There’s a growing pressure in the design industry to treat AI as a shortcut to solutions—wireframes generated instantly, UX problems “solved” in a prompt, or research synthesized without context. What I’ve learned instead is that AI is most powerful when it’s used as a productivity partner, not a replacement for design thinking. The value isn’t in skipping steps, but in moving through them more intentionally.
Discovery & Framing
At the beginning of a project, AI can help designers accelerate understanding without dictating direction. I’ve found it useful for synthesizing early research notes, identifying patterns across stakeholder interviews, or stress-testing assumptions by asking “what might we be missing?” These outputs don’t replace discovery—they sharpen it. The designer still owns interpretation, prioritization, and framing; AI simply helps surface signals faster.
Defining the Problem
As projects move from exploration to definition, AI becomes a useful thought partner for clarifying problem statements and constraints. It can help reframe goals in user-centered language, generate alternative “How Might We” questions, or pressure-test whether a proposed problem statement actually aligns with business and technical realities. Used this way, AI supports clearer articulation—not decisions.
Ideation & Concept Development
During ideation, AI is best treated as a divergence tool rather than a source of final concepts. It can help generate multiple approaches, highlight edge cases, or explore variations that might not surface in a single design pass. The key is restraint. The designer evaluates what’s viable, meaningful, and aligned—not what’s simply possible.
Design Execution
Once the direction is set, AI can meaningfully reduce execution friction. From drafting UX copy variations to generating placeholder content, summarizing feedback, or helping structure documentation, AI allows designers to spend less time on repetitive tasks and more time on decision-making, collaboration, and refinement. This is where productivity gains are most tangible.
Validation & Iteration
Post-design, AI can assist in organizing usability feedback, categorizing themes, and identifying follow-up questions for testing. It can also help anticipate stakeholder questions and prepare clearer narratives for design reviews. The insights still come from users—the efficiency comes from how quickly designers can respond to them.
Launch & Beyond
After launch, AI becomes a tool for reflection and continuity. It can support post-launch analysis, help document lessons learned, and even assist in preparing future iterations or handoffs. Used thoughtfully, it reinforces design as an ongoing practice rather than a one-time deliverable.
The Designer's Role Doesn't Change—It Becomes More Visible
What’s important to remember is that AI doesn’t remove the need for judgment, empathy, or accountability. In many ways, it makes those skills more visible. When productivity increases, the quality of thinking becomes the differentiator. Designers who use AI well aren’t designing less—they’re designing with more clarity, focus, and intention.
For me, the goal isn’t to move faster at the expense of quality. It’s to remove unnecessary friction so I can spend more time doing the work that actually requires a designer: making sense of complexity, aligning people, and shaping thoughtful outcomes.