By Philip Burgess | UX Research Leader User experience (UX) testing aims to understand how real users interact with a product and identify areas for improvement. Among the many metrics available, task success rate stands out as one of the most direct and reliable indicators of usability. It measures whether users can complete a specific task within a product, reflecting how intuitive and effective the design truly is. This post explores why task success rate matters, how to measure it effectively, and how it can guide better design decisions. User completing a task on laptop during UX testing What is Task Success Rate and Why It Matters Task success rate is the percentage of users who complete a predefined task successfully during a usability test. A task might be as simple as finding a product on an e-commerce site or as complex as completing a multi-step form. This metric provides a clear, objective measure of whether users can achieve their goals without frustration or error. Why is this so important? Because the ultimate goal of UX design is to help users accomplish what they came to do quickly and easily. If users struggle or fail to complete tasks, it signals problems with navigation, layout, instructions, or functionality. Improving task success rate means improving the overall user experience, reducing frustration, and increasing satisfaction. How to Measure Task Success Rate Effectively Measuring task success rate requires careful planning and execution. Here are key steps to ensure accurate and useful results: Define clear tasks: Tasks should be specific, realistic, and relevant to typical user goals. Avoid vague or overly broad tasks. Set success criteria: Decide what counts as success. For example, finding a product page, completing a purchase, or submitting a form without errors. Recruit representative users: Test with people who match your target audience to get meaningful insights. Observe and record: Watch users attempt tasks, noting whether they succeed, fail, or need assistance. Calculate the rate: Divide the number of successful completions by the total number of attempts, then multiply by 100 to get a percentage. For example, if 18 out of 20 users successfully complete a checkout process, the task success rate is 90%. This number can then be compared against benchmarks or previous tests to track improvements. Using Task Success Rate to Improve UX Design Task success rate is not just a number; it’s a tool for making design decisions. Here’s how it can guide improvements: Identify problem areas: Low success rates on specific tasks highlight where users struggle. This points designers to features or flows needing attention. Prioritize fixes: Focus on tasks with the lowest success rates first, as these cause the most user frustration. Test design changes: After making improvements, retest the same tasks to see if success rates increase. Combine with qualitative data: Pair success rate with user feedback and observations to understand why users fail and how to fix issues. For instance, if users fail to find the checkout button, designers might make it more prominent or simplify the steps leading to it. After redesign, a higher task success rate confirms the change worked. UX researcher analyzing task success rate data on computer Common Challenges and How to Overcome Them While task success rate is valuable, it comes with challenges: Defining success can be tricky: Some tasks have multiple valid outcomes. Be clear about what counts as success before testing. Small sample sizes: Testing with too few users can produce misleading results. Aim for at least 15-20 participants for reliable data. Ignoring context: Success rate alone doesn’t explain why users fail. Combine it with observations and interviews. Overemphasis on success: Don’t focus only on success rate. Consider task time, errors, and user satisfaction for a fuller picture. Addressing these challenges ensures task success rate remains a powerful metric in your UX toolkit. Practical Examples of Task Success Rate in Action E-commerce site: A retailer tested the task of adding an item to the cart. Initial success rate was 65%. After redesigning the “Add to Cart” button to be larger and more visible, the rate rose to 92%. Mobile app: A banking app tested users’ ability to transfer money. Success rate was 80%, but users reported confusion about confirmation screens. Simplifying the flow increased success to 95% and reduced errors. Educational platform: A learning site tested course enrollment. Success rate was 70%. Adding clearer instructions and progress indicators raised it to 88%. These examples show how task success rate helps pinpoint issues and measure the impact of design changes.

By Philip Burgess | UX Research Leader Choosing the right usability questionnaire can make a big difference in understanding how users experience your product. Three popular tools often come up in discussions: SUPR-Q, SUS, and UMUX-Lite. Each has its strengths and fits different research needs. This post breaks down what sets them apart and helps you decide which one suits your project best. User completing a usability survey on a tablet What Are SUPR-Q, SUS, and UMUX-Lite? Before comparing, it’s important to understand what each questionnaire measures and how it works. SUPR-Q (Standardized User Experience Percentile Rank Questionnaire) SUPR-Q focuses on overall user experience, covering usability, trust, appearance, and loyalty. It has eight items and provides percentile ranks to compare your product against others. SUS (System Usability Scale) SUS is a classic tool with ten questions that measure perceived usability. It’s quick, reliable, and widely used across industries. UMUX-Lite (Usability Metric for User Experience - Lite) UMUX-Lite is a shorter version of the UMUX, consisting of just two questions. It measures perceived usability and usefulness, making it very efficient for quick assessments. How They Differ in Focus and Detail Each questionnaire has a slightly different focus, which affects how you might use it. SUPR-Q SUPR-Q goes beyond usability by including trust and appearance. This makes it useful when you want a broader view of user experience, especially for websites or brands where trust and visual appeal matter. SUS SUS zeroes in on usability alone. It’s great for evaluating how easy a system is to use but doesn’t capture emotional or trust-related factors. UMUX-Lite UMUX-Lite balances brevity and insight. It measures usability and usefulness, which are key drivers of user satisfaction, but with only two questions, it’s less detailed than the others. When to Use Each Questionnaire Choosing the right tool depends on your goals, timeline, and the depth of feedback you need. Use SUPR-Q When You Want a Broader User Experience Picture If your project involves a website or digital product where trust and appearance influence user decisions, SUPR-Q offers a more comprehensive view. For example, an e-commerce site might use SUPR-Q to understand not just if users find the site easy to use, but also if they trust it enough to make purchases. Use SUS for Quick and Reliable Usability Testing SUS is ideal when you need a well-established, reliable measure of usability. It works well in usability labs or remote testing when you want to benchmark your product’s ease of use against others. For instance, a software company testing a new app feature might use SUS to quickly gauge if users find it intuitive. Use UMUX-Lite for Fast Feedback with Minimal Burden UMUX-Lite shines when you want to minimize survey fatigue or when you need quick feedback from a large audience. It’s useful in agile environments where rapid iteration is key. For example, a startup releasing frequent updates might use UMUX-Lite to monitor usability trends without overwhelming users. Dashboard showing usability survey scores and comparisons Comparing Length and User Burden Survey length affects response rates and data quality. Here’s how they stack up: SUPR-Q: 8 questions SUS: 10 questions UMUX-Lite: 2 questions UMUX-Lite’s brevity reduces user effort but may miss nuances. SUS and SUPR-Q take longer but provide richer data. Scoring and Interpretation Differences SUPR-Q provides percentile ranks, making it easy to see how your product compares to others in the same category. It also breaks down scores by subcategories like trust and appearance. SUS scores range from 0 to 100, with higher scores indicating better usability. It’s widely benchmarked, so you can compare your results to industry averages. UMUX-Lite scores also range from 0 to 100 but focus on usability and usefulness combined. It’s less common but growing in popularity for quick assessments. Practical Examples A financial services website used SUPR-Q to identify trust issues that were not apparent in SUS scores. This helped them redesign their homepage to improve credibility. A mobile app developer used SUS to benchmark usability before and after a redesign, showing a 15-point increase in SUS score that correlated with higher user retention. An agile team used UMUX-Lite after each sprint to quickly track usability trends, enabling faster fixes without lengthy surveys.

By Philip Burgess | UX Research Leader User experience plays a crucial role in the success of any product or service. Measuring this experience accurately helps teams improve usability and customer satisfaction. One tool that has gained attention for its simplicity and effectiveness is UMUX-Lite. This post explains what UMUX-Lite is, why it works well for quick usability assessments, and how to score it correctly to get meaningful insights. Person completing UMUX-Lite survey on tablet What Is UMUX-Lite? UMUX-Lite stands for Usability Metric for User Experience Lite. It is a short questionnaire designed to measure perceived usability with just two questions. These questions focus on how easy and how effective a product or system feels to the user. The goal is to provide a quick, reliable snapshot of user satisfaction without the need for long surveys. The two questions typically ask users to rate: How easy the system is to use How well the system meets their needs Each question is answered on a 7-point scale, ranging from strong disagreement to strong agreement. This brevity makes UMUX-Lite ideal for situations where time is limited or when you want to gather feedback frequently. Why UMUX-Lite Works Well UMUX-Lite works because it balances simplicity with reliability. Traditional usability surveys like the System Usability Scale (SUS) have 10 questions, which can be time-consuming and sometimes overwhelming for users. UMUX-Lite reduces this to two focused questions, making it easier for users to respond honestly and quickly. Research shows that UMUX-Lite scores correlate strongly with SUS scores, meaning it provides a valid measure of usability despite its brevity. This makes it a practical choice for teams that want to: Collect quick feedback during iterative design cycles Avoid survey fatigue among users Get a clear sense of usability without complex analysis Because it focuses on perceived ease of use and effectiveness, UMUX-Lite captures the core of user experience in a straightforward way. This focus helps teams identify usability problems early and track improvements over time. How to Score UMUX-Lite Correctly Scoring UMUX-Lite involves a simple calculation based on the two user responses. Each question is rated on a 7-point scale, where 1 means strong disagreement and 7 means strong agreement. The scoring formula converts these ratings into a usability score between 0 and 100. Here is the step-by-step process: Collect responses to the two questions from users. Calculate the average score of the two responses. Apply the UMUX-Lite formula : ``` UMUX-Lite Score = (Average Score - 1) × 100 / 6 ``` This formula adjusts the average rating from the 1–7 scale to a 0–100 scale, where 0 represents the lowest usability and 100 the highest. Example If a user rates the questions as 5 and 6, the average is (5 + 6) / 2 = 5.5. Applying the formula: ``` (5.5 - 1) × 100 / 6 = 4.5 × 100 / 6 = 75 ``` So, the UMUX-Lite score for this user is 75, indicating good usability. Tips for Accurate Scoring Ensure users understand the questions clearly to avoid confusion. Use consistent rating scales across surveys. Collect enough responses to get a reliable average. Compare scores over time or between different versions of your product to track usability changes. Printed UMUX-Lite questionnaire with user responses Practical Uses of UMUX-Lite UMUX-Lite fits well in many real-world scenarios: Agile development : Quickly assess usability after each sprint without burdening users. Remote testing : Send short surveys to users after remote sessions. Product launches : Gather immediate feedback on new features or updates. Customer support : Measure satisfaction after support interactions to identify usability issues. Because it is easy to administer and interpret, UMUX-Lite helps teams stay user-focused and make data-driven decisions.

By Philip Burgess | UX Research Leader Research today moves faster than ever, and artificial intelligence plays a key role in helping scholars, scientists, and students manage data, analyze results, and generate insights. Choosing the right AI tools can save time, improve accuracy, and open new possibilities in research. This post highlights some of the best AI tools available in 2025 that are worth using for various research tasks. Researcher working with AI software on laptop AI Tools for Data Collection and Literature Review Collecting and organizing relevant research materials is often the most time-consuming part of any project. AI tools can automate this process by scanning vast databases and extracting key information. Semantic Scholar This AI-powered search engine uses natural language processing to find relevant papers quickly. It highlights influential citations and summarizes key points, helping researchers focus on the most important studies. Research Rabbit This tool visualizes connections between papers, authors, and topics. It helps users discover related work they might miss with traditional keyword searches. Iris.ai Iris.ai reads research papers and maps out the core concepts. It can suggest relevant articles based on the content of your own research proposal or draft. These tools reduce the hours spent manually searching through databases and help build a solid foundation for any research project. AI Tools for Data Analysis and Visualization Once data is collected, analyzing it accurately is crucial. AI tools can assist with statistical analysis, pattern recognition, and creating visual representations of complex data. Tableau with AI Extensions Tableau integrates AI features that suggest the best ways to visualize data and detect trends automatically. This makes it easier to communicate findings clearly. IBM Watson Studio Watson Studio supports data cleaning, model building, and deployment. It offers automated machine learning capabilities that help researchers build predictive models without deep coding knowledge. Orange Data Mining This open-source tool provides visual programming for data analysis. It includes AI-powered widgets for clustering, classification, and regression, making it accessible for researchers with varying technical skills. Using these tools can improve the quality of data interpretation and help uncover insights that might be missed with manual methods. Colorful AI-generated data visualization on computer screen AI Tools for Writing and Editing Research Papers Writing clear and well-structured papers is essential for sharing research findings. AI tools can assist with drafting, editing, and formatting to improve clarity and reduce errors. Grammarly Beyond basic grammar checks, Grammarly now offers suggestions for academic style, tone, and conciseness. It helps polish manuscripts before submission. Writefull Writefull provides language feedback tailored for scientific writing. It can suggest better phrasing based on millions of published papers. SciNote Manuscript Writer This tool helps generate sections of research papers by summarizing experimental data and suggesting text based on the study’s results. These AI writing assistants save time and help maintain high standards in academic communication. AI Tools for Collaboration and Project Management Research often involves teams working across locations and disciplines. AI tools can improve collaboration and keep projects on track. Notion AI Notion combines note-taking, task management, and AI-powered content generation. It helps teams organize ideas, track progress, and generate meeting notes automatically. Mendeley Mendeley is a reference manager with AI features that recommend relevant papers and help manage citations collaboratively. Slack with AI Bots Slack’s AI bots can summarize conversations, schedule meetings, and automate reminders, keeping research teams connected and efficient. These tools support smooth communication and coordination, which are vital for successful research projects. Ethical Considerations When Using AI in Research While AI tools offer many benefits, researchers must use them responsibly. Always verify AI-generated results and maintain transparency about AI’s role in your work. Avoid relying solely on AI for critical decisions or interpretations. Ethical use includes respecting data privacy, avoiding plagiarism, and acknowledging AI assistance in publications. Summary AI tools in 2025 provide powerful support for every stage of research, from gathering information to writing and collaboration. Tools like Semantic Scholar and Iris.ai speed up literature reviews. Data analysis becomes more accessible with platforms like IBM Watson Studio and Tableau. Writing assistants such as Grammarly and Writefull improve clarity and style. Collaboration tools like Notion AI and Mendeley keep teams organized.

By Philip Burgess | UX Research Leader User experience (UX) research is essential for creating products that truly meet user needs. Recently, artificial intelligence (AI) tools have become popular for speeding up data analysis and generating insights. While AI offers clear benefits, relying too heavily on it in UX research can lead to serious pitfalls. This post explores why depending too much on AI can harm the quality of UX research and what researchers should keep in mind to maintain balance. Researcher reviewing user data with AI tools AI’s Role in UX Research Today AI tools help UX researchers by automating tasks like sorting through large datasets, identifying patterns, and even generating user personas. These capabilities save time and reduce manual effort. For example, AI-powered sentiment analysis can quickly scan thousands of user comments to highlight common frustrations or praises. Many teams use AI to: Process survey responses faster Analyze user behavior from heatmaps or clickstreams Generate initial hypotheses based on data trends These uses improve efficiency and allow researchers to focus on higher-level interpretation. Why Overreliance on AI Can Be Problematic Despite its advantages, AI has limits that make it risky to depend on exclusively. AI Lacks Human Context and Nuance AI algorithms analyze data based on patterns and rules but cannot fully understand human emotions, cultural context, or subtle user motivations. For example, sarcasm or irony in user feedback often confuses sentiment analysis tools, leading to inaccurate conclusions. Risk of Bias and Misinterpretation AI models learn from existing data, which may contain biases. If training data is skewed, AI can reinforce stereotypes or overlook minority user groups. This leads to designs that fail to serve all users fairly. Missing the “Why” Behind User Behavior AI excels at showing what users do but struggles to explain why they behave that way. UX research requires deep empathy and qualitative insights from interviews or observations that AI cannot replicate. Overlooking Unexpected Insights AI focuses on known patterns and may miss surprising or novel findings. Human researchers often discover unexpected pain points or opportunities by engaging directly with users. Balancing AI and Human Expertise in UX Research To avoid these risks, UX teams should use AI as a tool rather than a replacement for human judgment. Combine AI with Qualitative Research Use AI to handle large-scale quantitative data but complement it with interviews, usability tests, and field studies. This mix provides a fuller picture of user needs. Validate AI Findings with Human Review Always have researchers review AI-generated insights critically. Question surprising results and check for potential bias or errors. Train Teams on AI Limitations Educate UX professionals about what AI can and cannot do. Awareness helps prevent blind trust in automated outputs. Use AI to Support Creativity, Not Replace It Let AI handle routine data tasks so researchers can spend more time brainstorming and designing innovative solutions. UX researcher documenting user feedback during an interview Practical Examples of AI Overdependence Issues A company relied solely on AI sentiment analysis for product feedback. The tool misread sarcastic comments as positive, leading to ignoring real user frustrations. This caused a drop in user satisfaction after launch. Another team used AI to generate user personas but failed to include diverse user voices. The product design missed accessibility needs, alienating some users. In one case, AI flagged a common user behavior but missed the underlying cause, which was only uncovered through direct interviews. The team initially built a feature that did not solve the real problem. These examples show that AI can assist but not replace human insight. Moving Forward with Responsible AI Use in UX AI will continue to grow as a valuable part of UX research. The key is to use it responsibly: Treat AI as a partner, not an oracle Maintain strong human involvement in interpretation Continuously test AI outputs against real user feedback Stay alert to bias and gaps in AI models By balancing AI tools with human skills, UX researchers can deliver richer, more accurate insights that lead to better user experiences.

By Philip Burgess | UX Research Leader User experience (UX) researchers face growing challenges as AI tools become part of their daily workflow. Crafting effective AI prompts can unlock powerful insights, speed up analysis, and improve design decisions. Yet, many UX researchers struggle to write prompts that deliver clear, relevant, and actionable results. Mastering the right AI prompt frameworks can transform how you gather and interpret user data. This post explores 10 essential AI prompt frameworks every UX researcher should know. These frameworks help you structure prompts to get precise answers, generate ideas, analyze feedback, and simulate user behavior. Whether you are new to AI or want to sharpen your prompt skills, these frameworks will boost your research quality and efficiency. A UX researcher working with AI tools to analyze user feedback 1. AI Prompt: Context-Setting Framework Start your prompt by clearly defining the context. This framework ensures the AI understands the background and scope of the task. Example: "Imagine you are a UX researcher analyzing a mobile app for fitness tracking. Users report difficulty finding workout history. Suggest three ways to improve navigation." This framework helps the AI focus on relevant details and avoid generic responses. 2. Role-Playing Framework Assign a role to the AI to simulate expert perspectives or user personas. This approach generates insights from specific viewpoints. Example: "Act as a first-time user of an e-commerce website. Describe your experience checking out and identify any pain points." Role-playing prompts reveal user emotions and expectations that might be missed in traditional surveys. 3. Step-by-Step Reasoning Framework Break down complex questions into smaller steps. This framework guides the AI to provide detailed, logical answers. Example: "List the steps a user takes to complete a purchase on a website. For each step, identify potential usability issues." This method uncovers problems at each stage of the user journey. 4. Comparative Analysis Framework Ask the AI to compare two or more options, designs, or features. This framework supports decision-making by highlighting pros and cons. Example: "Compare the checkout process of Website A and Website B. Which one offers a smoother experience and why?" Comparisons help prioritize design improvements based on user impact. 5. Data Summarization Framework Use this framework to condense large volumes of user feedback or research data into key themes. Example: "Summarize the main complaints from 100 user reviews about a travel app." Summaries save time and reveal common patterns quickly. A UX researcher examining summarized user feedback on a tablet device 6. Hypothesis Generation Framework Encourage the AI to propose hypotheses based on user data or observed behaviors. Example: "Based on low engagement rates in the app, suggest possible reasons why users drop off after the onboarding screen." Hypotheses guide further testing and validation. 7. Scenario Simulation Framework Create hypothetical user scenarios to explore how users might interact with a product under different conditions. Example: "Simulate a user trying to book a flight on a website during a high-traffic sale event. Identify potential usability challenges." Simulations help anticipate issues before real users encounter them. 8. Sentiment Analysis Framework Ask the AI to analyze the tone and sentiment behind user comments or reviews. Example: "Analyze the sentiment of these 50 customer feedback entries and categorize them as positive, neutral, or negative." Sentiment insights inform emotional responses and satisfaction levels. 9. Question Refinement Framework Use this framework to improve the clarity and focus of your research questions or survey prompts. Example: "Rewrite this survey question to make it clearer and less biased: 'Do you find the app difficult to use?'" Clear questions lead to more reliable data. 10. Idea Generation Framework Leverage AI to brainstorm new features, design elements, or research methods. Example: "Suggest five innovative features that could enhance user engagement in a meditation app." Idea generation sparks creativity and expands possibilities.

By Philip Burgess | UX Research Leader The rise of artificial intelligence has transformed many fields, including user experience (UX) research. AI tools can analyze large datasets, identify patterns, and even generate user insights faster than ever before. Yet, despite these advances, the future of UX research depends heavily on human skills that AI cannot replicate. Understanding why these skills remain essential helps UX professionals prepare for a future where technology supports but does not replace human judgment. A UX researcher mapping user journeys by hand The Limits of AI in Understanding Human Experience AI excels at processing data and recognizing trends, but it struggles with the nuances of human behavior. UX research often requires interpreting emotions, motivations, and cultural contexts that do not fit neatly into algorithms. For example, when conducting interviews or usability tests, researchers pick up on subtle cues like hesitation, tone changes, or body language. These insights shape design decisions in ways AI cannot fully grasp. AI tools may flag common usability issues, but they cannot replace the empathy needed to understand why users feel frustrated or delighted. Human researchers connect with users on a personal level, building trust that encourages honest feedback. This connection leads to richer data and more meaningful design improvements. Empathy as a Core Skill for UX Researchers Empathy allows UX researchers to step into users’ shoes and see the product through their eyes. This skill goes beyond collecting data; it involves feeling the user’s pain points and aspirations. Empathy drives researchers to ask better questions and explore deeper issues. Consider a case where a UX researcher works with elderly users unfamiliar with smartphones. AI might identify that these users struggle with navigation, but only a human can appreciate the emotional impact of feeling excluded from digital services. This understanding leads to designs that are not only functional but also inclusive and respectful. Critical Thinking and Contextual Judgment UX research requires more than data collection; it demands critical thinking to interpret findings within the right context. Human researchers evaluate how external factors like environment, culture, and social norms influence user behavior. They question assumptions and challenge data that may be misleading. For instance, a spike in app usage might seem positive, but a researcher might discover it results from users struggling to complete tasks, causing repeated attempts. AI might miss this nuance, but a human can investigate further and recommend targeted fixes. Creativity in Problem Solving and Design AI can suggest solutions based on patterns, but creativity remains a uniquely human trait. UX researchers use creative thinking to design experiments, craft interview questions, and envision new user experiences. They combine insights from diverse sources to generate innovative ideas. When redesigning a website, a researcher might brainstorm ways to simplify navigation while maintaining brand identity. This process involves imagination, intuition, and collaboration—qualities AI cannot replicate. Creativity also helps researchers adapt methods to fit different projects and user groups. A UX researcher organizing ideas with sticky notes during a brainstorming session Communication Skills to Share Insights Effectively Gathering insights is only part of the job; UX researchers must communicate findings clearly to designers, developers, and stakeholders. This requires storytelling skills to translate complex data into compelling narratives that inspire action. Humans excel at tailoring messages to different audiences, using visuals, anecdotes, and analogies to make research accessible. For example, a researcher might present user frustrations through a story that resonates with a product team, motivating them to prioritize changes. Collaboration and Adaptability in a Changing Landscape UX research is rarely a solo effort. It involves working closely with cross-functional teams and adapting to evolving project needs. Human skills like collaboration, negotiation, and flexibility enable researchers to navigate challenges and build consensus. AI tools can assist with data analysis, but they cannot replace the interpersonal dynamics that drive successful teamwork. Researchers who listen, empathize, and adjust their approach create stronger partnerships and better outcomes. Preparing for a Future Where AI Supports Human Expertise The future of UX research will blend AI capabilities with human skills. Researchers who embrace technology as a tool rather than a replacement will thrive. This means developing skills that AI cannot mimic: Deep empathy to connect with users Critical thinking to interpret complex data Creativity to design innovative solutions Clear communication to influence teams Collaboration to work effectively across disciplines By focusing on these areas, UX researchers ensure their role remains vital in shaping user-centered products.

By Philip Burgess | UX Research Leader User experience (UX) design is evolving rapidly, and one of the biggest questions today is how much of this work will be done by artificial intelligence. Agentic AI, which can act independently to complete tasks, is advancing quickly. Some experts predict that it could replace up to 50% of UX tasks in the near future. This shift will change how designers work and what skills they need. Let’s explore what this means for UX design and how professionals can prepare. AI-generated UX wireframes on a digital workspace What Agentic AI Means for UX Design Agentic AI refers to systems that do not just assist but take initiative in completing tasks. In UX design, this means AI tools could handle research, prototyping, testing, and even some decision-making. Unlike traditional AI that requires constant human input, agentic AI can learn from data and act independently. For example, an agentic AI might analyze user behavior data, identify pain points, and suggest design changes without a designer needing to guide every step. This could speed up the design process and reduce repetitive work. Tasks AI Can Take Over Not all UX tasks are equally suited for automation. Agentic AI will likely replace tasks that are data-driven, repetitive, or rule-based. Here are some examples: User research analysis : AI can process large sets of user data, spot patterns, and generate reports faster than humans. Wireframe and prototype creation : AI tools can produce initial design drafts based on user requirements and best practices. Usability testing : Automated testing can simulate user interactions and identify issues without manual oversight. Content personalization : AI can tailor content and interfaces to individual users based on their behavior and preferences. These tasks make up a significant portion of the UX workflow, which is why experts estimate that agentic AI could take over about half of the workload. Tasks That Still Need Human Touch Despite AI’s growing capabilities, some UX tasks require human creativity, empathy, and judgment. These include: Understanding complex user emotions : Designing for emotional impact and subtle user needs often requires human insight. Strategic decision-making : Aligning UX with business goals and brand identity needs human input. Collaborating with stakeholders : Communication and negotiation skills are essential when working with teams and clients. Innovative problem-solving : Creating unique solutions for new challenges often goes beyond AI’s current abilities. UX designers will need to focus more on these areas as AI handles routine tasks. How Designers Can Adapt UX professionals should prepare for this shift by developing skills that complement AI rather than compete with it. Here are some practical steps: Learn to work with AI tools : Understanding how to use AI-driven platforms will help designers stay relevant. Focus on soft skills : Communication, empathy, and creativity will become even more valuable. Stay updated on AI developments : Keeping track of new AI capabilities will allow designers to leverage them effectively. Build strategic thinking : Designers should deepen their understanding of business goals and user psychology. By embracing AI as a partner, designers can improve their productivity and impact. UX designer sketching ideas with AI-generated suggestions on a tablet Real-World Examples Some companies already use AI to assist UX design. For instance, platforms like Adobe Sensei offer AI-powered features that automate image editing and layout suggestions. Other tools analyze user feedback to prioritize design fixes. A case study from a major e-commerce site showed that AI-driven personalization increased user engagement by 20%. The AI adjusted product recommendations and interface elements based on individual browsing patterns, improving the overall experience. These examples show how agentic AI can handle significant parts of UX work while freeing designers to focus on higher-level tasks. What This Means for the UX Industry The rise of agentic AI will reshape the UX field. Job roles may change, with fewer people needed for routine tasks and more emphasis on strategic and creative work. Companies might expect faster project turnaround and more data-driven design decisions. At the same time, ethical considerations will grow. Designers will need to ensure AI respects user privacy and avoids bias. Transparency about AI’s role in design will become important for user trust. UX education and training programs will also evolve, incorporating AI literacy and new skill sets.

By Philip Burgess | UX Research Leader User experience (UX) research has always been a vital part of designing products that truly meet user needs. As technology advances, the methods and tools used in UX research evolve as well. Artificial intelligence (AI) is set to change the way UX researchers gather, analyze, and apply data by 2026 and beyond. This transformation will make research faster, more accurate, and more insightful, ultimately improving how products connect with users. AI-driven data visualization on a digital interface How AI Will Speed Up Data Collection Traditional UX research often involves time-consuming tasks like interviews, surveys, and usability testing. AI will automate many of these processes, allowing researchers to collect large amounts of data quickly. For example, AI-powered chatbots can conduct user interviews 24/7, asking relevant questions and recording responses without human intervention. This means researchers can gather diverse user feedback from different time zones and demographics without delays. Additionally, AI can analyze user behavior in real time. Tools that track mouse movements, clicks, and scrolling patterns will use machine learning to identify patterns that humans might miss. This continuous data collection will provide a richer understanding of how users interact with products. Improving Data Analysis with Machine Learning One of the biggest challenges in UX research is making sense of vast amounts of data. AI will help by using machine learning algorithms to analyze qualitative and quantitative data faster and more accurately. For instance, natural language processing (NLP) can analyze open-ended survey responses or interview transcripts, identifying common themes and sentiments without manual coding. Machine learning models can also predict user preferences and pain points by combining data from multiple sources. This predictive power allows UX teams to anticipate problems before users report them, leading to proactive design improvements. Personalizing User Experiences AI will enable UX researchers to create more personalized experiences by understanding individual user needs at scale. By analyzing user data, AI can segment audiences into smaller, more specific groups based on behavior, preferences, and goals. This segmentation helps designers tailor interfaces and features that resonate with each group. For example, an e-commerce site could use AI insights to show different product recommendations or layouts depending on the user's browsing history and purchase behavior. This level of personalization increases user satisfaction and engagement. Enhancing Remote and Automated Testing Remote usability testing has grown in popularity, especially after recent global shifts toward remote work. AI will enhance these tests by automating the setup, monitoring, and analysis phases. AI tools can detect when users struggle with a task by monitoring facial expressions, voice tone, or hesitation during testing sessions. Moreover, AI can simulate user interactions to test new designs before real users try them. These simulations help identify usability issues early, saving time and resources. UX researcher reviewing AI-generated heatmaps of user interactions Ethical Considerations and Challenges While AI offers many benefits, UX researchers must address ethical concerns. Privacy is a major issue since AI relies on collecting and processing large amounts of user data. Researchers need to ensure transparency about data use and obtain informed consent. Bias in AI algorithms is another challenge. If training data is not diverse, AI may produce skewed results that do not represent all user groups fairly. UX teams must regularly audit AI tools and include diverse datasets to minimize bias. Preparing for the AI-Driven Future of UX Research To make the most of AI’s potential, UX researchers should develop new skills. Understanding AI basics, data science, and machine learning concepts will become essential. Collaboration between UX professionals and AI specialists will also grow, combining human empathy with AI’s analytical power. Organizations should invest in AI tools that integrate smoothly with existing UX workflows. Starting with pilot projects can help teams learn how to use AI effectively without disrupting ongoing research. Looking Ahead By 2026, AI will be a standard part of UX research, transforming how teams gather insights and design user-centered products. Faster data collection, smarter analysis, and personalized experiences will improve product quality and user satisfaction. At the same time, ethical use of AI will ensure research respects user privacy and fairness. UX researchers who embrace AI will gain a competitive edge, creating products that truly meet user needs in a rapidly changing digital world. The future of UX research is not just about new technology; it’s about using that technology to better understand and serve people.

By Philip Burgess | UX Research Leader Cognitive walkthroughs offer a powerful way to evaluate how users interact with a product or system. They focus on understanding the user's thought process as they complete tasks, helping designers spot usability problems early. This method is especially useful for improving software interfaces, websites, and other interactive systems where user experience matters. Person conducting a cognitive walkthrough on a digital interface What Is a Cognitive Walkthrough? A cognitive walkthrough is a step-by-step evaluation method where experts simulate a user's problem-solving process while interacting with a system. The goal is to identify where users might struggle or get confused when trying to complete specific tasks. Unlike other usability tests that rely on actual users, cognitive walkthroughs use a detailed analysis of the interface and task flow. This method asks questions like: Will the user know what to do at this step? Will the user notice the correct action to take? Will the user understand the feedback after taking an action? By answering these questions, evaluators can pinpoint design flaws that might block users from achieving their goals. How Cognitive Walkthroughs Work The process starts by selecting key tasks that users need to perform. These tasks should represent typical or critical actions within the system. Then, evaluators walk through each step of the task, imagining the user's perspective. Here’s a typical approach: Define the user profile and goals. Break down the task into individual actions. For each action, ask if the user will know what to do and how to do it. Note any potential problems or confusing elements. Suggest improvements based on findings. This method works best when evaluators have a clear understanding of the target users and the context in which the system will be used. Benefits of Using Cognitive Walkthroughs Cognitive walkthroughs offer several advantages for product teams: Early detection of usability issues : Problems can be found before development or user testing, saving time and costs. Focus on user goals : The method centers on what users want to achieve, ensuring the design supports their needs. No need for actual users : This makes it easier to conduct evaluations when recruiting users is difficult. Clear documentation : The step-by-step nature provides detailed insights that guide design improvements. For example, a team designing a mobile app might use a cognitive walkthrough to check if new users can easily register and start using the app without confusion. Practical Tips for Conducting Effective Cognitive Walkthroughs To get the most from a cognitive walkthrough, consider these tips: Choose realistic user profiles : Base the walkthrough on actual user personas to keep the evaluation relevant. Focus on critical tasks : Prioritize tasks that are essential or known to cause issues. Involve multiple evaluators : Different perspectives can uncover more problems. Document findings clearly : Use notes, screenshots, or diagrams to capture issues and suggestions. Iterate after changes : Repeat walkthroughs after updates to verify improvements. Using these strategies helps ensure the walkthrough leads to meaningful design changes. Checklist and notes for cognitive walkthrough evaluation Examples of Cognitive Walkthroughs in Action Consider a website redesign project. The team wants to ensure new visitors can find product information quickly. They select the task "Find product details and add to cart." During the walkthrough, evaluators notice that the "Add to Cart" button is hidden under a dropdown menu, which might confuse users. They recommend making the button more visible and accessible. In another case, a software company uses cognitive walkthroughs to test a new feature for data export. The walkthrough reveals that users might not understand how to select file formats because the options are labeled with technical terms. The team updates the labels to simpler language, improving clarity. These examples show how cognitive walkthroughs help catch issues that might not be obvious until users struggle. When to Use Cognitive Walkthroughs Cognitive walkthroughs work well in several situations: Early design stages before user testing. When user recruitment is challenging. For complex systems where user goals are clear. To complement other usability methods like heuristic evaluation or user testing. They are less effective if the system is highly exploratory or if user goals are unclear, as the method depends on predictable task flows. Final Thoughts on Cognitive Walkthroughs Cognitive walkthroughs provide a structured way to see a system through the user's eyes. They help teams find and fix usability problems early, improving the overall user experience. By focusing on user goals and task steps, this method offers clear, actionable insights that guide better design decisions.

When designing a product or interface, identifying usability problems early can save time and resources. One effective method to find these issues is heuristic evaluation. This technique involves experts reviewing a product against established usability principles to spot problems that might confuse or frustrate users. Understanding heuristic evaluations helps teams improve user experience before launching or updating a product. Expert conducting heuristic evaluation on a website interface What Is a Heuristic Evaluation? Heuristic evaluation is a usability inspection method where evaluators examine a user interface and judge its compliance with recognized usability principles, known as heuristics. These heuristics act as guidelines to assess how well the interface supports user goals and tasks. The most widely used set of heuristics was developed by Jakob Nielsen. They include principles such as: Visibility of system status Match between system and the real world User control and freedom Consistency and standards Error prevention Recognition rather than recall Flexibility and efficiency of use Aesthetic and minimalist design Help users recognize, diagnose, and recover from errors Help and documentation Evaluators use these principles to systematically check the interface, noting any violations or areas that could confuse users. Why Use Heuristic Evaluations? Heuristic evaluations offer several benefits for product teams: Cost-effective : They require fewer resources than large user testing sessions. A small group of experts can identify many usability problems quickly. Early detection : Problems can be found before development or launch, reducing costly fixes later. Broad coverage : Experts can evaluate multiple aspects of usability, including navigation, error messages, and visual design. Improves user satisfaction : Fixing issues found through heuristic evaluation leads to smoother, more intuitive user experiences. For example, a mobile app team used heuristic evaluation early in development and found confusing navigation labels. Fixing these before user testing saved time and improved user feedback later. How to Conduct a Heuristic Evaluation Follow these steps to perform an effective heuristic evaluation: Select evaluators Choose 3 to 5 usability experts familiar with the product domain. More evaluators increase the chance of finding issues but also require more coordination. Define the scope Decide which parts of the product will be evaluated. This could be a website, mobile app, or specific features. Brief evaluators Provide evaluators with the heuristics to use and any context about the product’s goals and users. Conduct independent reviews Each evaluator reviews the interface alone, noting usability problems and referencing the heuristics they violate. Aggregate findings Combine the results from all evaluators, removing duplicates and prioritizing issues by severity. Report and recommend fixes Create a clear report that describes each problem, its impact, and suggestions for improvement. Common Issues Found in Heuristic Evaluations Heuristic evaluations often reveal problems such as: Unclear feedback Users don’t know if an action succeeded or failed because the system status is not visible. Inconsistent design Buttons, fonts, or colors vary across screens, confusing users. Poor error messages Messages are vague or technical, leaving users unsure how to fix mistakes. Overloaded interfaces Too much information or too many options overwhelm users. Hidden controls Important functions are buried in menus or hard to find. Addressing these issues improves usability and reduces user frustration. Usability expert documenting issues during heuristic evaluation of a mobile app Tips for Getting the Most from Heuristic Evaluations To maximize the value of heuristic evaluations, keep these tips in mind: Use evaluators with diverse backgrounds to catch a wider range of issues. Combine heuristic evaluation with user testing for a fuller picture of usability. Prioritize issues based on how much they affect user goals and frequency of occurrence. Update heuristics or add custom ones tailored to your product’s unique needs. Repeat evaluations after fixes to verify improvements and catch new problems. Final Thoughts on Heuristic Evaluations Heuristic evaluations provide a practical way to find usability problems early and improve user experience. By applying clear principles and expert judgment, teams can uncover issues that might otherwise go unnoticed until users report frustration. This method saves time and money while helping create products that are easier and more enjoyable to use.

By Philip Burgess | UX Research Leader Card sorting is a simple yet powerful technique used to organize information in a way that makes sense to users. It helps designers, developers, and researchers understand how people group and label content, which improves website navigation, app menus, and overall user experience. This post explores what card sorting is, how it works, and practical ways to use it effectively. Cards sorted into categories on a wooden table What Is Card Sorting? Card sorting is a user research method where participants organize topics or items into categories that make sense to them. Each item is written on a card, either physical or digital, and participants group these cards based on their understanding. They may also label the groups with names they find appropriate. This method reveals how users think about information and helps create intuitive structures. It is especially useful when designing websites, apps, or any system that requires clear navigation and content organization. Types of Card Sorting There are three main types of card sorting: Open Card Sorting Participants create their own categories and names. This approach uncovers natural groupings and terminology users prefer. Closed Card Sorting Participants sort cards into predefined categories. This method tests if the existing structure works well for users. Hybrid Card Sorting Combines open and closed methods. Participants sort cards into given categories but can also create new ones if needed. Each type serves different goals. Open sorting is best for early design stages, while closed sorting helps validate existing structures. How to Conduct a Card Sorting Session Running a card sorting session involves a few clear steps: Prepare the Cards List all items or topics you want to organize. Write each on a separate card. Keep the list manageable, usually between 30 and 60 cards. Choose Participants Select people who represent your target audience. Aim for at least 15 participants to get reliable results. Explain the Task Tell participants to group cards in a way that makes sense to them. For open sorting, ask them to name each group. Observe and Record Watch how participants sort cards and take notes. If digital tools are used, data is collected automatically. Analyze Results Look for patterns in how cards are grouped and named. Identify common categories and any confusing items. Tools for Card Sorting Card sorting can be done with physical cards or online tools. Physical cards work well for small groups or in-person sessions. Online tools offer convenience and automatic data collection, especially for remote participants. Popular online card sorting tools include: OptimalSort UserZoom UsabilityHub These platforms allow easy setup, participant invitations, and detailed analysis reports. Benefits of Card Sorting Card sorting offers several advantages: Improves Navigation Helps create menus and categories that users find logical. Enhances User Experience Reduces confusion by aligning content structure with user expectations. Saves Time and Money Identifies problems early before development or redesign. Supports Content Strategy Guides how content is grouped and labeled across platforms. Practical Example of Card Sorting Imagine a team designing an online bookstore. They want to organize book categories so customers find what they want quickly. They prepare cards with book genres like "Science Fiction," "Romance," "History," and "Children’s Books." Using open card sorting, participants group these cards and create labels such as "Fiction," "Non-Fiction," and "Kids." The team notices most users group "Science Fiction" and "Romance" under "Fiction," while "History" falls under "Non-Fiction." Based on this, the bookstore’s menu is structured with clear, user-friendly categories. This approach ensures the website matches how customers think about books, making browsing easier. Hands sorting cards into labeled groups during a card sorting exercise Tips for Effective Card Sorting To get the most from card sorting, consider these tips: Keep the number of cards manageable to avoid participant fatigue. Use clear, simple language on cards. Include a mix of familiar and new items to test understanding. Encourage participants to think aloud during the process. Combine card sorting with other research methods like interviews or usability tests. Analyze results carefully, looking for consensus and outliers. When to Use Card Sorting Card sorting works best when: Designing or redesigning website navigation. Creating menus for apps or software. Organizing large amounts of content. Developing taxonomies or classification systems. Testing existing information structures. It is less useful for very small projects with limited content or when the structure is already well established. Summary Card sorting is a straightforward way to understand how users organize information. It helps create clear, user-friendly navigation and content structures. By involving real users in grouping and labeling content, teams can build products that feel natural and easy to use.