How UX teams are evolving with AI: insights, biases, and best practices

Dan Damsa

From automating repetitive tasks to providing deep insights through data analysis, AI-powered tools are reshaping how UX researchers and designers work, making many processes faster, more efficient, and often more insightful.

Yet, as with any powerful technology, integrating AI into UX requires a delicate balance between automation and human judgment for meaningful, user-centered outcomes.

AI Agents as autonomous helpers in UX workflows

A particularly exciting development is the emergence of AI agents (or autonomous systems) capable of performing complex tasks with minimal human intervention.

Unlike traditional AI tools that respond to direct commands, AI agents can plan, reason (within reason), and adapt over time, orchestrating workflows that involve multiple subtasks and external data sources. However, as IBM’s research highlights, these agents require careful goal-setting, tool integration, and human oversight to avoid pitfalls like infinite feedback loops or data privacy risks. The human-in-the-loop remains essential to guide AI agents, validate outputs, and ensure ethical standards.

Beyond automation

AI’s role in UX goes beyond simple automation of mundane tasks. While automating routine activities like data collection, transcription, or basic analysis frees up valuable time, AI also offers advanced capabilities such as pattern recognition, sentiment analysis, and predictive modeling. These empower UX researchers and designers to uncover hidden user needs, anticipate behaviors, and tailor experiences more precisely.

For example, AI-driven analytics platforms can comb through thousands of user interactions to identify pain points or feature requests that might otherwise go unnoticed. Similarly, natural language processing (NLP) enables automated coding of qualitative data, accelerating thematic analysis without sacrificing depth.

The key to effective AI support is trust

One of the most fascinating aspects of AI in UX is how users perceive and interact with these tools. Our findings also confirm that people tend to humanize AI assistants like ChatGPT, engaging in conversational interactions that build trust over time. This trust is closely linked to the AI’s communication style. Polite, clear, and personalized responses foster a sense of dialogue rather than a transactional query.

For UX professionals, this insight underscores the importance of designing AI tools that communicate transparently and empathetically. When AI support feels like a collaborative partner rather than a cold machine, users are more likely to embrace its recommendations and integrate its outputs into their workflows.

However, this must be done with caution. Ethics and transparency must always come first to avoid AI psychosis and a perception of AI assistants that feel more uncanny than personable.

Screens from Inquisita's homepage, designed by UX studio — We helped Inquisita build trust in AI for high-stakes legal work, all through design and research

Navigating automation bias and maintaining critical judgment

While AI offers tremendous benefits, it also introduces risks such as automation bias: the tendency to over-rely on automated suggestions and overlook contradictory information. This bias can lead to errors of commission (blindly following AI recommendations) or omission (failing to detect AI errors), which are well-documented in high-stakes fields like aviation and healthcare.

In UX, automation bias might manifest as unquestioning acceptance of AI-generated user insights or design suggestions without sufficient critical review. To mitigate this, UX teams should foster a culture of healthy skepticism, combining AI outputs with human expertise and contextual understanding.

Training and system design can also help reduce bias. For instance, providing transparency about AI confidence levels, enabling easy access to raw data, and encouraging users to verify AI findings all promote balanced decision-making.

Prompting challenges

Effective use of AI in UX often hinges on the quality of prompts and inputs provided to AI tools. As highlighted in our recent UX research, crafting clear, detailed prompts significantly improves AI output quality. However, many users face an “articulation barrier”, difficulty expressing their intentions in a way the AI can interpret correctly.

AI-powered writing assistants and research tools are evolving to bridge this gap by guiding users through step-by-step input collection, offering flexible outlines, and providing multiple output options. It’s an iterative process, these tools that teach users how to prompt better, creating a cycle of improved AI-human collaboration, are our current best friends to hone our prompting skills.

Best practices for integrating AI support in UX

To harness AI’s full potential while avoiding common pitfalls, UX teams should consider the following best practices:

Define clear goals and boundaries: Set explicit objectives for AI tools and agents, including what tasks they should automate and where human judgment is indispensable.
Maintain human oversight: Keep humans in the loop, especially for high-impact decisions, to validate AI outputs and provide ethical guidance.
Design for transparency: Ensure AI tools communicate their reasoning, confidence, and data sources clearly to build trust and enable informed use.
Train teams on AI literacy: Educate UX professionals about AI capabilities, limitations, and biases to foster effective collaboration.
Iterate and refine: Continuously test AI integrations with real users, gathering feedback to improve usability and reliability.

Sara Hannikar holding a presentation about AI workflows to UX studio designers and researchers in their office — UX studio team members learning about AI workflows in fintech with the help of guest presenter Sara Hanniker

Looking ahead

AI support and automation are transforming UX research and design from a labor-intensive craft into a more scalable, data-driven discipline. Yet, the human element remains irreplaceable. AI excels at processing vast amounts of information and automating routine tasks, but empathy, creativity, and ethical reasoning are uniquely human strengths.

By thoughtfully integrating AI tools and agents into UX workflows, professionals can amplify their impact, focus on strategic challenges, and ultimately create more meaningful, user-centered experiences. The future of UX is not AI versus humans, it’s AI and humans working together in harmony.

Want to explore how AI can elevate your UX practice? Get in touch with our experts to discuss tailored AI UX research, strategy, and design solutions that fit your unique needs.

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This blog post was written by Dan Damsa, UX researcher.
Fact-checking by Laura Sima, UX researcher.
Editing by Dr. Johanna Székelyhidi, marketing manager.