I’ve always been interested in what happens when the old way stops working.
Whether the change is technological, organizational or deeply human, I’m drawn to the moment when what worked before is no longer enough — but the new way hasn’t been figured out yet.
That is where I do my best work.
Today, I help organizations navigate that space through AI adoption, work redesign, and transformation strategy — turning emerging capability into practical ways of working that people can understand, adopt and sustain.
My path has not been particularly linear, but I consider that an advantage.
I have been a digital marketer, writer, strategist, community builder, trainer, and transformation practitioner.
I have worked across different industries, lived in different countries and repeatedly found myself entering environments where I had to learn quickly, understand people and connect ideas that did not obviously belong together.
For a long time, those experiences looked like separate chapters, but I see them differently now. They taught me to look across boundaries and to:
To understand the commercial problem and the human one.
To move between executive language and employee reality.
To see the system without forgetting the person inside it.
And to remain comfortable when the answer is still forming.
That breadth of experiences has made me realize my greatest strength is in making sense of the “in-between.”
My career has always been about translation.
Long before I worked in AI, I was working at the intersection of people, ideas, and change.
I have spent more than two decades across marketing, publishing, digital strategy, customer experience, community building and organizational enablement.
The industries changed.
The technology changed.
The question underneath the work didn’t:
How do you take something complex and make it meaningful enough for people to act on?
Marketing taught me to understand behavior.
Writing taught me to make complexity clear.
Digital transformation taught me that introducing a new tool means very little if the surrounding work stays exactly the same.
And AI has brought all of those lessons together.
Because the hardest part of technological transformation is rarely the technology itself.
It is everything that has to change around it.
AI made the problem impossible to ignore.
When generative AI arrived inside organizations, I became fascinated by the gap between what the technology could theoretically do and what people were actually doing with it.
Access didn't create adoption.
Training didn't necessarily create behavior change.
Long lists of use cases didn't automatically create value.
And the people expected to change how they worked were often being asked to do so without anyone first understanding how their work actually happened.
That became the problem I wanted to solve.
My work moved increasingly toward AI enablement: discovering practical use cases, running pilots, building communities of practice, creating training and playbooks, developing governance approaches and translating experimentation into evidence senior leaders could actually use.
The deeper I went, the clearer something became…and it’s now where I focus.
AI Adoption is really a work-design problem
I see the structure inside messy change.
My strongest skill isn't knowing every answer.
It is being able to enter a complicated environment and relatively quickly understand:
what is actually happening,
where the real friction sits,
what everyone is treating as the problem that isn't actually the problem,
what needs to change first,
and how to turn that insight into something people can execute.
I’m at my best when the path isn't obvious yet.
When a new capability has arrived before the organization has figured out what to do with it.
When a pilot worked, but nobody knows how to scale it.
When employees have the tools, but behavior isn't changing.
When leaders have 20 possible opportunities and need to know which three actually matter.
Or when a transformation is technically moving forward but something about the way the work is designed simply isn't working.
I help make that complexity legible.
Then we build from there.
Beyond the work
I'm a writer by instinct, an endlessly curious learner, and someone who will probably always have several books open at once.
I'm interested in technology, psychology, behavior, identity, transformation and the strange ways humans respond when the world changes faster than our understanding of ourselves.
I live in London with my husband and our pug, Lulu.
And although my professional work now centers on AI and organizational transformation, I'm still fascinated by a much bigger question:
That question sits underneath far more of my work than AI alone.
And I suspect it always will.