Human First.
AI that follows.
I spent over a decade running experimentation programmes, learning how people actually behave when you put something new in front of them. That's not an AI skill. Except it turns out it's the most important one. Organisations don't struggle with AI because the technology is hard. They struggle because they skip the human part, understanding how people will react, adopt, resist, and adapt. That's the gap I fill.
What experimentation teaches you
(that AI programmes desperately need)
Running experimentation programmes doesn't just teach you how to set up A/B tests. It builds the disciplines needed for human-centred AI strategies.
Speaking everyone's language. Experimentation sits at the intersection of engineering, design, analytics, and commercial strategy. So does AI. The skill is the same: translating between them.
Thinking in systems, not features. AI doesn't fail one tool at a time. It fails when the pieces don't connect data, decisions, and the humans in the centre.
Knowing what "working" actually looks like. Experimentation taught me to define success before building anything. Most AI initiatives can't tell the difference between a good demo and genuine value.
See it in practice
Chat GPSteve
My CV, reimagined as an interactive AI chatbot. A static document redesigned around how people actually want to explore someone's experience.
The AI Team
A coordinated system of 11 AI skills managing a website. Not one mega-prompt doing everything, a team of specialists, each with a defined role, working in sequence.
Where next
The full story
How a decade of experimentation led to AI, and what I learned along the way.
The thinking
Blog posts on AI strategy, systems thinking, and why the human part is always the hard part.
Start a conversation
Whether it's consulting, collaboration, or just a question, I'm always happy to talk.