Simon Rosenqvist

Design leadership across product, customer experience, and brand.

I lead design teams and build the tools they work with — grounded in cognitive science, proven in automotive, fintech, and music. This is where I write about the work.

Black-and-white photo of a wooden artist mannequin lying on its side

Accident Models

A tour of accident models—from blame-focused ‘bad worker’ thinking to Safety-II and resilience engineering—with practical implications for designing safer automotive interfaces.

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Building a Color Contrast Design Jig

How I created a visual tool to explore color relationships and solve contrast requirements in automotive interface design

Jigs, Not Tools

Building personal utilities instead of general tools - why jigs matter for designers

11ty RSS Feed Setup: Three Plugin Problems and How I Fixed Them

Dependency management, configuration conflicts, and deprecated APIs while setting up RSS feeds in 11ty

UX Design's Role in the Automotive Sales Lifecycle

Understanding how UX design impacts every stage of the customer journey, from initial interest to long-term advocacy in the automotive industry.

Moving the Blog to 11ty

Moved the blog away from pure HTML and migrated to 11ty

AI hardware products

Why should companies invest in AI hardware products for software solutions? Both Humane's AI pin and Rabbits R1 thought so despite The high costs, complexities, and distribution challenges.

How has the role of the UX designer changed?

How have the role of the UX designer evloved over the years?

The Evolution of UX Design

The evolution of UX design roles and responsibilities

How to Become a Design Engineer as a Designer

Moving from design to design engineering is challenging but achievable. Here's the technical and organizational hurdles to look out for.

How to adapt to AI as a Designer?

If we feel urgency about AI what should we do?

Command line and prompt interfaces

A blogpost about design patterns for ai and the new types of interactions that are needed and enjoyed

The Blunt and Sharp end of AI

What is the blunt and sharp end of organisations and how does it affect the design of AI tools?