In a world where AI is helping every brand create faster, what your content sounds like is sometimes more important than what you are actually trying to say. 

Your tone. Your rhythm. Your bite. Your charm. 

What makes your brand, your brand.

AI can enhance this. It can also dilute it. Most AI-generated content today sounds like it was written by a moderately caffeinated robot with a thesaurus and a limited understanding of nuance. Why? Because AI defaults to the average. It creates a mean-sum product of everything it has access to. So if you don’t give it the right parts, the end product ends up undifferentiated and dreadfully average. 

The future of content isn’t just about using AI to create more. It’s about training AI to write and create like you, efficiently. Here’s how to make that happen:

1. Build a Brand Voice Dataset

Step one: stop expecting AI to “get” your voice out of thin air.

Instead, feed it a dataset of content that is your voice: your top-performing blog posts, email campaigns, social copy, internal docs, pitch decks, podcast and meeting transcripts. Anything that reflects how you actually talk to customers when you’re on fire.

What to do:

• Collect 10–20 strong samples
  
• Annotate them: tone, structure, favorite phrases, personality cues
  
• Upload into AI tools like Jasper, Writer, or OpenAI’s Custom GPTs
  

Think of this like teaching a band to play your music. You don’t hand them sheet music from a competitor and hope for the best: you give them your demo tapes.

2. Create a Living Style Guide (and a “Do-Not-Say” List)

Training AI isn’t just about telling it what to say—it’s about telling it what not to say.

That’s where your style guide and banned-phrase list come in. This is your brand’s verbal immune system, protecting your voice from clichés, cringe, and competitor copycats.

What to include:

• Tone of voice rules (“We sound like X, never like Y”)
  
• Grammar and spelling preferences (“email,” not “e-mail”)
  
• Taboo terms (buzzwords you hate, words your competitors use)
  

No AI can intuit your tone unless you’ve documented it clearly—and made it enforceable.

3. Fine-Tune AI on Your Proprietary Content

If you want your AI to act like a team member, you have to train it like one.

Modern platforms (like OpenAI’s Custom GPTs or Anthropic’s Claude) allow you to fine-tune large language models on your content—not just generic internet text.

How it works:

• Upload internal documents, sales scripts, product copy, customer support chats
  
• Add clear instructions: “Use a bold, conversational tone. Avoid passive voice. Prioritize clarity over cleverness.”
  
• Review outputs and retrain based on real feedback
  

This is how your AI assistant graduates from “meh” to “magic.”

4. Human-in-the-Loop Feedback Loops

AI still gets it wrong—even with meticulous training (just like humans). Which is why humans stay in the loop, serving as a balance-check to AI performance.

Think of your content review team as your brand’s voice coaches. They tweak, refine, and—most importantly—train the AI to do better next time.

How to implement:

• Set up a workflow where every AI draft gets reviewed by a brand expert
  
• Capture their edits as training data
  
• Run periodic “voice audits” to catch drift
  

5. Train Your AI to Not Sound Like Everyone Else

Want to stand out? Train your AI to intentionally avoid sounding like your competitors.

You can upload competitor content as negative examples and flag it as “off-brand.” That way, your AI learns to steer clear of industry-speak, watered-down platitudes, or language that sounds like it came from a marketing Mad Libs generator.

And this isn’t just a tone preference—it’s strategic positioning. According to Jasper’s guide on brand voice, your voice is “a defensive moat that, once cultivated, is hard to replicate.” In a landscape where generative AI can reproduce anything, the only thing it can’t fake well is you.

To make that defensibility stick, Matrix Marketing Group suggests including both positive and negative training data. This helps your model learn not just how to sound like your brand, but how to avoid sounding like the brand next door.

6. Duolingo: A Case Study in Brand Voice (The Good, the Bad, and the Silent)

For years, Duolingo was a masterclass in brand voice. The deranged owl. The unhinged TikToks. The “we’ll kill your streak” push notifications. It was weird, witty, and wildly effective.

But when Duolingo announced a shift to an “AI-first” strategy—replacing contract writers with generative AI and rolling out 148 courses, the vibe shifted. Fast.

Users weren’t inspired. They were pissed. They flooded comments calling it dystopian. Duolingo deleted its social posts and disappeared. When it returned, the content felt…off. Detached. It wasn’t quirky. It was confusing.

The lesson? You can’t build a brand on irreverence and then ghost your audience when they’re looking for accountability.

What went wrong here wasn’t just a technical misstep—it was a voice crisis. The brand’s silence and tonal dissonance broke trust. It’s a cautionary tale: if you don’t protect your voice during a major transition, AI won’t save you. It might just accelerate the backlash.

That said, Duolingo is starting to re-engage. If they re-embrace their tone and bring users into the journey, the rebound story writes itself. But it only works if the brand finds its voice again and trains its AI to speak in that language, too.

The TL;DR:

AI is not your brand’s voice. You are.

Train your tools. Define your tone. Treat your AI tools like partners, extensions of your own brain (not a replacement). If you need help scaling bespoke brand buoying AI systems…you know where to find me.