A designer who immigrated to the US found a fitting role as a UX designer by relying on data-driven methods. That approach delivered clear, measurable improvements and helped integrate design into product teams. Over time, industry focus shifted toward process, templates, and metrics, making many design tasks predictable and repeatable. AI and lower-cost labor can now reproduce structured, data-backed iterations, threatening those roles. Data-driven optimization often flattens user experiences and reacts to past signals, leaving little space for invention or preventive work. Clinging to process risks making design work increasingly replaceable in the job market.
When I first came to the US a decade ago, I wasn't sure how I'd fit into the job market. I wasn't from here and didn't know the playbook. Through trial and error, I eventually found myself in the then-booming role of UX designer - a job that felt relatable, in demand, and easy to explain to others at the time. Like many in the field, I leaned heavily into the mantra of "data-driven" design. Every choice had to be backed by numbers, validated by user tests, or confirmed by analytics. Every choice had to be backed by numbers, validated by user tests.
Data-driven design is easily replicable, especially with AI. It's a great tool for an operator, but that has risked some design jobs. It flattens experiences. Optimizing for numbers alone converges toward sameness: endless scroll feeds, grid layouts, the same funnels. It's reactive. Most available data reflects only the past. Leading indicators are often hard to identify or measure. As a result, we tend to focus on lagging data, making iterations reactive rather than inventive or preventive.
Collection
[
|
...
]