NN/G: AI for design workflows

Nielsen Norman Group runs this live online course for designers who want to add AI tools to their existing workflows without losing the judgment that makes design work. The course is taught by NN/G researchers and instructors who work in evidence-based UX — Megan Chan, Rachel Krause, and Huei-Hsin Wang — which keeps the focus on what AI actually does well in design contexts rather than on vendor claims.

Sessions run via Zoom in either a two-day format (morning or evening) or as a single full day. Upcoming dates include July 14–15, July 21, and August 19–20, with September dates available for teams. Pricing is $1,200–1,290 USD, with a volume discount for September groups.

Who it is for

Practicing UX and product designers who want to work faster on the labor-intensive parts of their process — concept generation, wireframing, copywriting, image assets — while keeping editorial control. The course is most useful for designers with project experience who have been putting off learning AI tools because nothing available felt directly applicable to design work. It is less relevant for designers who have already built a working AI workflow, or for those focused on engineering handoff and design systems.

What it covers

The course runs through the full design process. Topics include AI capabilities and their limits in design contexts, prompt engineering techniques for design tasks, ethical guidelines for AI use, decision frameworks for when AI adds value versus when it introduces risk, and applied practice across research, ideation, concept development, design critique, QA, microcopy, image generation, moodboards, wireframing, and prototyping.

A section on how AI is changing design roles and team structures covers the organizational context that most tool-focused training ignores.

What it does not cover

This course does not address how to design AI-powered products or systems — NN/G offers a separate course, “Designing AI Experiences,” for that. It also does not cover agentic workflows, model fine-tuning, or the engineering side of building AI features. Designers whose primary responsibility is building AI-native products will need to look beyond this course.