Product design in the age of AI
Jake Knapp and I hosted a webinar about product design in the age of AI.
A cool 1500 people signed up.
We didn't talk about how to vibe design or vibe code (there are already lots of how-tos available for that).
We didn't talk about the most hyped tools you have to be using right now (we're not cool enough to be up-to-the-minute).
Instead, we opened the book on our “missing manual” for building products that cut through the noise and actually solve real problems for customers.
We pulled up an empty Miro board and started building out a framework of context, big ideas, and advice.
We shared our unique approach to product design, based on lessons from working hand-in-hand with 300+ teams.
I was gonna write up the big lessons we shared at the event, but Mark Hubbard did it for me. Pasting those below with a few edits and much gratitude to Mark!
BIG IDEAS
Differentiation is Crucial: Successful products solve problems in a radically different way from existing solutions.
AI Output is Generic by Nature: AI-generated outputs tend to be generic because they are based on common data. Differentiation requires human insight and creativity.
AI Stresses Everyone: The rapid pace of AI development can be overwhelming, but it's important to focus on quality and differentiation.
Quality Matters: Obsessing over quality and details is important in an era when more and more teams are putting out slapdash work.
Context is Key: If you want to produce differentiated output with AI, you need to provide differentiated context (from customer data, personal insight, etc). In the words of Tyler Cohen, "Context is that which is scarce."
AI Can Reproduce Design, But Not the Process of Design, with credit to M.G. Siegler
ADVICE
Don't Outsource Your Thinking: Use AI for grunt work but maintain your unique insights and thinking.
Start on Paper: Begin with sketches to capture your intuition before using AI tools. If it's slow and hard, that means it's working.
Ask Questions Before Choosing Tools: What needs to be true? > What experiment do I need to run? > What prototype do I need? > What's the best tool(s) for ME (not someone else) to use to build that prototype?
Slow Down to Save Time: Taking time to think critically can lead to higher quality outcomes. Thinking is slow, but it's how you tap into unique insights and differentiation output.
Use AI as a Collaborator: AI should be used to speed up processes and explore ideas, not to replace human creativity.
Still unpacking the insights and lessons learned from the first session. It's time for round 2. Any way we can get access to the Miro or slides to dive into how the teams built and monitored tests and decision points?
Love this!