Music was my first passion. I set out early to become an artist, songwriter and producer — natural science at school, with music school alongside it — and once school ended I started writing my own material and finding out who I was as a creator. Years followed of playing in bands, running my own, and building a studio where I wrote, recorded and produced for other artists. Leadership and creative direction came naturally, and running those projects — my own and other people's — turned into an early, practical education in entrepreneurship, networking and managing the people around me.
At some point the pull shifted. The daily grind of the music industry wore on the part of it I'd loved most, and increasingly it was science, philosophy and emerging technology that held my attention. I took an indefinite pause from music and went to university — a bachelor's in theoretical philosophy first, then management, computer science and entrepreneurship. Partway through my master's, I found what would become my second passion: machine learning. I remember the fascination immediately — the scale of what the technology could do for wealth, innovation and everyday life. I wrote my thesis on what applying it actually does to organisations and the people inside them, and came out the other side certain I wanted to spend my working life on it, in some form.
My professional path into AI started at the Swedish Prison and Probation Service, working alongside the Head of Innovation — an unusual entry point, and a useful one: a sensitive, high-stakes public institution, where getting things wrong has real consequences. I went on to become the organisation's advisor on artificial intelligence, working directly with the CIO to identify and evaluate where AI could responsibly be applied, and to shape a human-centred approach to it, covering ethics, trust and personal data.
From there I joined AI Sweden, the national centre for applied AI, as an AI Transformation Strategist — advising public organisations across Sweden on AI strategy, organisational readiness and capability.
Then ChatGPT was released, and the ground moved. Generative AI became, almost overnight, the technology that would sit at the centre of productivity in every domain for years to come, and I became convinced that Swedish public sector needed a national, sovereign capability for it — rather than each organisation working it out alone. I pitched the idea that became Svea: an innovation project that started with six organisations and a grant from Vinnova, Sweden's innovation agency. Three years on, it's grown into the largest collaborative AI project the Swedish public sector has run — 123 participating organisations and around €9 million raised in combined funding and contributions.
Building Svea has meant building a genuine programme, not just a product: a national AI application, education and upskilling for thousands of public-sector employees, and a distributed annotation effort involving hundreds of contributors. I now lead a multidisciplinary team of 20, spanning AI engineering, software development, product, law, change management, education and public-sector implementation — and the project has taught me as much about leading across disciplines, raising funding at national scale and building institutional trust as it has about the technology itself.
I'm based in Stockholm, and open to conversations about AI product and transformation leadership, in Sweden and internationally.