AI in UX: Promise of Efficiency or Risk to Quality?

AI Raises Growing Concerns

Artificial intelligence (AI) is at the heart of current debates, and a 2025 Pew Research study reveals that 50% of Americans express more concern than enthusiasm about its increasing use in daily life. This worry is shared by both experts and non-experts, who express a common desire to exert more control over this technology. However, these figures would likely vary if we focused solely on workers in the tech sector.

I consider myself a skeptic of AI. While this may seem paradoxical given my role as a UX research leader, I am not alone in this position. My skepticism does not mean I oppose all AI tools. Some can be extremely useful when used in the right context and with the right limitations. However, I advocate for a measured and thoughtful approach to their use.

AI in the UX Sector: Between Promise and Reality

In the field of user experience (UX), AI is often seen as a revolution capable of transforming design and research processes. It promises increased efficiency, allowing product managers to quickly create mockups and designers to move directly from prototype to code. Yet, the idea that AI can replace the quality of research is misleading. AI-generated prototypes, while functional, often require revisions by engineers, and the insights produced can sometimes lead companies in the wrong direction. Thus, AI does not so much change the design process as it adds additional revision steps.

A Measured Approach to AI in UX

A UX research leader, with ten years of experience in the tech sector, has chosen to adopt a thoughtful approach to integrating AI into their team of eight researchers, including a manager and UX operations. While the adoption of AI has been slower than in other companies, the team compensates for this lag with marked enthusiasm. Teams, including those in UX, are encouraged to proactively identify opportunities to integrate AI into their workflows to enhance efficiency. Management, at all levels, is committed to this initiative, although discussions about risks are rare.

The leader has been encouraged to incorporate AI into all workflows of their teams, often without a thorough consideration of the risks and trade-offs. This pressure to adopt AI quickly is common in the tech sector, but it must be balanced with critical reflection on long-term implications.

Guidelines for Reasoned Use of AI

To guide their team, the leader has defined a "north star": AI should support, not replace, the quality of research. Key skills have been identified, and guidelines have been established to preserve these skills. For example, AI should not be used to develop research questions, but it can be used to clean survey data or prepare data for analysis. Sections of research documents generated by AI must be clearly labeled.

The leader emphasizes the importance of cultivating and developing essential professional skills, such as good qualitative interviewing techniques, rigorous data analysis, and persuasive storytelling. They believe it is their responsibility to maintain an environment where researchers, at all stages of their careers, can learn and practice these skills.

Evaluating AI Tools

The research leader approaches the implementation of AI with a "risk versus reward" mindset, asking questions about the effectiveness, time savings, and cost-effectiveness of the tools used. Often, the answers highlight that AI does not always meet expectations, but these discussions help refocus the debate on the added value for the company. The leader regularly documents what works and what does not for each tool and tracks their usage.

The Importance of Critical Reflection

While not opposed to new technologies, this leader underscores the importance of critical reflection in adopting AI. They fear that AI may harm our ability to think for ourselves and that, in our quest for speed, we may lose the necessary time to produce quality results. Ultimately, the goal remains to support the team in their work, and AI must be integrated in a way that does not compromise this objective. The leader insists that the adoption of new technologies must be accompanied by reflection on the risks involved, so as not to hinder the ability to think independently and produce quality outcomes.