Any time I talk to someone in the industry these days, they ask how I'm integrating AI into my day-to-day design work. We’re working in a time when AI apps can produce movie-like video footage that usually takes a crew of people to produce or 3D animations that require a team of animators to all work in unison. I've even experimented with it build complex apps and month over month, it's advancement and improved quality has been nothing short of astonishing. AI has become a force multiplier for me in the last two years across my end-to-end product design workflows and creative/brand design work in a number of ways.
Research: Industry & Competitor Analysis
When you work within fast timelines, small startups or lean budgets, research is a luxury. AI’s ability to gather and synthesize large swaths of information from hundreds of sources and then break it down into a clean summary allows me to conduct my own research for design purposes to better understand an industry and it's competitors. With AI I’m able to quickly key in on insights that aid my thought process and understanding of a space. It won’t replace what an actual researcher can conduct, it’s a great second option for teams that need a boost of info or reassurance.
UX & Dev Specs Documentation
Any engineer that’s worked with me knows that I don’t consider a design complete until specifications, behavior and interactions have been drafted into notes within my Figma files. Part of my job is to create a source of truth for all things need to build a feature and with that responsibility I need to make sure that my designs are hand off ready with edge cases outlined, designed and documented, in addition to factoring in all of the screens, states and requirements. Integrating Claude has allowed me to pass on that task, so that it can draft up those specifications for me and in some cases it may even recommend something that I hadn’t factored in, so from that standpoint it overdelivers on this task.
Idea Generation & Prototyping
Leveraging Claude Code in the early stage of design to explore concepts and ideas for new features that I can then refine and improve within my FIgma designs has become a valuable way for me to jump start my problem solving phase. In some cases it works like a thought partner to expand beyond my initial thinking with different concepts or ways of thinking about the problem. Being able to use Claude to create initial prototypes with my ideas and recommendations that I can test drive with teammates earlier in my ideation phase allows us to align our collective thinking and alignment on the approach. Being able to have these discussions without the need to spend hours designing and preparing notes is a game changer to build consensus on a feature in a lean fashion that everyone can rally around. I also advocate for Product Managers and Engineers to run the same experiments with me, if they’d like to collaborate in the ideation phase, because design is a team sport and it should not be exclusive to a single person.
Continued Future Advancement of How We Design & Build Apps
We can expect that most of our current design software will get updated to an AI chat setup that will augment or replace how we execute design tasks. Designers will be able to type in prompts and let AI create and build a set of screens with the designer making modifications as needed. No longer will we have to design every pixel, asset or artifact from scratch. Apps like Figma are racing to figure out how to harness AI, so that it doesn't get left behind. Every other week they are launching new AI features to keep up with the likes of Claude Design.
In terms of how designers responsibilities and roles will adapt, we expect that our time will transition to defining problems, establishing the vision, cultivating the strategy and collaborating across a company to own the outcomes. No more juggling all of these responsibilities and then having to work into the evening on designs for a new app feature set. The day-to-day will require more communication and a heavier emphasis on user research, data analysis and testing prototypes with users to more quickly and effectively launch features.