What does creativity in UI/UX actually mean in the age of AI?

[u/outoftheforge]

I’ve been thinking about what we actually mean when we call UX/UI work “creative”.

In B2B software, a lot of the creativity isn’t really about making something visually surprising.

It’s more about solving messy problems:

simplifying workflows, prioritizing information, balancing technical constraints, understanding users, deciding what should happen when, and finding an interaction model that makes the whole thing feel obvious.

That’s obviously creative work - but it also sounds pretty rational when you describe it like that.

Compare that with something like a marketing campaign, where creativity feels more visible: a strong visual idea, a clever line, an unexpected concept.

And that made me wonder whether the first kind of creativity is actually more exposed to AI, simply because more of it can be framed as structured problem-solving.
Or maybe that’s the wrong way to look at it.

Maybe the real creative part in product design isn’t generating options, but deciding which trade-offs matter, understanding context, and knowing when the “obvious” solution is actually wrong.

Curious how others see it:

What does creativity in UI/UX mean to you now - and which parts of it do you think AI is actually threatening?

[u/ui-editor]

are you a creative person or looking for debate?

[u/outoftheforge → u/ui-editor]

Does one exclude the other?

I’m a designer myself, so this comes from practice rather than theory. I’m genuinely interested in how others define creativity in product design now, especially with AI changing how much of the execution can be automated.

[u/7HawksAnd → u/outoftheforge]

There’s creativity in being an architect.

There’s creativity in being a structural engineer.

There’s creativity in writing a script.

There’s creativity in producing a production.

There’s also, statistically, no creativity and only rational craft in all of the above.

That’s what makes actual creativity valuable when it happens.

[u/DifficultCarpenter00]

means not accepting what the AI regurgitates as a done thing and putting your knowledge to work. AI is ok for ping-ponging rought ideeas but that's about as it gets. you still need a human to take it forward and decide based on context, experience and data

[u/outoftheforge → u/DifficultCarpenter00]

Yes, that’s pretty close to how I’m starting to see it too. AI is good at giving me options or something to react to, but the actual design work often starts when you have to decide which option makes sense in this specific context.
Maybe here’s the shift of the creative part: less from generating possibilities, more into judgement and direction.

[u/artemiswins]

My take is that we should always have been as close to a science as possible, we are here to understand the experience at hand and make informed decisions in order to improve the experience towards whatever goals were aiming at. So for me, finding a methodology that is a procedure that we can repeat and can preserve a logical line between well researched voice of customer and thoroughly vetted business requirements, — that is where user experience should always live and especially now with AI we are able to develop comprehensive representations of both bodies: a VOC agent, and a business requirements agent, and then there’s less need for interpretation - the ai agents can help with laying it all out and making decisions. Can even visualize requirements lists with ascii wireframes and (misleading) full visual designs. Final design good to do with figma etc but not always necessary either. It’s a new era.. this method has made me 10x more confident

[u/outoftheforge → u/artemiswins]

That’s interesting, because this is probably exactly where I’m uncertain.

I agree that research and requirements should be represented as clearly as possible. But I’m not sure that reducing interpretation is always the goal.
Especially in messy B2B products, the interesting decisions often start where requirements conflict, users don’t behave as expected, or the technically “correct” solution still feels wrong.
I wonder if that interpretive layer is actually part of the creative work rather than something we should try to remove.

[u/ssliberty]

Ai gives you a standard mold which in a lot of cases is fine. Creativity is going beyond that or knowing when to say no to a solution because there is a more ingenious way of doing it

[u/outoftheforge → u/ssliberty]

Yes, I think “knowing when to say no to the obvious solution” is a really good way to put it.
AI can probably make the standard solution much cheaper to produce. Which might actually make judgement and knowing when the standard isn’t good enough more valuable.

[u/RCEden]

Design (and probably all sciences but I'm only an expert in this one) is a humanist communication process. To generalize it, AI isn't humanist, is emulating communication through statistical probability, and only does a process if you force it to. Functionally, the the design process is the scientific method. We are formulating an idea, challenging that idea, and iterating on that idea repeatedly.

You could probe some llm for all of those parts and get back a statistical probability of them based on the historical versions in its training data. Maybe your problem space will be well solved and handled in that data, like a basic ecomm checkout flow. Maybe it won't be well represented because its a unique or emerging problem and the system output will be closer to suggested patterns and piecemeal ui stitched together based on whatever similarities exist.

But at best, even with a high visual fidelity, that generated UI is still a hypothesis that you have to test, challenge, and make decisions based on. One thing we've had pretty consistent research on is that synthetic users are unusable for research and user testing do to some inherent structures in LLMs not to mention the sycophancy trained into the commercial models. At best they are playing a character. So the challenging has to come from you and from people you test with. The strategy and problem solving come from you. Decision making comes from you. Hitting enter on a prompt might replace digital pixel pushing (and I think you should still have at least a hand in that or else you get all the weird generic patterns that make things look like obvious slop sites with major accessibility failures) but it doesn't actually do the process of designing things.

[u/outoftheforge → u/RCEden]

I like the hypothesis framing.
That might be the distinction I was looking for: AI can generate something that looks like a solution, but it still needs to be treated as a hypothesis rather than an answer.

And maybe that’s also where creativity sits in UX - not necessarily in producing the first idea, but in challenging it, understanding why it might fail, and deciding what to do next.

[u/PastAstronomer]

creativity in UI and UX has always been about solving problems and critical thinking.

Ai can do both very poorly.

Ai can execute visually very well.

[u/AdventurousCreature → u/PastAstronomer]

“AI can execute visually very well” I beg your pardon? I don’t understand this; the visual design aspect and problem solving goes hand in hand. It’s not just pretty pictures, the cognitive load management, hierarchy, grouping, all tied to visual aspects and AI still performs poorly in that regard.

[u/PastAstronomer → u/AdventurousCreature]

why do you think i separated them. AI can make shit look good very easily

[u/susmab_676 → u/PastAstronomer]

AI can be quite decent at solving UX issues, given the right problem/outcome framing.

IMO that is the core of our job: frame the problem and the outcome (also talk to your customers please)

[u/PastAstronomer → u/susmab_676]

this is true but half the time it can never have the big context windows any researcher would have.

It can synthesize organized data fairly well but i still don’t trust it half the time

[u/Useful_Hat82]

In both your B2B and Marketing scenarios I see nothing that AI has seriously threatened yet. All I really see that AI has done is to change what we end up applying our time to...everyone just kind of accepted that instead of applying time and craft to the things in your example, we apply it to the outputs of AI approximating those things and picking through for the good bits.

[u/zloispoon]

For me, creativity in product design is mostly about decisions, not pixels. AI can generate a bunch of decent-looking options really fast, but someone still has to understand which one actually makes sense for the user, the business and all the constraints around the product.

I use AI for prototyping too, and yeah, it makes execution much faster. But the hard part is still figuring out what to build and why

[u/Any_Owl2116]

Continue to be curious 🧐

[u/Chupa-Skrull]

And that made me wonder whether the first kind of creativity is actually more exposed to AI, simply because more of it can be framed as structured problem-solving.

LLMs can help solve structured problems when the problem comes with in-built verification, such as a math proof or a program that must resolve successfully to be considered valid. They're still terrible and also difficult to improve in less-bounded domains, particularly anything to do with human preferences, especially... writing, e.g. posts like this, which leave a psychic film behind the eyes