Last Friday, we hosted an AI Powered Workplace webinar for our clients on a problem more teams are starting to feel: AI-generated content is getting faster, but it is not always recognizably yours.
Teams can now draft campaign copy, sales follow-ups, executive updates, proposals, FAQs, enablement content, and service communications in minutes. Some of it reads well. Some of it could have come from any one of your competitors.
We opened the session with our own experience. After rolling out multiple AI tools, draft time improved. First-pass quality improved. The blank page stopped blocking people! Then the hidden cost showed up: voice, specificity, and the details that make work sound recognizably ours. Fixing tone after the fact often took longer than writing the piece from scratch.
The output wasn’t obviously wrong. It was just missing the details that make work sound like us. It created a surprising amount of rework because teams kept trying to improve content that should have been rewritten from the start.
The issue usually sits in the operating model. Brand guidance lives in PDFs. Tone rules rely on words like “professional” and “authentic.” People prompt from memory. Each tool gets different context. Nobody owns upkeep, so review becomes the correction layer.
Researchers now have a name for the cost: workslop, AI output that looks polished and carries little substance. BetterUp Labs and Stanford Social Media Lab found that roughly 40% of U.S. desk workers had received it in the prior month, and 42% viewed the sender as less trustworthy afterward. Brands pay for that lack of trust.
In our AI Powered Workplace session on building an AI brand standard, the attendees reacted most strongly to one question: what in this AI-generated follow-up could only have come from your company? The honest answer was almost nothing. The draft was grammatical, friendly, and completely interchangeable. No meeting detail. No recommendation. No owner. No next step.
Use that as the test: if the logo changed, would the work still sound like you?
If AI makes the output look slick, but nobody can tell it came from you, you still have a brand problem.
-Ashley Pyle, Chief Experience Officer, GadellNet
This is why we do not start with prompt libraries. They help, but they do not solve the real problem. Teams need a practical operating layer that makes the right brand behavior easier to repeat than the shortcut.
How Do We Cut Down on Workslop?
1. Inventory what AI needs to know.
Start with what the tool actually needs to know: how the brand sounds in practice, which words you use and avoid, what your buyers expect, what good work already looks like, and where review is required. Most organizations have these pieces scattered across decks, PDFs, old campaigns, and people’s heads. The pre-work is getting them into one usable source.
2. Translate vague brand words into observable instructions.
This is where most brand standards fail AI. “Use a professional tone” sounds clear until six people interpret it six different ways. Better instruction looks like this: “Lead with the answer. Use plain language. Support claims. Remove hype.” If two reviewers would argue about whether the rule was followed, the rule is not done yet!
3. Configure the standard where work already happens.
The standard has to live where the work happens. Standing memory and pasted prompts will drift. Put the approved instructions, source files, examples, and review criteria inside the tools your team already uses: Brand kits, templates, grounded files, or agents in Microsoft Copilot; Projects and Skills in Claude; Projects, approved reference files, and workspace GPTs in ChatGPT.
We used the same approach to build the webinar. The deck started as a Word outline, then PowerPoint generated a branded presentation from our approved template, fonts, colors, and structure. We still reviewed and adjusted it. The first draft already looked like us!
4. Deploy one workflow before you scale.
Do not pick the biggest, most visible workflow first. Pick the one with enough volume to matter, enough rework to measure, and low enough risk to learn safely. Post-call follow-ups, monthly leadership updates, customer renewal notices, campaign variants, and new-hire AI guidance are good places to start. If the approved setup is harder to find than the old shortcut, the shortcut wins.
5. Govern it like an operating asset.
Brand standards with AI need an owner, not a committee with good intentions. Decide what requires human, brand, legal, or executive review. Share examples of strong and weak output with your team. Remove stale instructions before they damage the work. Messaging changes do not automatically update an AI configuration.
I want you to be able to answer this question: before AI scales your content, have you scaled the standards, ownership, and judgment that protect the brand?
Start smaller than a program. Choose one workflow, one owner, one usable standard, and one way to learn from real output.
If you are looking for support setting up your own AI Brand Standard, GadellNet can help. Get in touch today!
