How to Use AI to Expand Your UXR Capabilities

By Erin Jones, Associate Director, UX Research and Kandrea Wade, AVP, UX Research at Material

The primary focus of the UX researcher is, of course, conducting research that illuminates user experiences and uncovers opportunities for improvement and innovation. But in keeping with shifts in technology and operations at businesses of all sizes and industries, our roles and responsibilities continue to expand. Fortunately, as these needs and roles evolve, so do the capabilities of AI.

UX researchers can now deftly wield AI tools to optimize workstreams – enabling a transformation from manual operator to conductor of diverse research processes. Doing so while maintaining rigorous oversight, strategic vision and a human-centric approach allows us to produce refined and impactful research more efficiently than ever before. Let’s explore key use cases that demonstrate AI’s immense potential for UX researchers – alongside some key guardrails to ensure the human element remains the anchor of our work.

Critical considerations

Maintain Human Oversight

Before you leverage AI, it’s crucial to remember that the technology is a tool – a versatile tool, but a tool nonetheless. UX researchers’ expertise in understanding user behavior, designing effective studies and interpreting nuanced findings remains essential. AI doesn’t take over any of those tasks; instead, it empowers researchers to collaborate more effectively across teams and deliver valuable insights faster.

Ensure Data Quality

Also bear in mind that data quality is paramount. The accuracy of AI-powered insights depends on the quality and relevance of the data used. “Garbage in, garbage out” still applies. And even when the data and the tools are above standard, researchers should critically evaluate and validate any AI-generated insights via other research methods.

Establish Ethical Guardrails

In addition, researchers – and anyone else – who use AI must be mindful of data privacy, potential biases in algorithms and other ethical considerations. Giving ethics short shrift can result in long-term damage to credibility and reputation.

AI and UXR in Action

In UXR, AI is most impactful when applied to focused, specific use cases where it can unlock measurable value. Consider use cases in three key areas: Research Operations, Domain Expertise and Moderation + Analysis.

1. Research Operations

Use case: Project management

• The task: Time-consuming, labor-intensive project management tasks such as creating and maintaining schedules, assigning tasks, generating status reports and keeping clients updated can take time away from fundamental research-related tasks. They are also assignments that AI can easily facilitate.

• AI applications: AI algorithms can automate scheduling, assign tasks and even analyze project data to predict possible delays – and then suggest mitigation strategies. In addition, AI tools can summarize meeting minutes and other team communications, simplifying the production of status reports and other updates to stakeholders.

• Guidance: As a rule with all AI applications, not just with project management, be certain not to input sensitive data into your AI tool. Likewise, ensure that the AI technology is not publicly sharing your input. Be sure to consistently check in with stakeholders to ensure automated timelines and mitigation strategies are feasible, and that communications are accurate.

Use case: Recruitment and screening

• The task: Recruiting and screening UX testing participants is a job in and of itself. Add to that the building and maintaining of research repositories, and UX researchers may find themselves short of sufficient time to conduct and analyze the research itself.

• AI applications: Just as they can automate and streamline project management tasks, AI algorithms can assist in recruiting and screening participants. Semi-structured screening and initial 1:1 outreach to potential participants can be automated via AI agents. When deployed with human oversight, these use cases can significantly reduce the degree of upfront manual effort and time associated with foundational UX research processes – particularly valuable when a project is bound to aggressive timelines.

• Guidance: Maintaining rigorous privacy standards for recruitment is imperative, whether or not you use AI tools. As with other AI outputs, always review automated tagging, categorizations and summaries to uncover potential misinterpretations or biases.

2. Domain Expertise

Use case: Knowledge-gathering

• The task: As researchers, we often need to rapidly gain expertise in new domains. Getting up to speed to communicate effectively with stakeholders and tailor projects appropriately can require significant upfront work.

• AI applications: Tools such as academic search engine Consensus and systematic reviews provider Elicit can enable researchers to quickly find, scan and summarize research papers, articles and competitor information to inform strategic research and market analysis. GenAI tools can also help researchers better understand relevant industry metrics and “translate” specialized terms and concepts. Created especially for UX applications, QoQo can help generate initial drafts of user personas and journey maps based on research data, providing a starting point for researchers to learn more about an ecosystem and its users. Emerging AI reasoning tools go beyond summarization; they are becoming transformative enough to recommend design principles, UI improvements and even industry best practices.

• Guidance: Many businesses give nonstandard definitions to standard terms or calculate KPIs slightly differently, so don’t assume that your stakeholders use the identical terminology and metrics, or use them in the same way, that the AI output indicates. Once aligned with your stakeholders, create a glossary of unique definitions and acronyms to incorporate into your AI tools. It’s also critical to double check any other AI-provided assumptions about users in a particular domain.

3. Moderation and Analysis

Use case: Moderation

• The task: Effective, consistent moderation is at the foundation of impactful UX research. It is also among the most time-consuming and effort-intensive elements of any research program. When managing large groups of participants – potentially across different geographies and time zones – researchers often face logistical challenges that inhibit efficiency.

• AI applications: Chatbots fueled by Machine Learning (ML) and Natural Language Processing (NLP) models can moderate some studies, while other tools can transcribe interviews as well as summarize findings. Scale and cost-efficiency are significant benefits – AI moderators can cover much larger sample sizes at a fraction of the cost and time associated with traditional human-moderated interviews, and they provide the option for asynchronous engagement with participants for greater flexibility. AI-powered moderation can also ensure consistency of experience for research participants, reducing unwanted variables that can skew outcomes.

• Guidance: While it offers unmatched scalability and cost-efficiency, AI may miss subtle – yet often crucial – cultural, emotional or behavioral cues that emerge in the process of engaging with research subjects. As a matter of instinct, empathy and experience, human moderators will always be better equipped to identify and understand these issues. AI moderation tools should always be viewed through the lens of augmentation, and not replacement; human involvement and oversight remain critical.

Use case: Dataset analysis

• The task: The amount of data from myriad sources – surveys, interviews, observation, customer support interactions, social listening – can be overwhelming. Manually identifying themes among the datasets is time-consuming, and humans might overlook meaningful patterns in such large and complex datasets.

• AI applications: AI excels in quickly processing and analyzing large datasets, often detecting nuanced themes and patterns people might miss. Dovetail, Looppanel and Notably are among the AI-powered solutions that specialize in speedily culling insights from data. When triangulated with qualitative research, these findings can lead to holistic “big picture” conclusions to inform strategic decision-making. By automatically tagging and categorizing past and current findings, these tools also show promise to enhance the searchability of research repositories in a fraction of the time it would take humans. AI processing capabilities can supercharge teams’ efficiency and scalability.

• Guidance: Algorithmic bias and misinterpretation of audience-specific jargon can lead to faulty outputs; always review results and interpretations, and do not take at face value any that do not track with other findings.

Human-powered Research with AI-powered Assistance

Beyond these use cases, UX researchers can leverage AI to help formulate surveys and guide questions, identify potential accessibility issues early in the design process and develop prototypes, among other applications. Unlocking the full potential of AI technology, however, requires the human in the loop. Since UX focuses on human usability, it requires a deep and context-aware understanding of humans – behavior, habits, motivations and emotions – that AI does not currently possess. Only then will the UX insights be grounded in and applicable to each business’s unique challenges and user needs.

Material’s team of UX researchers thrives at the intersection of deep human understanding and AI-powered efficiency. Explore our UX research services, capabilities and approach to learn more.