Top Tasks in UX: How to Identify What Really Matters to Your Users

As AI takes on a bigger role in product decision-making and user experience design, ethical concerns are becoming more pressing for product teams. From privacy risks to unintended biases and manipulation, AI raises important questions: How do we balance automation with human responsibility? When should AI make decisions, and when should humans stay in control?

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These aren't just theoretical questions they have real consequences for users, businesses, and society. A chatbot that misunderstands cultural nuances, a recommendation engine that reinforces harmful stereotypes, or an AI assistant that collects too much personal data can all cause genuine harm while appearing to improve user experience.

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The Ethical Challenges of AI

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Privacy & Data Ethics

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AI needs personal data to work effectively, which raises serious concerns about transparency, consent, and data stewardship:

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• Data Collection Boundaries – What information is reasonable to collect? Just because we can gather certain data doesn't mean we should.
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• Informed Consent – Do users really understand how their data powers AI experiences? Traditional privacy policies often don't do the job.
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• Data Longevity – How long should AI systems keep user data, and what rights should users have to control or delete this information?
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• Unexpected Insights – AI can draw sensitive conclusions about users that they never explicitly shared, creating privacy concerns beyond traditional data collection.

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A 2023 study by the Baymard Institute found that 78% of users were uncomfortable with how much personal data was used for personalized experiences once they understood the full extent of the data collection. Yet only 12% felt adequately informed about these practices through standard disclosures.

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Bias & Fairness

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AI can amplify existing inequalities if it's not carefully designed and tested with diverse users:

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• Representation Gaps – AI trained on limited datasets often performs poorly for underrepresented groups.
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• Algorithmic Discrimination – Systems might unintentionally discriminate based on protected characteristics like race, gender, or disability status.
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• Performance Disparities – AI-powered interfaces may work well for some users while creating significant barriers for others.
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• Reinforcement of Stereotypes – Recommendation systems can reinforce harmful stereotypes or create echo chambers.

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Recent research from Stanford's Human-Centered AI Institute revealed that AI-driven interfaces created 2.6 times more usability issues for older adults and 3.2 times more issues for users with disabilities compared to general populations, a gap that often goes undetected without specific testing for these groups.

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User Autonomy & Agency

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Over-reliance on AI-driven suggestions may limit user freedom and sense of control:

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• Choice Architecture – AI systems can nudge users toward certain decisions, raising questions about manipulation versus assistance.
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• Dependency Concerns – As users rely more on AI recommendations, they may lose skills or confidence in making independent judgments.
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• Transparency of Influence – Users often don't recognize when their choices are being shaped by algorithms.
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• Right to Human Interaction – In critical situations, users may prefer or need human support rather than AI assistance.

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A longitudinal study by the University of Amsterdam found that users of AI-powered decision-making tools showed decreased confidence in their own judgment over time, especially in areas where they had limited expertise.

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Accessibility & Digital Divide

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AI-powered interfaces may create new barriers:

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• Technology Requirements – Advanced AI features often require newer devices or faster internet connections.
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• Learning Curves – Novel AI interfaces may be particularly challenging for certain user groups to learn.
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• Voice and Language Barriers – Voice-based AI often struggles with accents, dialects, and non-native speakers.
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• Cognitive Load – AI that behaves unpredictably can increase cognitive burden for users.

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Accountability & Transparency

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Who's responsible when AI makes mistakes or causes harm?

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• Explainability – Can users understand why an AI system made a particular recommendation or decision?
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• Appeal Mechanisms – Do users have recourse when AI systems make errors?
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• Responsibility Attribution – Is it the designer, developer, or organization that bears responsibility for AI outcomes?
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• Audit Trails – How can we verify that AI systems are functioning as intended?

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How Product Owners Can Champion Ethical AI Through UX

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At Optimal, we advocate for research-driven AI development that puts human needs and ethical considerations at the center of the design process. Here's how UX research can help:

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User-Centered Testing for AI Systems

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AI-powered experiences must be tested with real users to identify potential ethical issues:

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• Longitudinal Studies – Track how AI influences user behavior and autonomy over time.
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• Diverse Testing Scenarios – Test AI under various conditions to identify edge cases where ethical issues might emerge.
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• Multi-Method Approaches – Combine quantitative metrics with qualitative insights to understand the full impact of AI features.
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• Ethical Impact Assessment – Develop frameworks specifically designed to evaluate the ethical dimensions of AI experiences.

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Inclusive Research Practices

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Ensuring diverse user participation helps prevent bias and ensures AI works for everyone:

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• Representation in Research Panels – Include participants from various demographic groups, ability levels, and socioeconomic backgrounds.
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• Contextual Research – Study how AI interfaces perform in real-world environments, not just controlled settings.
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• Cultural Sensitivity – Test AI across different cultural contexts to identify potential misalignments.
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• Intersectional Analysis – Consider how various aspects of identity might interact to create unique challenges for certain users.

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Transparency in AI Decision-Making

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UX teams should investigate how users perceive AI-driven recommendations:

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• Mental Model Testing – Do users understand how and why AI is making certain recommendations?
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• Disclosure Design – Develop and test effective ways to communicate how AI is using data and making decisions.
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• Trust Research – Investigate what factors influence user trust in AI systems and how this affects experience.
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• Control Mechanisms – Design and test interfaces that give users appropriate control over AI behavior.

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The Path Forward: Responsible Innovation

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As AI becomes more sophisticated and pervasive in UX design, the ethical stakes will only increase. However, this doesn't mean we should abandon AI-powered innovations. Instead, we need to embrace responsible innovation that considers ethical implications from the start rather than as an afterthought.

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AI should enhance human decision-making, not replace it. Through continuous UX research focused not just on usability but on broader human impact, we can ensure AI-driven experiences remain ethical, inclusive, user-friendly, and truly beneficial.

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The most successful AI implementations will be those that augment human capabilities while respecting human autonomy, providing assistance without creating dependency, offering personalization without compromising privacy, and enhancing experiences without reinforcing biases.

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A Product Owner's Responsibility: Leading the Charge for Ethical AI

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As UX professionals, we have both the opportunity and responsibility to shape how AI is integrated into the products people use daily. This requires us to:

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• Advocate for ethical considerations in product requirements and design processes
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• Develop new research methods specifically designed to evaluate AI ethics
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• Collaborate across disciplines with data scientists, ethicists, and domain experts
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• Educate stakeholders about the importance of ethical AI design
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• Amplify diverse perspectives in all stages of AI development

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By embracing these responsibilities, we can help ensure that AI serves as a force for positive change in user experience enhancing human capabilities while respecting human values, autonomy, and diversity.

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The future of AI in UX isn't just about what's technologically possible; it's about what's ethically responsible. Through thoughtful research, inclusive design practices, and a commitment to human-centered values, we can navigate this complex landscape and create AI experiences that truly benefit everyone.

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In the field of user research, every method is either qualitative, quantitative – or both. Understandably, there’s some confusion around these 2 approaches and where the different methods are applicable. This article provides a handy breakdown of the different terms and where and why you’d want to use qualitative or quantitative research methods.

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Qualitative research

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Let’s start with qualitative research, an approach that’s all about the ‘why’. It’s exploratory and not about numbers, instead focusing on reasons, motivations, behaviors and opinions – it’s best at helping you gain insight and delve deep into a particular problem. This type of data typically comes from conversations, interviews and responses to open questions. The real value of qualitative research is in its ability to give you a human perspective on a research question. Unlike quantitative research, this approach will help you understand some of the more intangible factors – things like behaviors, habits and past experiences – whose effects may not always be readily apparent when you’re conducting quantitative research. A qualitative research question could be investigating why people switch between different banks, for example.

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When to use qualitative research

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Qualitative research is best suited to identifying how people think about problems, how they interact with products and services, and what encourages them to behave a certain way. For example, you could run a study to better understand how people feel about a product they use, or why people have trouble filling out your sign up form. Qualitative research can be very exploratory (e.g., user interviews) as well as more closely tied to evaluating designs (e.g., usability testing). Good qualitative research questions to ask include:

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• Why do customers never add items to their wishlist on our website?
• How do new customers find out about our services?
• What are the main reasons people don’t sign up for our newsletter?

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How to gather qualitative data

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There’s no shortage of methods to gather qualitative data, which commonly takes the form of interview transcripts, notes and audio and video recordings. Here are some of the most widely-used qualitative research methods:

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• Usability test – Test a product with people by observing them as they attempt to complete various tasks.
• User interview – Sit down with a user to learn more about their background, motivations and pain points.
• Contextual inquiry – Learn more about your users in their own environment by asking them questions before moving onto an observation activity.
• Focus group – Gather 6 to 10 people for a forum-like session to get feedback on a product.

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How many participants will you need?

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You don’t often need large numbers of participants for qualitative research, with the average range usually somewhere between 5 to 10 people. You’ll likely require more if you're focusing your work on specific personas, for example, in which case you may need to study 5-10 people for each persona. While this may seem quite low, consider the research methods you’ll be using. Carrying out large numbers of in-person research sessions requires a significant time investment in terms of planning, actually hosting the sessions and analyzing your findings.

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Quantitative research

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On the other side of the coin you’ve got quantitative research. This type of research is focused on numbers and measurement, gathering data and being able to transform this information into statistics. Given that quantitative research is all about generating data that can be expressed in numbers, there multiple ways you make use of it. Statistical analysis means you can pull useful facts from your quantitative data, for example trends, demographic information and differences between groups. It’s an excellent way to understand a snapshot of your users. A quantitative research question could involve investigating the number of people that upgrade from a free plan to a paid plan.

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When to use quantitative research

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Quantitative research is ideal for understanding behaviors and usage. In many cases it's a lot less resource-heavy than qualitative research because you don't need to pay incentives or spend time scheduling sessions etc). With that in mind, you might do some quantitative research early on to better understand the problem space, for example by running a survey on your users. Here are some examples of good quantitative research questions to ask:

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• How many customers view our pricing page before making a purchase decision?
• How many customers search versus navigate to find products on our website?
• How often do visitors on our website change their password?

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How to gather quantitative data

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Commonly, quantitative data takes the form of numbers and statistics.

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Here are some of the most popular quantitative research methods:

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• Card sorts – Find out how people categorize and sort information on your website.
• First-click tests – See where people click first when tasked with completing an action.
• A/B tests – Compare 2 versions of a design in order to work out which is more effective.
• Clickstream analysis – Analyze aggregate data about website visits.

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How many participants will you need?

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While you only need a small number of participants for qualitative research, you need significantly more for quantitative research. Quantitative research is all about quantity. With more participants, you can generate more useful and reliable data you can analyze. In turn, you’ll have a clearer understanding of your research problem. This means that quantitative research can often involve gathering data from thousands of participants through an A/B test, or with 30 through a card sort. Read more about the right number of participants to gather for your research.

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Mixed methods research

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While there are certainly times when you’d only want to focus on qualitative or quantitative data to get answers, there’s significant value in utilizing both methods on the same research projects.Interestingly, there are a number of research methods that will generate both quantitative and qualitative data. Take surveys as an example. A survey could include questions that require written answers from participants as well as questions that require participants to select from multiple choices.

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Looking back at the earlier example of how people move from a free plan to a paid plan, applying both research approaches to the question will yield a more robust or holistic answer. You’ll know why people upgrade to the paid plan in addition to how many. You can read more about mixed methods research in this article:

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• What is mixed methods research?

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Where to from here?

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Now that you know the difference between qualitative and quantitative research, the best way to build confidence is to start testing. Hands-on experience is the fastest path to deeper insight. At Optimal, we make it easy to run your first study, no matter your role or research experience.

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• Explore our 101 guides to user research
• Start a free trial and discover insights that drive real business impact
• How to encourage people to participate in your study – Seemingly one of the hardest parts of conducting research is finding willing participants. It’s actually not that difficult.

There’s no doubt usability is a key element of all great user experiences, how do we apply and test usability principles for a website? This article looks at usability principles in web design, how to test it, practical tips for success and a look at our remote testing tool, Treejack.

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A definition of usability for websites 🧐📖

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Web usability is defined as the extent to which a website can be used to achieve a specific task or goal by a user. It refers to the quality of the user experience and can be broken down into five key usability principles:

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• Ease of use: How easy is the website to use? How easily are users able to complete their goals and tasks? How much effort is required from the user?
• Learnability: How easily are users able to complete their goals and tasks the first time they use the website?
• Efficiency: How quickly can users perform tasks while using your website?
• User satisfaction: How satisfied are users with the experience the website provides? Is the experience a pleasant one?
• Impact of errors: Are users making errors when using the website and if so, how serious are the consequences of those errors? Is the design forgiving enough make it easy for errors to be corrected?

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Why is web usability important? 👀

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Aside from the obvious desire to improve the experience for the people who use our websites, web usability is crucial to your website’s survival. If your website is difficult to use, people will simply go somewhere else. In the cases where users do not have the option to go somewhere else, for example government services, poor web usability can lead to serious issues. How do we know if our website is well-designed? We test it with users.

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Testing usability: What are the common methods? 🖊️📖✏️📚

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There are many ways to evaluate web usability and here are the common methods:

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• Moderated usability testing: Moderated usability testing refers to testing that is conducted in-person with a participant. You might do this in a specialised usability testing lab or perhaps in the user’s contextual environment such as their home or place of business. This method allows you to test just about anything from a low fidelity paper prototype all the way up to an interactive high fidelity prototype that closely resembles the end product.
• Moderated remote usability testing: Moderated remote usability testing is very similar to the previous method but with one key difference- the facilitator and the participant/s are not in the same location. The session is still a moderated two-way conversation just over skype or via a webinar platform instead of in person. This method is particularly useful if you are short on time or unable to travel to where your users are located, e.g. overseas.
• Unmoderated remote usability testing: As the name suggests, unmoderated remote usability testing is conducted without a facilitator present. This is usually done online and provides the flexibility for your participants to complete the activity at a time that suits them. There are several remote testing tools available ( including our suite of tools ) and once a study is launched these tools take care of themselves collating the results for you and surfacing key findings using powerful visual aids.
• Guerilla testing: Guerilla testing is a powerful, quick and low cost way of obtaining user feedback on the usability of your website. Usually conducted in public spaces with large amounts of foot traffic, guerilla testing gets its name from its ‘in the wild’ nature. It is a scaled back usability testing method that usually only involves a few minutes for each test but allows you to reach large amounts of people and has very few costs associated with it.
• Heuristic evaluation: A heuristic evaluation is conducted by usability experts to assess a website against recognized usability standards and rules of thumb (heuristics). This method evaluates usability without involving the user and works best when done in conjunction with other usability testing methods eg Moderated usability testing to ensure the voice of the user is heard during the design process.
• Tree testing: Also known as a reverse card sort, tree testing is used to evaluate the findability of information on a website. This method allows you to work backwards through your information architecture and test that thinking against real world scenarios with users.
• First click testing: Research has found that 87% of users who start out on the right path from the very first click will be able to successfully complete their task while less than half ( 46%) who start down the wrong path will succeed. First click testing is used to evaluate how well a website is supporting users and also provides insights into design elements that are being noticed and those that are being ignored.
• Hallway testing: Hallway testing is a usability testing method used to gain insights from anyone nearby who is unfamiliar with your project. These might be your friends, family or the people who work in another department down the hall from you. Similar to guerilla testing but less ‘wild’. This method works best at picking up issues early in the design process before moving on to testing a more refined product with your intended audience.

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Online usability testing tool: Tree testing 🌲🌳🌿

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Tree testing is a remote usability testing tool that uses tree testing to help you discover exactly where your users are getting lost in the structure of your website. Treejack uses a simplified text-based version of your website structure removing distractions such as navigation and visual design allowing you to test the design from its most basic level.

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Like any other tree test, it uses task based scenarios and includes the opportunity to ask participants pre and post study questions that can be used to gain further insights. Tree testing is a useful tool for testing those five key usability principles mentioned earlier with powerful inbuilt features that do most of the heavy lifting for you. Tree testing records and presents the following for each task:

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• complete details of the pathways followed by each participant
• the time taken to complete each task
• first click data
• the directness of each result
• visibility on when and where participants skipped a task

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Participant paths data in our tree testing tool 🛣️

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The level of detail recorded on the pathways followed by your participants makes it easy for you to determine the ease of use, learnability, efficiency and impact of errors of your website. The time taken to complete each task and the directness of each result also provide insights in relation to those four principles and user satisfaction can be measured through the results to your pre and post survey questions.

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The first click data brings in the added benefits of first click testing and knowing when and where your participants gave up and moved on can help you identify any issues.Another thing tree testing does well is the way it brings all data for each task together into one comprehensive overview that tells you everything you need to know at a glance. Tree testing's task overview- all the key information in one placeIn addition to this, tree testing also generates comprehensive pathway maps called pietrees.

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Each junction in the pathway is a piechart showing a statistical breakdown of participant activity at that point in the site structure including details about: how many were on the right track, how many were following the incorrect path and how many turned around and went back. These beautiful diagrams tell the story of your usability testing and are useful for communicating the results to your stakeholders.

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Usability testing tips 🪄

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Here are seven practical usability testing tips to get you started:

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• Test early and often: Usability testing isn’t something that only happens at the end of the project. Start your testing as soon as possible and iterate your design based on findings. There are so many different ways to test an idea with users and you have the flexibility to scale it back to suit your needs.
• Try testing with paper prototypes: Just like there are many usability testing methods, there are also several ways to present your designs to your participant during testing. Fully functioning high fidelity prototypes are amazing but they’re not always feasible (especially if you followed the previous tip of test early and often). Paper prototypes work well for usability testing because your participant can draw on them and their own ideas- they’re also more likely to feel comfortable providing feedback on work that is less resolved! You could also use paper prototypes to form the basis for collaborative design sessions with your users by showing them your idea and asking them to redesign or design the next page/screen.
• Run a benchmarking round of testing: Test the current state of the design to understand how your users feel about it. This is especially useful if you are planning to redesign an existing product or service and will save you time in the problem identification stages.
• Bring stakeholders and clients into the testing process: Hearing how a product or service is performing direct from a user can be quite a powerful experience for a stakeholder or client. If you are running your usability testing in a lab with an observation room, invite them to attend as observers and also include them in your post session debriefs. They’ll gain feedback straight from the source and you’ll gain an extra pair of eyes and ears in the observation room. If you’re not using a lab or doing a different type of testing, try to find ways to include them as observers in some way. Also, don’t forget to remind them that as observers they will need to stay silent for the entire session beyond introducing themselves so as not to influence the participant - unless you’ve allocated time for questions.
• Make the most of available resources: Given all the usability testing options out there, there’s really no excuse for not testing a design with users. Whether it’s time, money, human resources or all of the above making it difficult for you, there’s always something you can do. Think creatively about ways to engage users in the process and consider combining elements of different methods or scaling down to something like hallway testing or guerilla testing. It is far better to have a less than perfect testing method than to not test at all.
• Never analyse your findings alone: Always analyse your usability testing results as a team or with at least one other person. Making sense of the results can be quite a big task and it is easy to miss or forget key insights. Bring the team together and affinity diagram your observations and notes after each usability testing session to ensure everything is captured. You could also use Reframer to record your observations live during each session because it does most of the analysis work for you by surfacing common themes and patterns as they emerge. Your whole team can use it too saving you time.
• Engage your stakeholders by presenting your findings in creative ways: No one reads thirty page reports anymore. Help your stakeholders and clients feel engaged and included in the process by delivering the usability testing results in an easily digestible format that has a lasting impact. You might create an A4 size one page summary, or maybe an A0 size wall poster to tell everyone in the office the story of your usability testing or you could create a short video with snippets taken from your usability testing sessions (with participant permission of course) to communicate your findings. Remember you’re also providing an experience for your clients and stakeholders so make sure your results are as usable as what you just tested.

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Related reading 🎧💌📖

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• Usability testing with Optimal Workshop
• Usability Testing: Five types of usability tests
• Usability Heuristics: How to conduct a heuristic evaluation
• Interface Usability:User Interface Design Basics