Use of AI In UX: Insights from Recent Research

Current Applications, Challenges, and Future Directions

Photo by Amélie Mourichon on Unsplash

Artificial intelligence (AI) is rapidly transforming how User Experience (UX) professionals conduct research, generate designs, and evaluate products. In this article we look at three recent studies that explore different aspects of AI's impact on UX design, offering us practical insights on how to effectively integrate these emerging technologies into our workflows.

Study 1: How UX Practitioners Use Generative AI in Industry

Takafoli, Li, and Mäkelä (2024) conducted interviews with 24 UX practitioners from multiple companies and countries to understand how AI is being used in real-world practice. Their research reveals both opportunities and significant gaps in current industry adoption.

Some of the main findings from this study are discussed below:

• Policy vacuum: Despite widespread interest in generative AI, the researchers found a "significant lack of GenAI company policies." Most companies are either providing informal cautionary advice or leaving responsibility to individual employees. This creates uncertainty and inconsistency in how AI tools are implemented.

• Individual rather than team-based usage: UX teams rarely have established team-wide practices for AI. UXers typically adopt AI tools individually, with limited knowledge sharing or collaborative approaches. This leads to fragmented implementation and missed opportunities for building on collective expertise.

• Task-specific adoption patterns: UX practitioners favour AI for writing-based tasks like documentation, interview question generation, and data analysis. However, they report significant limitations when using AI for design-focused activities like wireframing and prototyping, suggesting current tools aren't meeting core design needs.

• Training gap: UX professionals explicitly call for better training to enhance their abilities to generate effective prompts and evaluate AI output quality. Without appropriate skills, many struggle to use AI tools effectively or critically assess their outputs.

These findings highlight the organisational and skill-based challenges of AI adoption in UX practice. While individual practitioners are experimenting with AI tools, they lack the organisational support, team-based practices, and training needed for effective integration.

Study 2: A Systematic Review of AI in UX Design

Stige, Zamani, Mikalef, and Zhu (2023) conducted a systematic literature review of 46 research articles to map how AI is currently used in UX design and identify future research directions.

They found the following themes:

• Uneven distribution across design phases: The researchers found AI applications focus predominantly on producing design solutions (35% of papers) and development (31%), while fewer address the earlier stages of understanding context and requirements. This imbalance suggests that AI tools may not be supporting the full design process effectively.

• Complementary rather than replacement approach: Successful AI integration typically augments rather than replaces UX professionals. As the authors note, "optimising layouts using ML is time efficient and leads to new ideas, but requires human involvement for refining the solution." The most effective approach is "designer-AI collaboration rather than relying on the one or the other."

• Technical capabilities and limitations: The study identifies several promising AI applications, including automatically generating personas from user data, converting low-fidelity sketches to higher-fidelity prototypes, and detecting UI errors. However, current AI tools often struggle with understanding design contexts and maintaining design coherence.

• Need for design process evolution: The researchers suggest that the design process itself will need to evolve with AI integration. Designers will need to use new tools to evolve the design process, requiring new skills and approaches to leverage AI effectively.

This systematic review provides a comprehensive overview of current AI capabilities in UX, highlighting both the technological possibilities and the need for thoughtful integration that respects the design process.

Study 3: AI Assistance for UX Through Human-Centred AI

The last study is a systematic review by Lu et al. (2024) that included 359 papers, analysing AI support for UX through the lens of Human-Centred AI. Their work examines how AI capabilities align with UX practitioners' needs and goals.

Number of papers on AI and UX (source: Lu et al.)

The main findings include:

• Technology-centric vs. human-centred approaches: Only 24.3% of papers in their sample used human-centred methodologies to understand user needs. This technology-centric tendency limits the practical value of many AI applications for UX design.

• Empathy building vs. automation: A key insight is that UX methodologies are primarily about building empathy with users, not just completing tasks. Simplistic automation can obstruct this critical function. For example, when generating personas, AI might provide "statistically most likely" information about users without fostering the empathy-building that makes personas valuable.

• Individual screens vs. user experiences: Most AI research focuses on individual UI screens rather than the connections between them. This fails to capture the full user experience which can span across multiple interfaces and include numerous user flows. As a result, it has limited applicability as the industry shifts toward designing holistic experiences rather than isolated screens.

• Task considerations: Not all UX tasks should be delegated to AI. For qualitative analysis, for instance, AI may be appropriate for applying an established coding framework but less suitable for the initial development of that framework, which builds researchers' understanding of the data.

This review highlights the fundamental tension between AI's computational approach and UX's human-centred philosophy, suggesting that effective integration requires a deeper understanding of UX practices and goals.

Common Themes and Practical Implications

While each study approaches AI in UX from a different angle, several important themes emerge across all three:

1. The Need for Strategic AI Integration

All three studies highlight that random, ad-hoc adoption of AI tools is insufficient. Organisations need clear strategies and policies for AI use in UX, a thoughtful approach to which design activities are appropriate for AI support, and careful consideration of how AI complements human-centred design processes.

UX leaders should develop formal AI policies and integration strategies rather than leaving adoption to individual practitioners. These should address when and how AI should be used in different design scenarios, establish guidelines for data privacy and intellectual property, and create evaluation frameworks for assessing AI's impact.

2. The Importance of Human-AI Collaboration

All three studies emphasise that AI works best as a collaborative partner rather than a replacement for human designers. Stige et al. explicitly recommend designer-AI collaboration, while Lu et al. emphasise preserving human agency in creative and interpretive tasks. Finally, Takafoli et al. note that practitioners currently favour AI for supporting tasks rather than core design work.

We should tackle this by considering tasks and their demands before deciding how to use AI. For example, we can try designing AI workflows that maintain human control over creative decisions while leveraging AI for supportive tasks like data analysis, alternative generation, and consistency checking. It is also important to establish clear processes for reviewing and refining AI outputs before incorporating them into final designs — AI makes a lot of mistakes.

3. The Gap Between Current AI Capabilities and UX Needs

All three studies identify misalignments between what current AI tools offer and what UX practitioners actually need. Takafoli et al. find practitioners report limitations for design-focused activities, Stige et al. note an imbalance across design phases, and Lu et al. highlight the disconnect between AI's focus on individual screens versus the industry's emphasis on connected experiences.

What does this mean for UXers? We should advocate for tools that better align with our actual needs, such as supporting user flows across multiple screens rather than just optimising individual interfaces.

4. The Need for New Skills and Training

As AI tools evolve, UX practitioners need to develop new skills to use them effectively. Generating effective prompts and evaluating output quality requires training, which isn’t provided by most companies.

If you’re a UX leader invest in AI literacy for your teams, including training on prompt engineering, understanding AI capabilities and limitations, and critically evaluating AI-generated content. Create spaces for knowledge sharing where team members can exchange successful approaches. These approaches don’t require big budgets and are essential to ensure AI is used effectively.

Some Recommendations for UX Practitioners

So how can you integrate AI in your practice?

Start with clear objectives: Same as all good work (including research) you need to know why you’re doing this. Before adopting AI tools, clearly define what you want to achieve. Are you looking to speed up certain processes, explore more design alternatives, or enhance your understanding of user data? Different tools are appropriate for different goals. Using AI just to use AI is not recommended and it will lead to frustration and disappointment. Plus, it’s expensive and impacts the environment…

Choose the right tasks for AI support: AI isn’t suitable for all tasks. Think about the tasks and its requirements before deciding. In general, the reviews showed that AI is most valuable for:

• Analysing large volumes of user feedback and less nuanced research data (e.g. NPS qualitative data). Keep in mind that this doesn’t mean AI can do this alone – a human researcher still needs to examine the output and check the analysis.

• Generating alternative design options to consider

• Checking designs against established guidelines and patterns

AI is less suitable for:

• Building deep empathy with users

• Making strategic design decisions

• Creating coherent design systems

• Understanding contextual nuances

Maintain human oversight and interpretation: Always review AI outputs critically. Seriously, AI makes a lot of mistakes. Often, the subject expert is the best person to do this.

Develop team practices for AI use: Rather than leaving AI adoption to individuals, establish team practices. For example:

• Create shared repositories of effective prompts for different UX tasks

• Develop guidelines for reviewing and refining AI-generated content

• Establish regular forums/groups/meetings to discuss AI applications and learnings

• Share successes and failures to build collective knowledge

Advocate for better tools and data: The reviews revealed that current AI tools often don't address core UX needs. Help shape the future by:

• Providing feedback to tool developers about limitations and needs

• Participating in research and testing of new AI approaches

• Sharing case studies of effective AI use in UX practice

These three studies collectively provide a comprehensive view of AI's current and potential role in UX design. While AI offers significant opportunities to enhance UX practice, successful integration requires thoughtful approaches that maintain the human-centred essence of UX while leveraging AI's computational strengths.

The future of AI in UX lies not in automation for its own sake, but in thoughtful collaboration between human UXers and AI systems, each contributing their unique strengths to create better user experiences. By understanding both the capabilities and limitations of current AI tools, we can make informed decisions about how to incorporate these technologies into their work, enhancing rather than diminishing the human-centred nature of effective design.

How do you use AI in your work?

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A side-note: I’m open to consultations over topics I cover in this blog. Please reach out if you’re interested.