AI Drafts Start Around 15% Human on Detection. Here's What Moves the Number.

The question every content team asks before committing to an AI writing tool is simple: does the output actually pass AI detection?

We asked the same thing, so we ran a systematic test. Several categories of AI writing tool, scored against AI detection on identical prompts. The results are below, including our own numbers, which are not always flattering.

The Setup

Prompt used: a 1,200-word blog post on how small businesses can build a content marketing strategy on a limited budget. Same prompt, same parameters, across every tool.

Detection: each output was scored with a widely used AI detector at its standard settings, with paragraph-level scoring, and cross-checked against a second detector where available. The detector returns a probability that the text is AI-generated. We report the inverse, "% human," because that is the number these tools show users.

All tests were run in April 2026.

The Results, by Category

We are not going to name and rank other vendors. The useful comparison is not tool against tool, it is approach against approach.

Approach	AI detector (% human)
Raw foundation-model output, no humanization	12-18%
General AI writing tools, default output	19-31%
Enterprise writing suite, fine-tuned	around 41%
NeuraWrite, draft only, no humanization	16%
NeuraWrite, after the humanize step	89%

The pattern holds regardless of which brand produced the draft. Tools that stop at "generate and hand you the text" land in the low range. Fine-tuning lifts the number somewhat but still flags. The thing that moves it decisively is measuring and revising after the draft, not the draft itself.

What These Numbers Mean

Before you read "89% human" and assume we cherry-picked: we ran this test 15 times across different topics and got a range of 74% to 94% after the humanize step, averaging 83%. The 89% above is from the marketing-strategy post.

The number that matters is not the single best result, it is the floor. Our floor was 74%. Raw output without humanization averaged 14% across the same 15 runs.

14% → 83%

Detection score, before and after

Raw AI output vs. the same content after a humanize-then-score loop, averaged across 15 runs in our testing.

That gap, 14% to 83%, is the product.

Why Raw AI Output Fails Detection

AI detectors look for a few structural signals. None of this is secret, and understanding it is the whole game:

Perplexity. How predictable is each word given what came before. Models generate high-probability sequences by default, and human writers make less expected choices. Low perplexity across a full document is a strong AI signal.

Burstiness. Human writing varies sentence length and complexity in recognizable patterns, a long, complex sentence followed by a short one, then a medium one. Raw AI output is more uniform, and some detectors weight this heavily.

Vocabulary patterns. Certain phrases show up constantly in AI text and almost never in human writing. Detection models have trained on millions of examples and know these patterns cold.

Raw output scores low because it is fluent, coherent, and completely predictable. A humanization pass increases sentence variation, replaces the phrasing detectors key on, and reworks the most machine-characteristic transitions. We are not going to publish the exact recipe, but the direction is what you would expect: make the structure less uniform and less predictable without changing what the piece says.

What "Passes Detection" Actually Means

A caveat worth stating plainly: AI detectors have meaningful false-positive and false-negative rates. Published data shows they sometimes flag human writing as AI, and they can be challenged on the same grounds.

"Passes detection" does not mean content is undetectable in every context or to every reviewer. It means the signal detectors use to flag content is not present at threshold levels in the output. For professional work, agency deliverables, B2B posts, thought leadership, the test is typically an automated detector rather than a human reader, and that is the specific test these numbers address.

The Loop That Closes the Gap

NeuraWrite's raw draft is comparable to other foundation-model output on detection. The difference is what runs after the draft:

Humanize. Rewrite the draft to reduce the structural signals detectors flag, without changing the meaning.
Score. Run detection and quality checks automatically, and return a composite score.
See the score before you publish, not after a client sends you a screenshot.

That is the loop. Research, draft, humanize, score, then you decide. The teams that will have detection problems are the ones who stop at the draft. The content looks fine, the draft is coherent, and the score they never checked is 14%.

Run it on a document you already have. If your existing content scores below 60%, that is a workflow gap worth closing before someone else finds it for you.