How I Got Here: Sociology, Systems, and the Language of AI

Pattern Recognition

I didn’t set out to work in AI.

I studied Sociology and Economics as an undergrad—drawn to how systems shape behavior, and how culture and structure interact. Later, I completed a postgraduate degree in Development Economics, focused on impact and equity. Along the way, I met my husband, a computer science student. We were both still at university when we had our first son.

I helped him study for his exams, and to my surprise, I didn’t just follow along—I found it fascinating. The structure of logic. The architecture of information. It made sense to me.

I was later recruited into British Airways through their graduate scheme, after scoring well on psychometric tests—things like pattern recognition and spatial awareness. That’s where I first stepped into the world of relational databases. And once I did, I never really left. My entire career revolved around how data connects, what it reveals, how it flows.

What I’ve always known is that data is relational. A token on its own is noise. But in context, in sequence, in structure—it becomes meaning.

So when AI came along, it didn’t feel foreign. It felt like the next evolution of a language I had already been speaking. But this time, the conversation is bigger: it’s not just about what data says. It’s about how we adapt to what it means for us as humans.