Nielsen Norman Group

Dogfooding, or using your own products internally, helps catch bugs, but it can't replace user research: your team knows too much to represent real users.

Pressure to adopt AI isn't evidence that a tool helps. The PROVE framework tests one tool against one task and produces a provisional decision you can defend.

Take up to 2 in-depth training courses, teaching user experience best practices for successful design. Training focused on long-lasting skills for UX professionals. October 26 - October 29, 2026.

NN/G is hiring a People & Culture Generalist. This is a fully remote, permanent role running the day-to-day of our People function. Three to five years of People Operations or HR generalist experie...

Mentorship is an essential growth tool that offers substantial personal and career benefits, but only if you engage in it properly.

A survey of 604 tech and design professionals found that “UX” remains the default name for our field, while alternative names remain fragmented.

As more interface work is AI-generated, the output of research and design shifts from documents written for humans to curated context that guides AI.

Product sense means predicting which product decisions will succeed based on patterns learned through experimentation — and knowing when those patterns apply.

Dropdown lists can be used in narrow, specific scenarios, but misuse can cause more harm than alternatives.

Even if AI matches researcher-output quality, human-led research will remain essential — the team learning from observing users can't be outsourced.

Take up to 5 in-depth training courses, teaching user experience best practices for successful design. Training focused on long-lasting skills for UX professionals. October 5- October 16, 2026.

Handoff willingness, flexibility, proactivity, emotional responsiveness, and transparency help you build trustworthy AI chatbots that guide users well.

Use this maturity model to assess your design system’s health and identify where to focus next.

UX teams should report business outcomes — not activity or UX metrics — to show impact on revenue, cost, risk, speed, retention, and to secure resources.

Different enterprise roles need different types of explanations for AI outputs.

Learn to spot and filter out survey bots’ responses before analysis so fake data doesn’t distort your findings.

Gather baseline metrics before starting a project so your team can demonstrate its impact.

A mindful incentive structure can keep diary study participants engaged and responding, without overloading you with low-quality responses.

Nondevelopers are building complex agentic AI systems on intuition developed through many hours of experimentation, YouTube videos, and Reddit threads.

To build useful and usable AI-powered systems, our understanding of users’ needs and our design judgement must be encoded into well-defined evaluation criteria.