Emerging principles for using AI in Design: What the product design team at Miro has learned from deeply integrating AI in their workflow

Key Insights

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  AI is shifting design research from primarily primary user interviews to more synthesis of secondary research using tools like AI-generated mind maps.

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  Miro users employ AI to cluster large amounts of data, like survey responses, to speed up thematic analysis.

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  AI-assisted prototyping tools can transform sketches or screenshots into interactive, shareable clickable prototypes within minutes.

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  Designers can now bring code-like interactivity (e.g., CSS, React code) into early prototypes without directly coding themselves.

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  Prototyping with AI helps designers collaborate more closely and earlier with engineers, improving design handoff and alignment.

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  Non-designers and product managers are empowered by AI tools to create and edit visual mockups, reducing dependency on designers.

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  Visual communication enhanced by AI fosters better cross-functional alignment and makes complex concepts accessible to diverse teams.

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  AI tools still require precise prompts and patience due to limitations like AI forgetting previous context (goldfish syndrome).

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  Current AI prototyping workflows often produce disconnected artifacts not fully collaborative yet, showing room for integration improvement.

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  Miro is actively experimenting with embedding AI features throughout the product development lifecycle, including AI prototyping and collaborative editing.

Notable Quotes