A few days ago I read "Don't let AI cost you your credibility at work" by Hoang Nguyen in the MindYourMind community. He tells the story of a Product Owner candidate who submitted a test almost entirely written by AI, then kept using AI to answer questions during the presentation. The test ended after two questions.
It made me realize this is not just a PO problem. As a designer, I have seen it too, and I have received documents from other stakeholders with the same issue. My previous post asked whether AI is making designers lazy thinkers, which is about what happens inside each of us. This one is about what happens outside: other people look at our work and decide whether to trust us.
A polished test, but two questions were enough
I once looked over a test from a designer candidate. The brief was to improve the checkout screen of a shopping app. The submission looked great: a problem statement, user insights and five proposed improvements. Proposal number one was "optimize the checkout journey to reduce friction for users".
During the presentation, I asked a simple question: "Which step has the friction? Where do users get stuck?". The candidate read the slide back in different words, but could not point to a step. I asked again: "If you could only do one thing, what would you pick and what would you drop?". No answer either, because those five proposals had been listed by AI, and none had been weighed by the candidate.
A more convincing answer can be very simple: "At the address step, the form has 9 fields. I asked 3 friends to fill it in and all 3 stopped at the postal code field. I would remove that field first." No fancy wording, it just shows you looked at the problem yourself.
I don't think the candidate was weak. A test simply isn't there to see how polished your document is, but how you think when you face a problem.
Three places designers lose points
In my view, these are the three places where AI most easily costs a designer credibility, because they are where others judge how we think.
Explaining design decisions
A design decision holds up when it answers three questions: what is the problem, what are we trading off, and how will we measure it. AI is quick to write lines like "optimize the user journey" or "elevate the visual experience". They are not wrong, they just say nothing specific, so one "why" is enough to expose them.
The same proposal, presented two ways. The mockup, figures and content have been changed to protect the project's confidentiality.
Presentation B is not longer than A. It simply contains things the author had to find out: 5 of 8 testers had to scroll to see the battery level, the weather card has to give up its spot, and after launch we will measure time to find the battery level. AI can help phrase those points more tightly, but it cannot come up with them for you.
Writing UX copy
This is where the "AI voice" shows most, because end users read it every day: long sentences with many clauses, words like "journey", "seamless", "optimal", and structures that read like a literal translation. In Vietnamese this is especially visible, and Hoang Nguyen notes he almost never saw such phrasing at work before AI became common.
An AI insight card before and after editing. The mockup, product name, figures and content have been changed to protect the project's confidentiality.
Nielsen Norman Group also points out that AI drafts tend toward generic phrasing, drift from brand voice, and know nothing about your users or context. Their advice is to treat AI as a first-draft collaborator and always have a person review the copy before it ships. The edited card above does not need beautiful writing. It only needs to answer what the user wants to know: does today affect me?
Writing case studies and portfolios
A case study is where recruiters learn how you work. One written with AI usually has every section: context, problem, process, results. But read closely and you can't find yourself in it. No moment where you were wrong, no hard decision, no detail only someone on the project would know.
People who read many case studies notice quickly. And as in the example above, they will check in the interview.
Across stakeholders: one missing edge case and the flow falls apart
For documents that pass between stakeholders, the problem is usually not the wording but what was never written down. I once received a requirements document that looked complete: goals, user stories, acceptance criteria. But edge cases were almost absent: what happens if the connection drops midway, what to show when there is no data, where users resume if they come back mid-flow.
Once design and development started, those questions came up one by one. Everyone guessed differently, the flow got messy, and we had to go back into meetings to align. The time the document saved its author was simply moved to the whole team.
In another case, the acceptance criteria (ACs) were written by AI separately for each user story, and the author never put them side by side to cross-check. Each AC made sense on its own, but together some contradicted each other: one required auto-saving a draft on exit, another required clearing data on exit. Developers built every AC correctly, and the flow was still wrong.
There are also times when a team uses AI to build the interface directly to move fast, and that version goes live. Each screen looks fine on its own. But when the team's design system is not tight enough, every AI-generated screen picks its own colors, spacing and components. Put together as a whole platform, the screens no longer speak the same language. In the end, designers have to take that live version as the starting point, bring it back into Figma, and review and fix it until it is consistent. The time saved by building fast gets paid back later in rework. My takeaway: AI builds fast and right only when the design system is clear enough for it to follow. If the foundation isn't solid, AI just makes the inconsistencies show up sooner.
Researchers at BetterUp Labs and the Stanford Social Media Lab call this kind of output "workslop": AI-generated content that looks like good work but lacks the substance to move a task forward. In their survey, about 40% of workers said they had received workslop in the past month, spending nearly two hours on average dealing with each instance. More striking, about half of recipients saw the sender as less creative, less capable and less trustworthy.
Nobody says anything, but people remember
The part of Hoang Nguyen's post I agree with most is that trust is not lost in one go. People still say thanks, still use the document, sometimes don't even ask for changes. But next time there is a hard problem, they think of someone else first.
(Every document gets a thank-you, but the trust level never shows up anywhere. See the animation in the original post.)
For a designer, that might mean not being invited to a product direction meeting, or having your proposals go through an extra round of checks. Nothing tells you it has started.
Using AI in a way people can still trust
To be honest, the website you are reading was also built with AI, vibe coding style. I use it as a place to show my projects and notes, not to sell web development or to suggest I am a developer. What I take responsibility for is what sits on it: the design decisions, the project stories and every post. I think being clear about which part AI helped with and which part is mine is also a way to keep people's trust.
I still use AI every day: summarizing documents, suggesting phrasing, listing edge cases for me to review, sketching a few options to compare. But before sending anything important, I ask myself four questions.
Four questions before hitting send, and a spec excerpt before and after rewriting. The mockup and content have been changed to protect the project's confidentiality.
- Can I say the key message again in my own words? If I need to look at the slide, I may not really understand it yet.
- Can I answer "why not the other option"? A decision with no rejected alternative is often not a decision yet.
- Have I opened the source of every number myself? AI is very good at producing numbers that sound plausible.
- Is there any sentence I would never say out loud? If so, I rewrite it the way I would say it in a meeting.
The fourth one sounds simple, but it works best for me. Sometimes I have to drop a line that sounds great and replace it with a plainer one that says what I actually mean.
Wrapping up
I don't think there is one right way to use AI. Every person and every team is finding their own rhythm with a tool that changes almost every month. Some of the stories in this post are moments I stumbled on myself. I wrote them down to remember, not to say who is right or wrong.
For me, working in a world with AI feels like having a very fast collaborator. I hand it the repetitive work and save my time for the parts that need me most: understanding users, weighing trade-offs and being able to explain my decisions. When someone asks "why", the answer comes from what I actually understand, and that is the part AI cannot hold for me.
Hoang Nguyen has a comparison I really like: when your AI tokens run out you can buy more, but trust takes time to build. So I choose to keep using AI and keep learning with it, while holding on to the habit of understanding what I send out. If you are also figuring out how to work with AI and want to talk, feel free to reach out on yurickart.com!
Sources
The survey data below was collected from US workers, has limited scope, and does not replace my own point of view.
- Hoang Nguyen (2026). Đừng để AI làm mất sự tín nhiệm của bạn trong công việc (Don't let AI cost you your credibility at work). MindYourMind (members-only post, in Vietnamese).
- Niederhoffer, K., Rosen Kellerman, G., Lee, A., Liebscher, A., Rapuano, K., Hancock, J. T. (2025). AI-Generated "Workslop" Is Destroying Productivity. Harvard Business Review.
- Kaley, A. (2026). UX Writing: FAQs from Practitioners. Nielsen Norman Group.
Originally published at yurickart.com.
