AI in UX: Work Is Faster, but Practitioners Have Mixed Feelings

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Summary: AI saves UX practitioners time on nearly every task, yet most feel lukewarm about it: mandates, unreliable output, and inflated expectations eat the gains.

We surveyed 521 UX practitioners about how they use AI at work and how it’s affecting them. The short answer: AI use is widespread and saves time, but practitioners are far from enthusiastic, and the benefits depend heavily on role, task, and workplace.

Explore our interactive dashboard to read a curated selection of respondents’ comments on what’s frustrating about AI at work and what’s working.

While virtually every respondent (over 99%) had access to generative AI tools at work, most (59%) used only an approved set of tools; only 12% were free to use any tool they chose.

Agentic AI was common but not universal. When we asked which types of AI tools respondents used at work, 66% reported using at least one agentic tool: either an agentic AI product (such as Claude Cowork) or an AI coding agent/app builder (such as Cursor). Each category on its own was used by just under half of respondents (48% and 47%, respectively); roughly 30% of the participants used both categories.

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Agentic-AI use varied considerably by organization type. Startups and small- to mid-sized businesses used agentic AI the most (85% and 75% of the respondents working for these organizations, respectively), while nonprofits and government organizations used agentic AI the least (36% and 42%, respectively).

These findings are not surprising; government organizations tend to be especially cautious about privacy and security, which may slow their adoption of new technology. For nonprofits, cost and technical know-how may be bigger barriers. Startups, by contrast, often have few approval processes, so teams can adopt new tools quickly. Smaller headcounts provide more reason to delegate to AI, and some startups may attract people who want to work with AI. One respondent who participated in a followup interview with us told us he joined his current startup specifically because it embraced AI tools.

“I really wanted to go into a company that will be building stuff from the ground up, where AI is embedded everywhere.”

Product managers and ops professionals (like DesignOps professionals) reported using agentic AI the most, while content designers and researchers used it the least.

One possible explanation is that product managers and ops roles spend much of their time orchestrating people and data across tools and systems, which is where AI agents are arguably most useful today. Researchers and content designers, on the other hand, focus on producing high-quality artifacts, such as research syntheses or UI copy, for which a general-purpose AI chatbot often suffices.

Technical comfort likely helps, too: PMs and ops professionals tend to be slightly more technical, and agentic tools still require setup and troubleshooting.

We asked respondents which factors affected their ability to adopt AI in their work (multiple answers allowed). Most respondents (80%) chose multiple factors. Only 9% of respondents reported no major barriers to adopting AI in their work.

The top two reasons for not adopting AI were concerns about AI’s output quality (54% of respondents) and data security or privacy (50% of respondents). Many respondents told us in open-ended comments at the end of the survey that AI’s outputs were often not good enough or required significant time to check or correct.

“It’s been hard to incorporate AI into our workflows because we design specialty apps, and the quality of generating designs from AI is very poor / immature.”

“Using AI in my job as a UX researcher has been an experiment that’s mostly unfruitful. I tend to find [that AI] tools […] produce untrustworthy results.”

Not surprisingly, lack of executive buy-in was the factor chosen the fewest times: many respondents reported intense pressure from leadership to use the technology. As one respondent noted:

“The biggest change is the constant push from management to use it more and more. I have to actively push against them on things I know AI doesn’t do well yet.”

More than a third of respondents (34%) selected ethical or sustainability concerns as a barrier to adopting AI in their work. These concerns also came up in the open-ended comments at the end of the survey, where respondents raised a range of issues: the environmental impact of data centers, AI’s effect on the job market, AI-enabled surveillance, and the use of creators’ work as training data without their consent.

For some respondents, these concerns were reason enough to avoid AI. Others described a kind of cognitive dissonance: their employers require them to use a technology they oppose on moral grounds.

“It’s a strange feeling to be forced to use something that I do not believe in.”

“I hate that I have to use something for work that I have banned my children from using. It feels complicated and gross in a way I’ve never struggled with new tech before.”

Some respondents seemed frustrated by how little these issues are discussed, both within their organizations and across the industry.

“I think as an org they have done better than other orgs with helping us learn AI and giving us tools but have done nothing to address ethical or sustainability concerns.”

“No one is talking about AI’s impact on the environment. Why is that?”

We asked respondents which design, research, content, and technical tasks they do, how often they use AI for each, and how AI has changed the time each task takes.

Overall, respondents said AI saved them time. On average, they rated AI’s effect on task time at 4.2 out of 5, where 3 means no difference and 5 means much shorter. (We averaged each respondent’s ratings across the tasks they used AI for, then averaged those scores across respondents.) For every task, most respondents who used AI for it said it made the task faster. But both use and time savings varied considerably by task.

Unsurprisingly, AI adoption was highest when working on artifacts or data and lowest on tasks involving direct interaction with users. Among respondents who did each task, about two thirds said they used AI often or always for writing or editing code (73%), desk research (69%), and analyzing their own research data (67%). These were also among the tasks where AI saved the most time: around 9 in 10 respondents who used AI for them said it made the task faster.

AI use was lowest for tasks involving users. Most respondents never used AI for interviewing users (61%) or testing designs with users (51%), and these tasks, along with evaluating designs, saw the smallest time savings. Sessions still take time to set up and run, and while AI can help with supporting work, like drafting interview guides and test plans, it doesn’t shorten the sessions themselves. Only 11% of respondents reported using AI interviewers, and the same share reported using synthetic users.

Similarly, AI can speed up parts of design evaluation, such as checking a design against accessibility best practices, but critique depends on a solid understanding of human goals and context, which is hard to convey to an AI system fully and reliably.

We asked respondents how they felt about using AI in their work on a 5-point scale, from 1 (very negative) to 5 (very positive). The mean score across all respondents was 3.57, which was significantly above the neutral point of 3 (p < 0.05) but not by much.

This average rating is somewhat surprising; respondents reported that AI shortened every task they used it for, so we might have expected them to feel more positive about using it. To understand what was tempering respondents’ enthusiasm, we turned to the comments they left at the end of the survey.

The survey ended with an optional open-ended question: Is there anything else you’d like to share about how AI is affecting your work? Of the 521 respondents, 226 (43%) answered it — a much higher response rate than we expected for an optional question at the end of a survey. This suggests many respondents had views they wanted to share.

A sentiment analysis revealed that these comments skewed negative. Most comments (69%) expressed at least some negative sentiment, and 41% were purely negative, with no positive sentiment. Only 20% were purely positive.

Researchers were significantly more likely than designers to leave a purely negative comment (51% vs. 33%, p < 0.05). But once mixed comments were counted too, the gap narrowed: 78% of researchers and 66% of designers expressed at least some negative sentiment, and that difference was not statistically significant.

These results should be interpreted with some caution. Fewer than half of respondents left a comment, and people with strong views are more likely to do so. Still, the comments suggest that many respondents are dissatisfied with how AI is used in their work or organizations, and that researchers may have more serious concerns about its use.

We coded each comment to identify the reasons behind its sentiment. Not every comment could be coded because some respondents expressed a sentiment without explaining it. Of the 155 comments expressing negative sentiment, we coded 149 with at least one of 12 reasons why they felt negatively about AI’s effect on their work.

Several of the coded reasons related to how AI is implemented in organizations and the culture around it. However, the most common reason was unreliable AI output and the resulting burden of checking and fixing it.

“AI feedback is long, takes time to read and no instructions, MD file, have ever completely stopped the agent from making wrong assumptions.”

“AI is incredible because it can make 80000 mistakes in the time that it takes for someone to correct 5 of them. It also makes it easier for businesspeople to create complex slop-flows that can take weeks to months to untangle, that would have taken a designer a month to make well without AI.”

Many respondents said that their organizations mandated AI and tracked their usage, which led to performative use. The result was more AI-generated output that someone then had to check and fix. Some respondents described fixing other people’s poor designs or wading through AI “slop.”

“I now sometimes find myself trying to quickly fix completely unusable prototypes made by a product manager. And I’m expected to come up with a solution just as quickly as AI does.”

“The people who actually care about what we make end up doing double work, having to review and fix the slop of others AND do our own work right.”

Some respondents felt that the time-saving expectations put forward by leadership were unreasonable and made their work more stressful.

“Leadership expects faster work and designers to be able to do more, but AI output quality does not support that yet.”

“Expectations from executives are so much higher now (unreasonably inflated), so the work is way more intense without the time to set up the workflows and operational processes first to be AI-enabled, so it’s a complete mess right now.”

Others felt that their expertise and contributions were being devalued in favor of speed.

“The expectation to move faster when the output quality is significantly worse just tells me companies don't actually care about good UX.”

Finally, some respondents reported that, because there was no training or guidance on using AI, the burden of figuring out how to use it and the expectation to move faster both fell to them.

“There’s a general expectation that we use AI as much as possible to make our work more efficient. At the same time, we get little to no guidance on which AI tools to use, or how.”

Not every comment was negative. Nearly half of the comments (49%) express