Should you use AI in your work? Yes. The question is no longer if it can be helpful, but when? And how? By most practical measures, AI doesn’t seem ready to take over UX work completely, but many organizations encourage their UX teams to find ways to adopt AI-based tools into their workflows in hopes of increasing high-quality and efficiently produced outputs.
Does AI guarantee better, more efficient work? No. The resources provided in this study guide can help you think about specific, targeted ways AI can assist your work, but you should keep your head on straight and think critically. If you don’t want to work for the robot, make the robot work for you.
Integrating AI into UX Workflows
So far, handing entire UX workflows over to AI is not always the answer. Rather, some of the best in our industry have found clever ways for AI to assist in their existing work. This generally means thinking of yourself as a strategist who leverages AI to increase what you can accomplish. Your hands still direct the work, but donning a set of AI “gloves” allows you to work in ways you never could before.
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UX professionals should use generative AI to enhance their skills, beginning with small tasks while being vigilant about hallucinations and unreliable advice. |
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Good AI prompts include context, a specific ask, rules, and examples. |
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UX professionals use genAI as a content editor, research assistant, ideation partner, and design assistant. |
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Succumbing to AI temptations weakens your UX skills. Strive for the 7 AI virtues to keep yourself strong as you use AI in your work. |
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Doing AI work with AI is like working with a talented intern who still needs supervision. |
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7 |
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Nondevelopers are building complex agentic AI systems through self-taught experimentation rather than formal training. |
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8 |
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AI compresses the design process rather than eliminating it; experienced designers still move through the same steps, only faster. |
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Why AI-Generated Holiday Ads Fail — And What They Teach Us About Using AI in UX Work |
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AI-generated holiday ads lack authenticity and emotional resonance, demonstrating why human judgment remains necessary in creative work. |
Career Strategy and the Evolution of UX Roles
UX job roles are changing because of AI. It’s unsettling to acknowledge, but true. This is neither the first nor the last time this will happen. Rather than lamenting how things used to be, embrace the changes you can make to your skills and mentalities to stay valuable in the age of AI.
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Tools constantly change, but AI will not eliminate the need for fundamental skills like understanding people and asking the right questions. |
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Designers retain their value by focusing on strategy, storytelling, important outcomes, and data-judgment skills. |
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As AI broadens what individuals can do, UX generalists who understand multiple disciplines and strategic thinking are becoming more valuable. |
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There are 5 basic principles to embrace as UX practitioners evolve their skills to utilize AI. |
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You can keep up with AI advancements by doing small experiments and reading a modest diet of optimistic and skeptical AI news. |
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Discernment and good taste will still be necessary to produce superior designs as AI enables more people to create. |
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8 |
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The hype of VR led to widespread disillusionment — AI is very susceptible to the same fate. |
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The single label of “AI design” has already split into four distinct types of work. |
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Consulting clients still expect strong judgment, research rigor, and respect for real-world constraints, even as AI use grows. |
Design
The core skills valuable today are moving more toward the pure “UX” work of defining user needs, specifying user intent, and overseeing the creation of excellent journeys — not the “UI” design of pushing pixels around in a design tool. It’s becoming ever more critical to curate meaningful context for the AI.
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Using AI to create prototypes shows promise but comes with a couple of dangerous risks. |
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Promptframes use AI to increase the fidelity of wireframes to get better feedback from users and stakeholders efficiently. |
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Leverage AI for Mock Tables and Charts When Testing Prototypes |
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Using AI to create realistic tables and charts for prototypes can improve results from user testing. |
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UX-Context Design: Using UX Knowledge to Inform AI-Generated Design |
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As more interface work is AI-generated, the output of research and design shifts from documents written for humans to curated context that guides AI. |
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Building useful and usable AI-powered systems requires encoding user needs and design judgment into well-defined evaluation criteria. |
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Outcome-oriented design defines adaptive frameworks that respond to individual user goals, rather than optimizing a single interface for average needs. |
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Prompt to Design Interfaces: Why Vague Prompts Fail and How to Fix Them |
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Precise visual keywords, references, mock data, and code snippets produce better AI-prototyping results than vague prompts. |
Research
AI-generated information cannot yet replace real data from real people. AI does not control your customer’s purse strings; they do. While AI can assist in many steps of the research process, from planning to analyzing to reporting, it’s critical to stay focused on insights that teach us about our real users.
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AI is most helpful for the planning and analysis phases of UX research. |
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AI can successfully help create a research plan with careful prompting that breaks down each step. |
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Key AI-related research questions for UX include genAI interfaces, new types of UIs, augmenting traditional methods, and AI-generated data sources. |
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Digital twins are becoming more useful for UX research as they better simulate human behaviors. |
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Synthetic Users: If, When, and How to Use AI-Generated “Research” |
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Synthetic users can supplement your research efforts but are far from capable of replacing research with real users. |
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A description of the methodology used in a study investigating how people use AI image-generation tools. |
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A case study for using ChatGPT to help plan research (to study genAI interfaces). |
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Don’t Outsource the Learning: Why Human-Led Research Still Matters in the Age of AI |
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Even if AI matches the quality of researcher output, the team learning that comes from observing users cannot be outsourced. |
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AI can serve as a thought partner during research analysis, but it should not lead the interpretation. |
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AI Can Help with Survey Writing, But It Still Requires Human Expertise |
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AI produces polished survey drafts quickly, but human expertise is still required to catch the subtle design flaws that weaken data quality. |
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AI-moderated interviews offer faster feedback at scale, but they do not replace in-depth, human-led semistructured interviews. |
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Methodological blind spots in UX research tools become more consequential once AI is planning and analyzing the research. |
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Outsourcing qualitative analysis to AI risks both poor insights and the researcher’s credibility. |
Writing
AI has exceptional writing abilities. However, relying on it too heavily can grind away the unique style, tone, and message you wish to convey. It might save time in the short run, but it will ultimately create a cacophony of monotony over time.
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Including tone words in prompts generally falls flat, whereas using existing copy and asking for multiple alternatives produces more natural AI output. |
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Three prompting practices improve the quality of the edits AI produces. |
Service Design
Service design is the activity of planning and organizing a business’s resources to directly improve the employee’s experience, which will indirectly improve the customer’s experience. AI is starting to help in both ways. It has the potential to increase the productivity of internal employees, which enables them to better serve customers. However, it also has the potential to interface directly with customers, providing an experience of its own.
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AI agents will increasingly carry out actions on behalf of users and organizations, changing how services are provided and received. |
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As AI becomes central to service delivery, new metrics must assess AI-to-AI performance, human-AI collaboration, data quality, and user trust. |
Ideation and Workshops
Many UX practitioners use AI extensively as an individual ideation and thinking partner. Does it have a role in workshops and group interactions? It can! Thinking of AI as a “cybernetic teammate” is a powerful way to enhance the outputs of any group working together to solve a problem.
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Teams using AI to augment ideation outperformed individuals and teams without AI, as well as individuals with AI. |
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Thoughtful preparation leads to more successful AI-enhanced workshops. |
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Follow these 5 tips for facilitating AI-enhanced workshops. |
State of AI for UX Work
The resources in this section report on aspects of AI’s impact on UX work at a specific point in time. While some highlight findings that still hold today, others have likely changed as AI’s capabilities have grown and its adoption in the UX field has evolved.
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Evaluating AI-Simulated Behavior: Insights from Three Studies on Digital Twins and Synthetic Users |
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AI-simulated users show promise for filling in missing data and predicting population-level trends. (2025) |
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At that time, narrowly scoped AI design tools were most useful, but not ready to replace designers. (2025) |
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UX-related activities were among the top types of requests made with Claude; many were focused on writing tasks. (2025) |
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In 2025, UX must shift from relying on toolkits to delivering user value with AI, reassessing tactics and cultivating deeper skills. (2025) |
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Status Update: AI UX-Design Tools Are Not Ready for Primetime |
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By April 2024, most AI tools designed for UX failed to meaningfully support core design workflows. (2024) |
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Programmers using GitHub Copilot boosted throughput by 126%, with the greatest gains among less experienced coders. (2023) |
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Support agents using AI handled 13.8% more inquiries per hour while slightly improving resolution quality — especially benefiting less-skilled agents. (2023) |
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Generative AI boosted employees’ output by 66% on average, proving especially helpful for less skilled workers and complex tasks. (2023) |
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Many AI-powered UX research tools are not fully capable of all they claim to be able to do. (2023) |
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ChatGPT Lifts Business Professionals’ Productivity and Improves Work Quality |
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Business professionals using ChatGPT wrote faster and produced higher-quality outputs than those who did not. (2023) |
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Before LLMs were widely available, Jakob Nielsen predicted many ways AI would change the user experience and work of UX professionals. (2020) |
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Demand Accuracy in Your AI Tools: Lessons from Baymard Institute |
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Most AI-powered tools for UX lack reliability and accountability in their outputs, so buyers should demand proven accuracy. |
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After the instability of 2025, the UX field is stabilizing, but differentiation and demonstrated business impact remain vital. |
Podcast Episodes
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Jakob Nielsen reflects on how prior turbulence in the UX field is similar to the recent impacts of AI. |
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44. AI & UX Research (feat. Savina Hawkins & Caleb Sponheim) |
podcast |
AI can enhance productivity and innovation in UX work, but it poses risks and challenges. |
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Bonus Episode: Design’s Role as AI Expands (Feat. Don Norman and Sarah Gibbons, VP at NN/g) |
podcast |
Don Norman and Sarah Gibbons encourage UX professionals to think big in the wake of AI's advancements. |
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50. Responsible AI Use for Research Analysis (feat. Alexander Knoll, Co-Founder of Condens.io) |
podcast |
AI tools for research have both strengths and limitations, and the user research role is evolving. |
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51. The Future of Service Design in the Age of AI (featuring Erika Flowers) |
podcast |
AI will shift designers’ focus away from the minutiae of design work to how we can make meaningful change. |
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55. Democratizing Research in the Age of AI (feat. Ned Dwyer, CEO & Co-founder of Great Question) |
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Ned Dwyer explores strategies for balancing the democratization of UX research with organizational rigor. |
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56. AI for UX Analysis: How Accurate Is It? (feat. Christian Holst & Jamie Holst, Baymard Institute) |
podcast |
Christian and Jamie discuss the risks of relying on unverified AI tools for UX analysis. |
