Good UX is deciding what matters, in what order, for whom, under constraints you don’t control. That’s the job. AI in UX can help, but it doesn’t do the deciding. Think of it as a tireless junior: fast, keen, occasionally delusional. Useful, supervised.
I’m not anti‑AI; I’m anti‑theatre. I use AI in UX design every week, but never to replace judgement. Here’s how I actually use it, where I won’t, and why UX work still matters even as every second product claims to be “AI‑powered”.
UX is about choices, not pixels
UX isn’t pixels. It’s priority. It’s trade‑offs. It’s choosing what not to ship. Most of my day is spent saying no: no to yet another step in onboarding, no to a clever animation that hides a slow query, no to five versions of the same CTA with different adjectives. Users don’t care how much effort you put in. They care that it works and they understand what to do next.
In AI UX design, the real interface is uncertainty. Models are probabilistic; they guess. Your job is not to pretend certainty. Your job is to make uncertainty usable: show confidence (without drama), admit failure (without shame), and give people a fast way to correct the system without losing their work. That is product design, not press‑release design.
Where AI UX helps — so I can think
I don’t ask a model to be my creative director. I ask it to remove friction so I can get to the decisions faster. A few ways this works in practice:
- Call digestion. One hour of user interviews becomes a first‑pass summary I can scan on the train. Then I re‑watch the sharp bits. Models love confident conclusions; I love contradictions. Both are useful.
- Theme clustering. Dump ten pages of notes in, get back clusters to argue with. It won’t know which cluster matters, but it will stop me drowning in stickies.
- Edge‑case generator. “List 20 ways this form can go wrong.” It never finds all of them, but it finds enough to kick me into test mode.
- Proto‑data for prototypes. Realistic names, transactions, and dates so screens feel like software, not theatre. Magical how much faster stakeholders get the point when the table isn’t full of Lorem Ipsum and ‘John Doe’.
- Empty states and error scaffolds. First‑pass microcopy for the boring bits no one wants to write. I will always edit it, but starting from something is quicker than starting from nothing.
- Variant sweeps. Ten headline directions in my tone. Nine go in the bin; one sparks the right angle.
- Analytics hygiene. Given a journey description, I’ll ask for a draft event map. I still decide what matters, but I’m not starting from a blank page.
- Handoff notes. I design the component; the model expands my terse annotations into a friendly spec for developers. (No, not the decisions — just the wording.)
This is what ux design ai tools are good for: search, summarise, scaffold, simulate, sanity‑check. The work becomes less miserable. I become harder to distract.
Two repeatable moves
Inbox autopsy
Inputs: last 50–100 support messages (subject, body, last seen screen/URL if you have it).
Prompt:
“Group these messages by blocked task and last seen screen/URL. For each group, return: blocked_task, last_seen, count, representative_quote (max 15 words). Do not invent fields. If unknown, write unknown.”
Output you want: a short table with 3–6 groups — not a novel.
Keep/kill rules:
- Keep groups that touch money paths (signup, checkout, billing, core task).
- Merge groups that are just wording variants of the same blockage.
- Kill anything with count = 1 unless it’s catastrophic.
Definition of done: one sentence per top group: “Fix X on Y screen.” That’s your mini backlog.
Next step: write the tiniest possible fix (copy change, hint, state) and a metric to watch for a week.
Common model failure: it will try to summarise by topic (“confusion”, “issues”). Force blocked task labels; that’s where action lives.
Copy contradiction diff
Inputs: paste the two flows (or string lists) you suspect are fighting each other.
Prompt:
“Find contradictions between these flows. Flag phrases that promise different behaviour (e.g., ‘autosave’ vs ‘save’). Propose the minimal wording change that makes both flows consistent. Keep suggestions to 15 words each.”
Output you want: a bullet list of contradictions with a one-line fix per item.
Keep/kill rules:
- Keep contradictions that touch state (save/unsaved, draft/published, success/failure).
- Kill stylistic quibbles (Oxford commas, synonyms that don’t change behaviour).
- Prefer removal over addition — fewer words, clearer truth.
Definition of done: one accepted change per contradiction, implemented in both places, with a note in the component or content source of truth.
Common model failure: it over-edits. Ask for minimal change and reject anything that alters behaviour.
Where I don’t use AI UX
- Prioritisation. Models are brilliant at options. They are terrible at cost. Choosing what not to do is leadership, not a prompt.
- Taste and hierarchy. A model can lay bricks; it cannot design the room. Choosing scale, rhythm, and pace is the point.
- Positioning and narrative. Product truth is earned in calls, demos, and uncomfortable meetings. AI can tidy the words; it can’t decide what you stand for.
- Guardrails. “Sensitive content warning” is not a safety system. Deciding what your product refuses to do is a human job; AI just explains it politely.
- Final copy for high‑stakes UI. I’ll happily take a draft. I will not outsource the promise.
When I’m doing UX design for AI products, this boundary matters even more. Start with the correction loop before the happy path. Show the model’s confidence honestly (bands beat fake percentages). Design latency states that tell the truth. Let people undo without punishment. This is where trust lives.
My simple rule
If the task is search, summarise, scaffold, simulate, or sanity‑check → give it to AI first.
If the task is decide, prioritise, promise, exclude, or sign‑off → that’s mine.
That rule keeps me fast without becoming lazy. It also keeps the team honest. If someone says “let’s have AI write the UX”, what they usually mean is “we don’t know what matters yet”. Fine — then we’re not done with the thinking.
The bit no AI UX tool solves
Most broken UX isn’t a tooling problem. It’s a leadership problem disguised as a Figma file. No model will save a roadmap with no priorities, a funnel with no owner, or a product that won’t choose who it’s for. AI can make you faster at the wrong thing. That’s not progress; that’s theatre with better lighting.
The inverse is also true: when the strategy is clear, AI saves you hours. I’ve shipped weeks faster because the grunt moved out of the way — transcripts digested, variants generated, specs expanded — so the team could focus on the argument that mattered.
Use AI like a very capable intern who never sleeps and occasionally lies. Helpful, supervised, never in charge. In ai ux design, your job is to make uncertainty humane. In ux design for ai products, your job is to admit the machine’s limits before your users discover them the hard way. And when you talk about AI in UX design, talk about outcomes, not magic: fewer support tickets, faster time‑to‑value, clearer decisions.
I don’t need a robot to have taste for me. I need space to use mine. AI gives me that space. The rest — the choosing, the responsibility, the “no” that keeps the product honest — is still the job.
And no, we can’t “AI the onboarding”. We can use AI to clear the fog around it — surface the real verbs, reveal the friction, scaffold the empty states — so the human decision can be obvious and fast. That’s the point of AI in UX design: less ceremony, more clarity.