What UX Consulting Clients Expect in the Age of AI

Why Read This from an HCI Perspective

This article clearly lays out what the core value of UX consulting still is in the age of AI. For HCI practitioners and researchers, it’s a useful reminder that the key competency is not using AI itself, but making judgments grounded in user evidence and adjusting design within real-world constraints. In particular, it connects the need to protect the rigor of interpretation and the quality of experience to practical work—not just the idea of quickly ‘producing’ research results.

CIT's Commentary

From a CIT perspective, the article is meaningful in that it makes UX’s responsibility boundaries even clearer rather than suggesting that AI will replace UX. LLMs can generate drafts quickly, but it is still the HCI researcher’s job to read and integrate the organization’s decision-making structure, user context, and risk level. The passage that emphasizes that ‘good UX’ is not about visual polish, but about the quality of judgment, the interpretation of evidence, and design that reflects constraints, is especially important. Going forward, however, the key need will be operational principles for when to strengthen human judgment and when to allow automation—not whether to exclude AI altogether.

Questions to Consider While Reading

• Q.When integrating AI into a research workflow, which steps should be automated, and which steps must always leave room for human review?
• Q.In situations where organizational constraints are strong, how can you design prioritization criteria to protect the quality of the user experience?
• Q.What reporting approach can make stakeholders trust the researcher’s interpretation more than the AI-generated outputs?