The panel suggests a best practice for AI prototyping tools: focus on pinpointed interactions or small, specific user flows. Once a prototype grows to encompass the entire product, it's more efficient to move directly into the codebase, as you're past the point of exploration.
Nick Pattison's firm creates generative tools for clients, enabling them to produce on-brand assets like geometric patterns themselves. This innovative handoff empowers clients to scale their brand system instantly and playfully, moving beyond static guidelines.
Vercel's Pranati Perry argues that even with no-code AI tools, having some coding knowledge is a superpower. It provides the vocabulary to guide the LLM, give constructive criticism during debugging, and avoid building on a 'house of cards,' leading to better, more stable results.
At Perplexity, the brand team's strategy is to use AI tools for 'velocity and volume,' according to VP of Design Henry Modiset. This allows a small team to produce a massive amount of creative assets, making the brand feel ubiquitous and loud in the market without a huge budget.
Vercel's Pranati Perry shows how she used V0 to build a personal tool for generating SVG components for her portfolio. This highlights a trend where designers build small, single-purpose tools to automate and enhance their own creative processes, not just for team deliverables.
Perplexity's VP of Design, Henry Modiset, states that when hiring, he values product intuition above all else. AI can generate options, but the essential, irreplaceable skill for designers is the ability to choose what to build, how it fits the market, and why users will care.
Vercel's Pranati Perry explains that tools like V0 occupy a new space between static design (Figma) and development. They enable designers and PMs to create interactive prototypes that better communicate intent, supplement PRDs, and explore dynamic states without requiring full engineering resources.
Vercel designer Pranati Perry advises viewing AI models as interns. This mindset shifts the focus from blindly accepting output to actively guiding the AI and reviewing its work. This collaborative approach helps designers build deeper technical understanding rather than just shipping code they don't comprehend.