AI-generated marketing content fails in predictable ways. The failures are lexical (specific words the model overuses), structural (patterns the model falls into), and voice-level (the content reads generic even when the facts are right). Fixing all three is the difference between AI-assisted marketing that works and AI-assisted marketing that quietly burns your reputation.
The three failure modes
- Lexical: specific words and punctuation that scream AI.
- Structural: sentence and paragraph patterns the model prefers but humans rarely use.
- Voice-level: content that feels "from nowhere." No specific experience, no concrete number, no point of view.
Lexical tells
The words and punctuation most indicative of AI writing:
- Em dashes at unusual frequency. The single strongest tell. Ban them.
- "Leverage" used as a verb. Replace with "use."
- "Robust." Replace with "solid" or "reliable."
- "Seamless." Replace with "smooth" or remove.
- "Navigate" (metaphorical). Replace with "handle."
- "In today's fast-paced landscape." Delete entirely.
- "Whether you're X or Y." Delete or rewrite.
- "Delve," "unveil," "embark." Delete.
A word-level scrubber that replaces these before publishing is the first line of defense. It is not sufficient, but it catches the most obvious cases.
Structural tells
Structure is harder to fix because it requires rewriting, not replacing. The patterns to watch for:
- The tri-colon: "not A, not B, but C." Models overuse this.
- The hedged superlative: "arguably one of the most..." Pick a lane.
- The false dichotomy intro: "In a world where X, founders face a choice." This is filler.
- The paragraph-ending summary: every paragraph ending with a restatement of the paragraph topic. Humans do not do this.
The scrubber + critique pattern
The pattern that works in production has two stages:
- Generate with voice examples. The first prompt includes 3-5 real posts in the founder's voice. The model pattern-matches to those.
- Scrub lexical tells. Deterministic replacements for the words above. Cheap, fast, always run.
- Critique pass. A second LLM call, with an editor persona, reads the scrubbed draft and rewrites anything that still reads generic. This is the step most systems skip, and it is the one that catches the structural failures.
Each stage catches different failures. Running only one is worse than running none, because you get the false confidence that you have addressed AI-tell issues.
What actually works
Beyond the mechanical fixes, the single highest-leverage change is adding a specific experience or concrete number to every post. "We tried X and saw Y" beats "X is a better approach" every time. AI content sounds generic because models default to the median example; forcing it to use the founder's actual data cures most of it.
The autonomous marketing guide covers where voice enforcement fits in the broader loop. Our GEO guide covers how voice-failed content also fails AI engine citation, not just human readers.