AI in UX: Work Is Faster, But Practitioners Have Mixed Feelings - NN/G

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October 23, 2026 2026-10-23
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.
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