How AI is Augmenting, Not Replacing, UX Researchers

The Growing Challenge of AI Bias in Digital Products

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AI is rapidly reshaping our digital landscape, powering everything from recommendation engines to automated customer service and content creation tools. But as these technologies become more widespread, we're facing a significant challenge: AI bias. When AI systems are trained on biased data, they end up reinforcing stereotypes, excluding marginalized groups, and creating inequitable digital experiences that harm both users and businesses.

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This isn't just theoretical, we're seeing real-world consequences. Biased AI has led to resume screening tools that favor male candidates, facial recognition systems that perform poorly on darker skin tones, and language models that perpetuate harmful stereotypes. As AI becomes more deeply integrated into our digital experiences, addressing these biases isn't just an ethical imperative t's essential for creating products that truly work for everyone.

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Why Does AI Bias Matter for UX?

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For those of us in UX and product teams, AI bias isn't just an ethical issue it directly impacts usability, adoption, and trust. Research has shown that biased AI can result in discriminatory hiring algorithms, skewed facial recognition software, and search engines that reinforce societal prejudices (Buolamwini & Gebru, 2018).

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When AI is applied to UX, these biases show up in several ways:

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• Navigation structures that favor certain user behaviors
• Chatbots that struggle to recognize diverse dialects or cultural expressions
• Recommendation engines that create "filter bubbles" 
• Personalization algorithms that make incorrect assumptions 

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These biases create real barriers that exclude users, diminish trust, and ultimately limit how effective our products can be. A 2022 study by the Pew Research Center found that 63% of Americans are concerned about algorithmic decision-making, with those concerns highest among groups that have historically faced discrimination.

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The Root Causes of AI Bias

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To tackle AI bias effectively, we need to understand where it comes from:

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1. Biased Training Data

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AI models learn from the data we feed them. If that data reflects historical inequities or lacks diversity, the AI will inevitably perpetuate these patterns. Think about a language model trained primarily on text written by and about men,  it's going to struggle to represent women's experiences accurately.

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2. Lack of Diversity in Development Teams

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When our AI and product teams lack diversity, blind spots naturally emerge. Teams that are homogeneous in background, experience, and perspective are simply less likely to spot potential biases or consider the needs of users unlike themselves.

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3. Insufficient Testing Across Diverse User Groups

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Without thorough testing across diverse populations, biases often go undetected until after launch when the damage to trust and user experience has already occurred.

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How UX Research Can Mitigate AI Bias

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At Optimal, we believe that continuous, human-centered research is key to designing fair and inclusive AI-driven experiences. Good UX research helps ensure AI-driven products remain unbiased and effective by:

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Ensuring Diverse Representation

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Conducting usability tests with participants from varied backgrounds helps prevent exclusionary patterns. This means:

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• Recruiting research participants who truly reflect the full diversity of your user base
• Paying special attention to traditionally underrepresented groups
• Creating safe spaces where participants feel comfortable sharing their authentic experiences
• Analyzing results with an intersectional lens, looking at how different aspects of identity affect user experiences
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Establishing Bias Monitoring Systems

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Product owners can create ongoing monitoring systems to detect bias:

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• Develop dashboards that track key metrics broken down by user demographics
• Schedule regular bias audits of AI-powered features
• Set clear thresholds for when disparities require intervention
• Make it easy for users to report perceived bias through simple feedback mechanisms
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Advocating for Ethical AI Practices

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Product owners are in a unique position to advocate for ethical AI development:

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• Push for transparency in how AI makes decisions that affect users
• Champion features that help users understand AI recommendations
• Work with data scientists to develop success metrics that consider equity, not just efficiency
• Promote inclusive design principles throughout the entire product development lifecycle
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The Future of AI and Inclusive UX

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As AI becomes more sophisticated and pervasive, the role of customer insight and UX in ensuring fairness will only grow in importance. By combining AI's efficiency with human insight, we can ensure that AI-driven products are not just smart but also fair, accessible, and truly user-friendly for everyone. The question isn't whether we can afford to invest in this work, it's whether we can afford not to.

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Despite AI being the buzzword in UX right now, there are still lots of concerns about how it’s going to impact research roles. One of the biggest concerns we hear is: is AI just going to replace UX researchers altogether?

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The answer, in our opinion, is no. The longer, more interesting answer is that AI is fundamentally transforming what it means to be a UX researcher, and in ways that make the role more strategic, more impactful, and more interesting than ever before.

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What AI Actually Does for Research 

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A 2024 survey by the UX Research Collective found that 68% of UX researchers are concerned about AI's impact on their roles. The fear makes sense, we've all seen how automation has transformed other industries. But what's actually happening is that rather than AI replacing researchers, it's eliminating the parts of research that researchers hate most.

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According to Gartner's 2024 Market Guide for User Research, AI tools can reduce analysis time by 60-70%, but not by replacing human insight. Instead, they handle:

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• Pattern Recognition at Scale: AI can process hundreds of user interviews and identify recurring themes in hours. For a human researcher that same work would take weeks. But those patterns will need human validation because AI doesn't understand why those patterns matter. That's where researchers will continue to add value, and we would argue, become more important than ever. 
• Synthesis Acceleration: According to research by the Nielsen Norman Group, AI can generate first-draft insight summaries 10x faster than humans. But these summaries still need researcher oversight to ensure context, accuracy, and actionable insights aren't lost. 
• Multi-language Analysis: AI can analyze feedback in 50+ languages simultaneously, democratizing global research. But cultural context and nuanced interpretation still require human understanding. 
•  Always-On Insights:  Traditional research is limited by human availability. Tools like AI interviewers can  run 24/7 while your team sleeps, allowing you to get continuous, high-quality user insights. 

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AI is Elevating the Role of Researchers 

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We think that what AI is actually doing  is making UX researchers more important, not less. By automating the less sophisticated  aspects of research, AI is pushing researchers toward the strategic work that only humans can do.

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From Operators to Strategists: McKinsey's 2024 research shows that teams using AI research tools spend 45% more time on strategic planning and only 20% on execution, compared to 30% strategy and 60% execution for traditional teams.

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From Reporters  to Storytellers: With AI handling data processing, researchers can focus on crafting compelling narratives. 

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From Analysts to Advisors: When freed from manual analysis, researchers become embedded strategic partners. 

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Human + AI Collaboration 

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The most effective research teams aren't choosing between human or AI, they're creating collaborative workflows that incorporate AI to augment researchers roles, not replace them: 

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• AI-Powered Data Collection: Automated transcription, sentiment analysis, and preliminary coding happen in real-time during user sessions.
• Human-Led Interpretation: Researchers review AI-generated insights, add context, challenge assumptions, and identify what AI might have missed.
• Collaborative Synthesis: AI suggests patterns and themes; researchers validate, refine, and connect to business context.
• Human Storytelling: Researchers craft narratives, implications, and recommendations that AI cannot generate.

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Is it likely that with AI more and more research tasks will become automated? Absolutely. Basic transcription, preliminary coding, and simple pattern recognition are already AI’s bread and butter. But research has never been about these tasks, it's been about understanding users and driving better decisions and that should always be left to humans. 

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The researchers thriving in 2025 and beyond aren't fighting AI, they're embracing it. They're using AI to handle the tedious 40% of their job so they can focus on the strategic 60% that creates real business value. You  have a choice. You can choose to adopt AI as a tool to elevate your role, or you can view it as a threat and get left behind. Our customers tell us that the researchers choosing elevation are finding their roles more strategic, more impactful, and more essential to product success than ever before.

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AI isn't replacing UX researchers. It's freeing them to do what they've always done best, understand humans and help build better products. And in a world drowning in data but starving for insight, that human expertise has never been more valuable.

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All the way back in 2014, the web passed a pretty significant milestone: 1 billion websites. Of course, fewer than 200 million of these are actually active as of 2019, but there’s an important underlying point. People love to create. If the current digital age that we live in has taught us anything, it’s that it’s never been as easy to get information and ideas out into the world.

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Understandably, this ability has been used – and often misused. Overloaded, convoluted websites are par for the course, with a common tactic for website renewal being to simply update them with a new coat of paint while ignoring the swirling pile of outdated and poorly organized content below.

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So what are you supposed to do when trying to address this problem on your own website or digital project? Well, there’s a fairly robust technique called top tasks management. Here, we’ll go over exactly what it is and how you can use it.

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Getting to grips with top tasks

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Ideally, all websites would be given regular, comprehensive reviews. Old content could be revisited and analyzed to see whether it’s still actually serving a purpose. If not, it could be reworked or just removed entirely. Based on research, content creators could add new content to address user needs. Of course, this is just the ideal. The reality is that there’s never really enough time or resource to manage the growing mass of digital content in this way. The solution is to hone in on what your users actually use your website for and tailor the experience accordingly by looking at top tasks.

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What are top tasks? They're basically a small set of tasks (typically around 5, but up to 10 is OK too) that are most important to your users. The thinking goes that if you get these core tasks right, your website will be serving the majority of your users and you’ll be more likely to retain them. Ignore top tasks (and any sort of task analysis), and you’ll likely find users leaving your website to find something else that better fits their needs.

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The counter to top tasks is tiny tasks. These are everything on a website that’s not all that important for the people actually using it. Commonly, tiny tasks are driven more by the organization’s needs than those of the users. Typically, the more important a task is to a user, the less information there is to support it. On the other hand, the less important a task is to a user, the more information there is. Tiny tasks stem very much from ‘organization first’ thinking, wherein user needs are placed lower on the list of considerations.
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According to Jerry McGovern (who penned an excellent write-up of top tasks on A List Apart), the top tasks model says “Focus on what really matters (the top tasks) and defocus on what matters less (the tiny tasks).”

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How to identify top tasks

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Figuring out your top tasks is an important step in clearing away the fog and identifying what actually matters to your users. We’ll call this stage of the process task discovery, and these are the steps:

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1. Gather: Work with your organization to gather a list of all customer tasks
2. Refine: Take this list of tasks to a smaller group of stakeholders and work it down into a shortlist
3. User feedback: Go out to your users and get a representative sample to vote on them
4. Finalise: Assemble a table of tasks with the one with the highest number of votes at the top and the lowest number of votes at the bottom

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We’ll go into detail on the above steps, explaining the best way of handling each one. Keep in mind that this process isn’t something you’ll be able to complete in a week – it’s more likely a 6 to 8-week project, depending on the size of your website, how large your user base is and the receptiveness of your organization to help out.

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Step 1: Gather – Figure out the long list of tasks

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The first part of the task process is to get out into the wider organization and discover what your users are actually trying to accomplish on your website or by using your products. It’s all about getting into the minds of your users – trying to see the world through their eyes, effectively.

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If you’re struggling to think of places where you might find customer tasks, here are some of the best sources:

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• Analytics: Take a deep dive into the analytics of your website or product to find out how people are using them. For websites, you’ll want to look at pages with high traffic and common downloads or interactions. The same applies to products – although the data you have access to will depend on the analytics systems in place.
• Customer support teams: Your own internal support teams can be a great source of user tasks. Support teams commonly spend all day speaking to users, and as a result, are able to build up a cohesive understanding of the types of tasks users commonly attempt.
• Sales teams: Similarly, sales teams are another good source of task data. Sales teams typically deal with people before they become your users, but a part of their job is to understand the problems they’re trying to solve and how your website or product can help.
• Direct customer feedback: Check for surveys your organization has run in the past to see whether any task data already exists.
• Social media: Head to Twitter, Facebook and LinkedIn to see what people are talking about with regards to your industry. What tasks are being mentioned?

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It’s important to note that you need to cast a wide net when gathering task data. You can’t just rely on analytics data. Why? Well, downloads and page visits only reflect what you have, but not what your users might actually be searching for.

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As for search, Jerry McGovern explains why it doesn’t actually tell the entire story: “When we worked on the BBC intranet, we found they had a feature called “Top Searches” on their homepage. The problem was that once they published the top searches list, these terms no longer needed to be searched for, so in time a new list of top searches emerged! Similarly, top tasks tend to get bookmarked, so they don’t show up as much in search. And the better the navigation, the more likely the site search is to reflect tiny tasks.”

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At the end of the initial task-gathering stage you should be left with around 300 to 500 tasks. Of course, this can scale up or down depending on the size of the website or product.

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Step 2: Refine – Create your shortlist

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Now that you’ve got your long list of tasks, it’s time to trim them back until you’ve got a shortlist of 100 or less. Keep in mind that working through your long list of tasks is going to take some time, so plan for this process to take at least 4 weeks (but likely more).

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It’s important to involve stakeholders from across the organization during the shortlist process. Bring in people from support, sales, product, marketing and leadership areas of the organization. In addition to helping you to create a more concise and usable list, the shortlist process helps your stakeholders to think about areas of overlap and where they may need to work together.

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When working your list down to something more usable, try and consolidate and simplify. Stay away from product names as well as internal organization and industry jargon. With your tasks, you essentially want to focus on the underlying thing that a user is trying to do. If you were focusing on tasks for a bank, opt for “Transactions” instead of “Digital mobile payments”. Similarly, bring together tasks where possible. “Customer support”, “Help and support” and “Support center” can all be merged.

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At a very technical level, it also helps to avoid lengthy tasks. Stick to around 7 to 8 words and try and avoid verbs, using them only when there’s really no other option. You’ll find that your task list becomes quite to navigate when tasks begin with “look”, “find” and “get”. Finally, stay away from specific audiences and demographics. You want to keep your tasks universal.

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Step 3: User feedback – Get users to vote

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With your shortlist created, it’s time to take it to your users. Using a survey tool like Optimal's Surveys, add in each one of your shortlisted tasks and have users rank 5 tasks on a scale from 1 to 5, with 5 being the most important and 1 being the least important.

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If you’re thinking that your users will never take the time to work through such a long list, consider that the very length of the list means they’ll seek out the tasks that matter to them and ignore the ones that don’t.

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Step 4: Finalize – Analyze your results

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Now for the task analysis side of the project. What you want at the end of the user survey end of the project is a league table of entire shortlist of tasks. We’re going to use the example from Cisco’s top tasks project, which has been documented over at A List Apart by Gerry McGovern (who actually ran the project). The entire article is worth a read as it covers the process of running a top task project for a large organization.

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Here’s what a league table of the top 20 tasks looks like from Cisco:

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Here’s the breakdown of the vote for Cisco’s tasks:

• 3 tasks got the first 25 percent of the vote
• 6 tasks got 25-50 percent of the vote
• 14 tasks got 50-75 percent of the vote
• 44 tasks got 75-100 percent of the vote

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While the pattern may seem surprising, it’s actually not unusual. As Jerry explains: “We have done this process over 400 times and the same patterns emerge every single time.”

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Final thoughts

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Focusing on top tasks management is really a practice that needs to be conducted on a semi-regular basis. The approach benefits organizations in a multitude of ways, bringing different teams and people together to figure out how to best address why your users are coming to your website and what they actually need from you.

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As we explained at the beginning of this article, top tasks is really about clearing away the fog and understanding on what really matters. Instead of spreading yourself thin and focusing on a host of tiny tasks, hone in on those top tasks that actually matter to your users.

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Understanding how to improve your website

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The top tasks approach is an effective way of giving you a clear idea of what you should be focusing on when designing or redesigning your website, but this should really just be one aspect of the work you do.

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Utilizing a host of other UX research methods can give you a much more comprehensive idea of what’s working and what’s not. With card sorting, for example, you can learn how your users think the content on your website should be arranged. Then, with this data in hand, you can use tree testing to assemble draft structures of your website and test how people navigate their way through it. You can keep iterating on these structures to ensure you’ve created the most user-friendly navigation.
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Take a look at our 101 guides to learn more about card sorting and tree testing, as well as the other user research methods you can use to make solid improvements to your website. If you’d rather just start putting methods into practice using user research tools, take our UX platform for a spin for free here.