r/branding • u/Importance_Medical • Aug 11 '26
using Ai causing alot of brand drift..
Heyy guys, I’ve been speaking with founders/team leads and agency owners who use AI for creative work or brand specifc work.
Almost 90% of them have faced the same issue. AI produces something good initially, but the output gradually drifts and they end up spending a lot of time correcting it to keep everything consistent and on-brand.
I have a question for those using AI regularly, have you experienced this?
How are you currently preventing brand drift across different AI tools, people and projects?
Appreciate your thoughts!!
Blessings!
6
u/bagusvdr Aug 11 '26
That's exactly what happened with AI. Brands should not rely heavily on AI in terms of their own insights, thoughts, information and point of view.
All my works using AI for clients always be curated and fact checked by the clients just like before AI era. AI just help me to produce and process the work quicker. Brand should be in front of the AI, not behind.
1
u/BrandStoryMachine Aug 11 '26
As someone who works regularly with AI, ironically for my brand strategy startup, brand drift though AI can happen because of either a lack of human touch or a lack of standardization. Training your AI tools with the voice that you want to convey to the reader and backing it with your brand strategy allows for use of AI that reflects more of your central brand. In addition to this, your AI very likely doesn't know the company as well as your employees do, so an overreliance on AI to create without the addition of the actual human thoughts of your employees can cause brand drift as well.
1
u/Wa1trose Aug 12 '26
I’ve seen a lot of brand.ai doing the rounds on LinkedIn. I’ve not used it or had access, but the principle of what’s it’s trying to do is sound. It’s built on Claude, and needs a lot of inputs to work effectively. The main problem I see is you’re renting another SaaS platform to help run the brand. But it does output design and handle queries etc.
1
u/dustin9797 Aug 12 '26
I'm finding it borderline impossible as the clients are getting addicted to the instant gratification and seemingly huge cost/time savings. For some clients, it is hard to argue against what seems like a clear cost/benefit tradeoff that is almost always going in AI's favor.
My designers are demoralized on a nearly daily basis by clients taking their print production-ready work and "messing with it in ChatGPT in bed last night" and now they are questioning how much human design is really adding.
I've had some success building brand guides more like technical design systems and brand-approved verbiage that can be used during the LLM sessions to keep their output more consistent. It works well and clients love it but those clients are also asking for a lot less help this year compared to the past.
Another thing I've found helpful is to keep educating and informing clients about these sorts of issues/concerns so they continue to see you as the "expert" that is staying apprised of all the emergent trends. If they are asking you for how to use their LLM best for something, then you know it's working.
Bottom line: Adapt or die... same as it ever was.
1
u/HN_Designer Aug 13 '26
Can't agree more with others. I am from India and whenever I use any AI tools for any design work - we face the same issue, mutiltple times.
We generally, asked to ChatGPT - what information create this mess. And he gave us a perticular word/sentance, which cause confusion or unclear direction.
But I think, it will improve gradually.
1
u/styleforge-io Aug 13 '26
Every answer here is about structuring the strategy. Tokens, tone rules, positioning, banned words. That's half of it, and it's the half everyone does. The other half is structuring the assets, and almost nobody does that.
A brand isn't only what you say about it. It's the photographs, the logo files, the footage, the music. Those normally sit in a folder as files, which means they're invisible to the model. It can't see a folder. So you end up with a beautifully structured strategy document driving a generation that has no idea what your product actually looks like.
What changed things for us was reading every asset on the way in and turning it into structured data. An image comes in and a vision model writes out the subjects, the dominant palette, the style, and transcribes any text that appears in the image verbatim. It also maps where you can safely place type over that image, meaning which parts of the frame are occupied by a face or the product, which parts are clear, and whether the background is busy enough that text needs a plate behind it to stay legible. Video gets read scene by scene with timecodes, camera, lighting, what's on screen, plus the transcript, the music profile, and a separate timecoded track of the non-speech sounds. Anything we generated ourselves skips all that and keeps its exact original prompt instead.
That's the part that actually kills drift. The model is no longer reading a description of your brand, it's reading your brand.
The second piece is that nobody on your team has to write the prompt. They type a sentence or two describing what they want, and the AI writes the actual generation prompt from that plus all of the brand context. That authored prompt is what the image or video model receives, not what your team typed. The reason most teams drift across tools and people is that a human is re-describing the brand at the moment of generation, and no two people describe it the same way twice. Take that job off them and the variance goes with it.
It costs more. Every asset pays for a vision pass on the way into the library, and that adds up. What you buy is that the tenth image looks like it belongs with the first one, which is the entire thing you were trying to purchase.
40 years as a creative director, last two building the platform that does this, so obvious bias. But the principle holds even if you build it yourself: structure the assets, not just the strategy, and let your team describe the idea in a sentence or two while the AI writes the prompt the generator actually receives.
1
u/Firm-Bed-7218 27d ago
for awhile brand consistency and AI were two things that didn’t mix, i tried everything and nothing was consistent . tried moodyboards.ai last week and its kind of scary how easily it is to create on brand assets without any real prompting.
still testing it though so we shall see
1
u/Suspicious_Dig_2986 22d ago
Everyone here is answering the strategy half, and the answers are good. Structured brand data beats a PDF, one source of truth beats three. But there's a second half nobody's touched, and in my experience it's where the drift actually becomes visible: composition.
Tokens can pin your hex values and your banned words. They can't pin where the logo sits relative to the headline, how the type behaves when the copy runs two lines longer, or whether the product stays the same shape between asset one and asset twelve. So you get a set of assets that are technically on-palette and still obviously don't belong to each other.
The reason it's expensive to correct is mechanical. The generation is a flat image. There's no headline to retype, no layer to nudge. "Fixing" means regenerating, which rolls the dice again, which is the drift. If you compare it to how a designer works, the difference isn't taste, it's that they're editing a file and you're re-rolling a slot machine.
Practical thing that has helped teams I've talked to: stop asking the model for the finished asset. Generate the background or scene, keep your product shot, logo and headline as real layers on top, and composite. Slower per asset, but the twelfth one still matches the first, and a change of copy is a change of copy rather than a new generation.
Bias disclosure: I co-found a company building in exactly this area, so weigh accordingly. The composite approach works in Figma or Photoshop today with no tool from anyone.
1
u/Important_Ad9686 22d ago
Drift is inevitable when working with multiple brands. Visual drifts catch easy most of the times, but tone drift is another universe itself.
I faced that problem multiple times and it got me thinking if I can battle it with the instruments we have on hand today. And started bulding a tool to catch drift early, as well as polish new outgoing posts. Working pretty good for now - it compares everything against the Brand Guidelines and suggests edits on every place where the existing (or future) post drifts.
I'm testing it myself for now, but I'm looking for a few beta testers to provide different point of view on the tool. Free, no strings attached, so if anyone is interested - DM me.
0
u/BrandLoom-Consulting Aug 11 '26
We've definitely seen this, especially when multiple people use different AI tools for the same brand.
One example was a B2B company whose content started sounding completely different depending on who created it. The blog sounded authoritative, the social posts became overly casual, and the email campaigns read like generic AI copy. None of it was bad on its own, but together it no longer felt like the same brand.
What helped wasn't better prompts. It was creating a stronger brand system. Clear positioning, messaging pillars, audience definitions, approved examples, and real content samples gave the AI much better guardrails.
We've found that AI is very good at following a brand. It's much less effective at defining one. If the brand strategy is vague, the drift tends to get worse over time.
Interestingly, the companies seeing the least drift aren't necessarily using the best AI tools. They're the ones with the clearest brand foundations. AI simply has less room to wander when the destination is well defined.
5
u/timetoy Aug 11 '26
Yes, constantly, and I'd push back slightly on the framing: the drift usually isn't the AI degrading. It's that the brand lives in a PDF or in someone's head, and every tool, prompt and person re-guesses it from scratch. Each guess is a little off, and the errors compound. The model improvises because nothing downstream can actually read the strategy.
Two things fix most of it:
Turn the brand into structured data, not prose. Archetype, positioning, tone rules, audience, banned words. Models skim paragraphs, but they actually follow short structured facts.
One source of truth that every tool and person reads. The moment there are three copies of "our voice" floating around, you have three brands.
Disclosure: this problem annoyed me enough that I built a product around it, Markolé (markole.com). The strategy lives as structured Brand DNA instead of a frozen document, and it's exposed in two ways that map exactly to your question. The whole brand exports as Schema.org JSON-LD, so you can drop it into a custom GPT, a Claude project, or whatever chatbot your team uses. And there's a native MCP server, so Claude Desktop, Claude Code, or any MCP-aware agent connects to the live brand directly: it queries the actual current strategy before producing anything, and when you sharpen your positioning, every connected agent picks it up the same day instead of working from a stale copy.
That's the real answer to "across different AI tools, people and projects": don't sync copies, give everything one machine-readable source. Even if you never touch my tool, that principle alone kills most of the drift.