AI-isms in Writing to Avoid: A Working List for Editors

What Is an AI-ism, Exactly?

An AI-ism is a word, phrase, or sentence pattern that shows up disproportionately often in AI-generated text compared to how frequently people actually use it in ordinary writing or speech. None of these phrases are wrong on their own — "it's important to note" is grammatically fine, and a person could write it. The problem is frequency and placement: a model trained on huge amounts of text learns that certain phrases are statistically safe openers, transitions, or closers, and it reaches for them by default whenever the sentence doesn't have anything more specific to say. Strip out the AI-isms in a piece of writing and what's usually left is a sentence with less padding around the actual point — which is why this is an editing exercise, not a vocabulary quiz. The goal isn't to memorize a banned-words list; it's to notice when a phrase is doing decoration instead of work and cut it.

An AI-ism isn't a wrong word. It's a word standing in for one the writer never had to choose.

Which Stock Phrases Show Up Most Often?

These are the openers, transitions, and closers that appear across almost every AI-drafted document regardless of topic, because they're generic enough to fit anywhere — which is exactly the problem. Below is a phrase-to-rewrite comparison for the most common offenders.

1. "In today's fast-paced world" → delete it and start with the actual subject: "Remote teams now ship code across four time zones."
2. "It's important to note that" → just state the fact: "Note:" or nothing at all — the reader assumes what follows matters because you wrote it.
3. "A testament to" → name what actually happened: "proves," "shows," or the specific result itself ("the feature shipped two weeks early").
4. "In conclusion" / "To sum up" → end on the point itself; readers can tell a piece is ending without a label.
5. "It is worth noting" / "Importantly" → cut the phrase and let the sentence that follows carry its own weight.
6. "Delve into" → "look at," "cover," or name the specific action: "we tested," "we compared," "we read through."
7. "Navigating the complexities of" → describe the actual difficulty: "three conflicting deadlines" instead of "complexities."

Why Do Models Default to These Phrases?

Language models generate one token at a time, choosing the next word based on what's statistically likely given everything written so far. Phrases like "it's important to note" and "in today's fast-paced world" appear constantly across the training data as safe, low-risk transitions — they fit almost any context without committing to a specific claim, so the model's probability distribution favors them over a riskier, more specific phrase that only fits one sentence. That's a feature for the model's training objective and a liability for the reader: a phrase that fits everywhere carries no information about the one thing you're actually writing about. This is also why AI-isms tend to travel in groups — a paragraph that opens with "In today's fast-paced world" is statistically more likely to also close with "In conclusion," because the model learned these as paired patterns rather than as independent word choices.

What Rhythm Patterns Give AI Writing Away?

Beyond individual phrases, AI-generated text has habits at the sentence and paragraph level that a reader notices even without being able to name a specific offending word. These are harder to fix with a find-and-replace pass because they're structural, not lexical.

1. Uniform sentence length: AI drafts tend to produce sentences that cluster around a similar word count, paragraph after paragraph — vary it deliberately, mixing a six-word sentence next to a twenty-five-word one.
2. The rule-of-three habit: "fast, reliable, and scalable" — three adjectives, every time. Cut it to one specific word or expand it into an actual sentence about why it's fast.
3. Hedge stacking: "can potentially help improve" stacks three hedges where one claim would do — decide what you actually mean and say that.
4. Symmetrical paragraph structure: topic sentence, three supporting points, wrap-up sentence, repeated identically for every paragraph in a piece — break the pattern in at least every third paragraph.
5. The em-dash pileup: AI drafts often reach for an em-dash to add a clause instead of restructuring the sentence — read a paragraph aloud and count them; more than one or two per paragraph is a sign the sentences need rebuilding, not just repunctuating.

How Do You Replace an AI-ism Without Losing the Point?

Cutting a phrase is the easy part. Replacing it with something that still carries the sentence's meaning — instead of leaving a gap — is where most first-pass edits fall short. A few examples show the difference between deleting and replacing.

1. Weak: "This tool offers a wide range of features that can significantly improve your workflow." Better: "This tool adds version history, inline comments, and a keyboard shortcut for every menu action."
2. Weak: "It's important to note that response times can vary depending on several factors." Better: "Response times ran between 40ms and 300ms in our tests, depending on server load."
3. Weak: "By leveraging cutting-edge technology, the company aims to revolutionize the industry." Better: "The company built its own routing model instead of licensing one, which cut latency by half."
4. Weak: "In conclusion, this approach represents a significant step forward." Better: end on the last concrete point — the number, the example, the decision — with no summary sentence at all.

The fix for a vague sentence is rarely a better adjective. It's a specific noun.

How Do You Build a Personal AI-isms List From Your Own Drafts?

The list above covers phrases that show up almost universally, but every writer — and every model — has favorites that repeat within a single document or across a body of work. Building a short, personal list from your own recent drafts catches patterns a generic checklist misses.

1. Paste your last three or four drafts into a single document and use find (Ctrl+F / Cmd+F) to search for "delve," "testament," "important to note," and "conclusion" — count the hits.
2. Search for your own repeated openers — many writers, human or AI-assisted, lean on one or two sentence starters ("In this article," "There are many ways to") without noticing.
3. Read the first and last sentence of every paragraph in isolation, skipping the middle — if they sound interchangeable across paragraphs, the structure is too uniform.
4. Keep a running list of phrases you personally catch yourself cutting more than twice in a week — that list is more useful than any generic one, because it reflects your actual drafting habits.
5. Re-check the list every few months. Habits change, and a phrase you used to overuse might stop appearing while a new one takes its place.

Does Fixing AI-isms Make Writing Read as Human?

Cutting AI-isms is a real improvement, but it's a vocabulary-level fix for what's often a structural problem. A paragraph with every stock phrase removed can still have uniform sentence lengths, predictable transitions, and generic examples — the surface-level tells are gone, but the underlying pattern that made a detector or a careful reader flag it in the first place can still be there. Think of the phrase list as the first pass, not the whole edit: after cutting the stock phrases, check the sentence-length variation and swap in at least one specific, checkable detail per paragraph — a number, a named example, an actual result. NotGPT's AI Text Detection can flag which specific sentences in a draft are still reading as AI-generated after a phrase-level cleanup, and the Humanize feature can take a second pass at sentence rhythm and word choice — with adjustable intensity — for paragraphs where trimming stock phrases wasn't enough on its own.