---
title: Keeping Your Content Authentic When AI Writes It
description: How to maintain brand trust and content authenticity while using AI for marketing. Practical strategies for human oversight, voice preservation, and quality control.
date: February 5, 2026
author: Robert Soares
category: ai-strategy
---

A friend sent me a newsletter last month. Something felt wrong. The sentences were smooth, the advice was sensible, the structure was clean. But it read like wallpaper. I scrolled to the end without absorbing anything, then closed the tab and forgot I had ever opened it.

That newsletter probably took ten minutes to produce because AI generated most of it, and that speed came with a cost that the author may not have noticed but every reader felt in their bones.

## The Plastic Feeling

On Hacker News, a user named temp00345 captured something essential about AI-generated writing in a [discussion about Medium's AI policy](https://news.ycombinator.com/item?id=34544487):

> "At the core of it, people write in order to transmit some deeply distilled messages about life."

They went on to note that generated text carries a "plastic feeling" distinct from human work.

That phrase has stuck with me. Plastic feeling. It describes something real. You know it when you encounter it. The words are correct. Grammar is fine. The information might even be accurate. But something essential is missing, something that makes you want to keep reading, something that makes you feel like another person is speaking to you rather than a very articulate spreadsheet.

The challenge for anyone using AI in content creation is figuring out what creates that plastic feeling and how to avoid it without abandoning the genuine productivity gains that AI offers, which are substantial and getting better every month.

## Why This Matters More Than It Used To

Content volume has exploded. AI made publishing easy. The result is a flood of competent but forgettable writing across every platform, every industry, every topic imaginable.

Readers are developing immunity. They scroll faster. They skim more aggressively. They abandon articles after two paragraphs because nothing has surprised them or connected with them or made them think this particular piece is worth their attention.

Research from the [Nuremberg Institute for Market Decisions](https://www.nim.org/en/publications/detail/transparency-without-trust) found that when consumers learned content was AI-generated, they rated it as less natural and less useful. The kicker? The actual content was identical to human-created versions. Perception alone changed the evaluation.

So you face two problems. First, if your content reads like AI wrote it, people will disengage even if they never consciously identify why. Second, if people discover AI involvement, they may retroactively devalue what you wrote even if it was genuinely useful.

## The Distinction That Matters

There is a difference between AI-generated content and AI-assisted content, and that difference is everything.

AI-generated content is what happens when you type a prompt, copy the output, and publish with minimal changes. The plastic feeling is inevitable because there is no human voice shaping the final product. The AI is writing. You are copying.

AI-assisted content is different. A human decides what to say. The AI helps say it faster. The human shapes, edits, adds, removes, and makes the final product their own. The AI accelerates. You still write.

One user in a [Hacker News thread on AI writing detection](https://news.ycombinator.com/item?id=35259214) named inciampati put it this way:

> "I'm feeling overwhelmed by 'ChatGPT voice'" and hoped society would "continue to value unique, quirky human communication over the smoothed-over outputs of some guardrailed LLM."

The smoothed-over outputs. That is the plastic feeling again. AI optimizes for average. It produces text that is acceptable to the largest possible audience. That means removing edges, flattening personality, defaulting to safe and sensible and utterly forgettable.

Human writing has texture. Opinions that not everyone shares. Sentence structures that break patterns. Words that feel specific to one person rather than generated by statistical probability.

## Voice Is The Whole Game

Your voice is what makes your content recognizable and memorable, and AI does not have one.

AI can mimic voices if you give it enough examples, but mimicry is not the same as having something to say, and readers can feel the difference even when they cannot articulate it.

Kim Klassen, a writer who has thought carefully about this problem, [describes the approach simply](https://www.kimklassen.com/blog/ai-writing-01): use AI as a co-thinker, not a ghostwriter. A collaborator that helps you work through ideas, not a replacement that thinks for you.

She also identifies the core danger:

> "Your voice is a beautiful, irreplaceable part of your creative expression. It's what makes you. YOU."

When you let AI generate without heavy editing, you are giving away the thing that makes your content yours. You might save time. You will lose differentiation.

## Where To Draw The Line

The editing versus generating distinction creates a spectrum rather than a binary, and where you draw your line depends on what you are creating.

For internal documentation, process guides, and reference material, heavy AI generation is fine. Nobody reads these for voice. They read them for information. Clarity matters more than personality.

For thought leadership, opinion pieces, and brand-building content, minimal AI generation makes sense. These formats exist specifically to showcase human perspective. Using AI to generate them defeats the purpose.

For everything in between, the answer is judgment. How much of this piece depends on sounding like a specific human with specific views? That question tells you how much human editing the AI output needs.

A practical test: if you could publish this exact text under someone else's name and nobody would notice, you have not added enough of yourself. The content might be fine. It will not build a relationship with readers.

## The Process That Works

Here is what actually preserves authenticity when using AI for content.

**Start with a point.** Before you prompt anything, know what you want to say. Not the topic. The argument. The specific insight. The thing that makes this piece worth reading. If you cannot state it in one sentence, you are not ready to write.

**Write the hard parts yourself.** The opening paragraph, the key examples, the specific opinions. These carry your voice. Let AI fill in around them rather than generating the skeleton you hang your voice on.

**Prompt specifically.** Generic prompts produce generic output. Instead of "write about email marketing," try "write about why most B2B email marketing fails from the perspective of someone who has watched dozens of campaigns underperform." The constraints shape better output.

**Edit aggressively.** Every sentence should survive the question: would I say this? If not, rewrite it. Look for the patterns that signal AI wrote it. Excessive hedging. Corporate vocabulary. Sentences that all sound the same. Conclusions that are too tidy.

**Add what you know.** Specific numbers from your experience. Real examples with names and details. Opinions that some people will disagree with. The things AI could not possibly invent because they come from your life and work.

**Read it aloud.** If you stumble over a phrase or it sounds unnatural coming out of your mouth, it will read that way too. Your voice has rhythm. AI output often has monotonous patterns that your ear will catch before your eyes do.

## The Fact Problem

Authenticity requires accuracy. One wrong fact destroys credibility faster than a hundred bland sentences.

AI makes things up. Not occasionally. Regularly. The technical term is hallucination, which sounds clinical but describes something serious: AI will generate plausible-sounding claims that have no basis in reality.

[According to IBM](https://www.ibm.com/think/topics/ai-hallucinations), AI hallucinations are false or misleading information presented as fact. A lawyer was sanctioned for submitting AI-generated legal briefs with fabricated case citations. The cases did not exist. The AI invented them because they sounded like they should exist.

Every statistic in AI output needs verification against original sources. Every quote needs confirmation that someone actually said it. Every claim about products, competitors, or markets needs fact-checking against current information.

This verification takes time. Skipping it trades short-term efficiency for long-term reputation damage. One caught error teaches your audience to doubt everything else you say.

## Detection Is Coming

The infrastructure for identifying AI-generated content is developing rapidly, and assuming you can pass off AI content as human-written is increasingly risky.

[Google's SynthID](https://deepmind.google/models/synthid/) has watermarked over 10 billion pieces of content. The [Content Authenticity Initiative](https://www.webpronews.com/2026-ai-video-detection-advances-combat-misinformation/), backed by Adobe and others, is building standards for tracking content origins.

The [EU AI Act](https://artificialintelligenceact.eu/article/50/) will require marking AI-generated content in machine-readable formats by August 2026. Transparency is becoming law, not just ethics.

This does not mean you should avoid AI. It means the goal should never be hiding AI use. The goal is ensuring that AI assistance produces genuinely valuable output regardless of whether someone knows how it was made.

## The Emotional Depth Gap

Tom Shapland, building a tool that generates social posts from voice interviews, noted on Hacker News that ["the hardest part has been making the social posts feel like they weren't written by an LLM."](https://news.ycombinator.com/item?id=41724993)

Another user, cwbuilds, agreed: "Have been having the problem of LLMs sounding too boring and corporate too."

This tracks with a common observation: AI handles information transfer competently but struggles with emotional texture. It can explain why something matters but cannot make you feel why it matters.

One Reddit writer quoted in a compilation of feedback described the problem precisely: "Every time ChatGPT tries to write a grief scene, it sounds like a Hallmark card."

Emotion requires specificity. The particular detail that makes a reader recognize their own experience. AI generalizes. It produces something that could apply to anyone, which means it resonates with no one in particular.

This is why adding your own experiences to AI output matters so much. Not generic examples. Your examples. The things you actually saw, felt, learned, regretted, celebrated. That specificity is what AI cannot generate and what readers actually connect with.

## Brand Voice At Scale

Organizations face a harder version of this problem. Individual voice is relatively easy to maintain because you know what sounds like you. Brand voice across multiple writers and thousands of pieces of content is significantly harder.

Documentation helps. Not aspirational brand guidelines that nobody reads, but practical reference material showing exactly what the brand sounds like. Vocabulary lists. Example sentences. Passages that nail the voice alongside passages that miss it.

Feed this material to AI when prompting. "Write in this style" works better when accompanied by examples of what that style actually sounds like.

But documentation only goes so far. Someone needs to be the voice guardian. A person who reviews AI-assisted content specifically for whether it sounds like the brand rather than generic AI output. This role matters more than it used to because the default output of AI tools is becoming recognizable. That ChatGPT voice is a tell.

## What Success Looks Like

Authentic AI-assisted content passes a simple test. Readers should not think about whether AI was involved. They should engage with the ideas, connect with the voice, and take away something worth remembering.

If your content sounds like it could have come from anywhere, you have not added enough of yourself. If it sounds like one specific person or brand wrote it, you have done the work.

The metric is not whether you used AI. It is whether the result was worth publishing. Worth reading. Worth sharing.

AI handles the blank page problem. It generates raw material faster than any human could. That is genuinely valuable.

But raw material is not content. What you do with it determines whether readers feel a human presence on the other end of the words or just the plastic feeling of text that nobody really wrote.

The distinction matters because readers can tell. Maybe not consciously. Maybe not consistently. But over time, they will gravitate toward the voices that feel real and away from the ones that do not.

Your job is to be one of the real ones.