same industry clichĂŠs already flooding the internet. Itâll check the SEO box without earning any attention.
3. Tone and Voice Misalignment
Most AI-generated writing sounds like it came from nowhere. Itâs overly formal, casual, or just flat. Because while AI can mimic sentence structure and grammar, it doesnât intuit tone. It doesn’t understand your audience, values, or the relationship you’re trying to build.
For founders building personal brands, this is especially risky. Voice is everything. If your content sounds like a template, you come across as disconnected or inauthentic.
Tone misfires are often subtle. A too-polished paragraph may feel insincere, while a casual joke might undermine trust. What AI canât do is judge when a message feels off for the moment, the medium, or the reader.
This means every draft requires voice-level editing: not just grammar and clarity checks, but real alignment with how you speak, what you stand for, and how your audience listens.
4. Cultural Blind Spots and Bias
AI mirrors its training data, and that data is biased. That means AI will unknowingly reinforce stereotypes, overlook marginalized perspectives, and default to Western-centric language and examples. Itâs not malicious, merely uncritical.
Youâve probably seen the examples. A supermarket chatbot (Google AI) that suggests glue-based recipes. A chatbot that downplays hate groups. When content isnât reviewed by a culturally aware human, the consequences go beyond awkward phrasing to damaging trust.
AI lacks context around race, class, geography, and most users arenât prompting or reviewing for those layers. Unless diverse reviewers are actively editing for inclusion, the final output may contain blind spots that alienate parts of your audience without you even realizing it.
5. Filler Content and Fluff
AI loves padding. It will write 800 words of filler when 300 words would be sharper. It leans on corporate cliches, bloated intros, and meaningless transitions to hit its word count. It writes like a junior copywriter trying to impress a manager who only skims headlines.
Watch for phrases like:
- âIn todayâs ever-evolving landscapeâ
- âDelve deeper intoâŚâ
- âUnlocking potentialâ
- âGame-changing solutionsâ
These phrases take up space and make you sound like everyone else. Trim your content often to remain readable.
6. Ethical and Strategic Risks
AI creates an illusion of completion. You enter a prompt, get 800 words back, and it feels “done.” But that draft hasnât passed through any of the filters a human would normally apply: intent, accuracy, nuance, and ethics.
The LA Times ran into this with its AI-generated article summaries. The writing looked clean but misrepresented key facts because there wasnât a clear handoff between machine output and human oversight.. Readers caught it and trust took a hit.
In some cases, the ethical lapse is internal. Teams quietly use AI without disclosing it. Or worse, they remove bylines to avoid accountability. This creates a culture where no one owns the message.
A Human-in-the-Loop Workflow That Works
A good AI writing process includes human judgement at every step. Hereâs what that actually looks like:
1. Start with a creative brief. Before you even prompt the AI, define what the piece is for, who it’s speaking to, and what take or insight it needs to carry. This can be three to five lines that outline audience, intent, tone, and POV. Paste that into your prompt so the AI isnât guessing.
2. Feed tone samples into the prompt. Donât ask it to “sound human.” Show it what that means. Include a few examples of your previous (ideally pre-AI) writing, or your founderâs voice. You can also include guardrails like: “Avoid phrases like âdelveâ or âgame-changing.â Keep it tight and opinionated.”
3. Use structured prompts, not vibes. Be direct. Say something like: âWrite a 600-word blog post for B2B marketers explaining why AI-generated SEO content is failing. Use a skeptical but not cynical tone. Include a real example from the previous year.â Thatâs one way to avoid generic outputs.
4. Build a checklist for review. Once you have a draft, check it against a clear set of questions. Are the facts verified? Is the tone consistent with your brand? Does the piece say something new or just echo whatâs already out there? If you’re working with a team, write this checklist down and use it consistently.
5. Assign named reviewers. Someone should be responsible for each layer of review, not just âsomeone.â Assign names for fact-checking, tone alignment, and final sign-off. Accountability makes quality scalable.
6. Post-edit with a purpose. Donât just fix grammar. Replace templated intros with specificity. Add a quote from your founder. Pull in real data, a campaign result, company stories, or customer language. Thatâs the layer AI canât fake.
7. Log repeat issues. If the model keeps making the same mistake â bad transitions, stat hallucinations, tone drift â tweak the prompt. Build feedback loops into your process so each draft improves the next.
8. Set your AI disclosure policy early. For internal notes, this might not matter. But for journalism or investor comms, it does. Decide when and where to disclose AI use now, not after trust takes a hit.
Final Thought on Blind Spots of AI-Generated Writing
AI is useful. But itâs not magic, and itâs not a writer. It doesnât care about your brand, nuance, or reputation. Thatâs your job.
Use AI to reduce the friction of starting, structure your ideas and get rough drafts out faster.
But if you publish without editing, checking, or reworking it with real thought, youâre not scaling your content. Youâre scaling your mistakes.If you need help building a human-in-the-loop system that catches what AI misses, we do this every day at Column. Get in touch.
Johnson is a Content Strategist at Column. He helps brands craft content that drives visibility and results. He studied Economics at the University of Ibadan and brings over years of experience in direct response marketing, combining strategy, creativity, and data-backed thinking.
Connect with him on LinkedIn.
