My design thinking process with the help of AII use **design thinking** as my core framework—empathize, define, ideate, prototype, test—and I treat **AI as a collaborator**, not the designer.
In the **empathize phase**, I talk to real users and then ask AI to help me synthesize notes: spotting patterns, clustering pain points, and drafting empathy statements. This speeds up analysis without replacing human insight.
In the **define phase**, I use AI to stress‑test my problem statement: “What am I missing?” “What assumptions am I making?” It helps me sharpen the question and frame it more clearly for the team.
During **ideation**, instead of waiting for inspiration, I prompt AI with constraints: “Give me 10 low‑tech solutions,” or “What would this look like in healthcare / education / gaming?” It acts like an extra brainstorming partner that never runs out of ideas.
For **prototyping**, depending on the medium (copy, UI flows, visuals), I use generative tools: text generators for microcopy and messaging flows; image or layout tools for quick mockups; code assistants for simple interactive prototypes. The goal is fast iteration so we can test earlier and often.
In the final **test and reflect stages**, AI helps me analyze feedback: summarizing user interviews or survey responses and highlighting recurring themes or contradictions. But the interpretation and decision‑making stay human; AI just surfaces what might otherwise be buried in data.
At every step my rule is simple: **think first with people in mind; then bring in AI to amplify that thinking**.
My design thinking process with the help of AII use **design thinking** as my core framework—empathize, define, ideate, prototype, test—and I treat **AI as a collaborator**, not the designer.
In the **empathize phase**, I talk to real users and then ask AI to help me synthesize notes: spotting patterns, clustering pain points, and drafting empathy statements. This speeds up analysis without replacing human insight.
In the **define phase**, I use AI to stress‑test my problem statement: “What am I missing?” “What assumptions am I making?” It helps me sharpen the question and frame it more clearly for the team.
During **ideation**, instead of waiting for inspiration, I prompt AI with constraints: “Give me 10 low‑tech solutions,” or “What would this look like in healthcare / education / gaming?” It acts like an extra brainstorming partner that never runs out of ideas.
For **prototyping**, depending on the medium (copy, UI flows, visuals), I use generative tools: text generators for microcopy and messaging flows; image or layout tools for quick mockups; code assistants for simple interactive prototypes. The goal is fast iteration so we can test earlier and often.
In the final **test and reflect stages**, AI helps me analyze feedback: summarizing user interviews or survey responses and highlighting recurring themes or contradictions. But the interpretation and decision‑making stay human; AI just surfaces what might otherwise be buried in data.
At every step my rule is simple: **think first with people in mind; then bring in AI to amplify that thinking**.