Where My Expertise Comes From
De Montfort University, 2011
I started with neural networks. Not as a business tool—as an art form. My final year project at De Montfort University was called "The 'Living' Electronic Performer" - an AI system that generated audio-visual performances using neural networks trained on my own compositions. This was 2011. My professors thought I was mad. The system thought in frequencies and colors. We performed together, the machine and I, creating something neither of us could produce alone.
That experience taught me something crucial: AI isn't about automation. It's about amplification. Real AI adoption happens when teams understand what they're amplifying—their own capability, their market position, the problems they solve for customers.
AI Mentorship at the Enterprise Level
For years, I watched organisations invest heavily in AI tools while their teams remained disconnected from how those tools actually create value. Through one of the UK's leading apprenticeship providers, I mentor enterprise clients across the UK—not executives who want slide decks, but the people actually responsible for making AI work inside their systems. I help them ask the right questions, identify realistic use cases, and build confidence in technology they don't fully understand yet.
The most common challenge I see? Teams know what AI can do in theory. They struggle to see what it can do for their specific business. They buy solutions before they've defined problems. That's the opposite of adoption.
Shaping How AI Skills Are Taught at Scale
As a subject matter expert at one of the UK's leading apprenticeship providers, I contribute to the national Level 4 AI & Automation Practitioner curriculum. This means helping shape how thousands of people across the UK learn to work with AI in professional environments. Not as a specialist discipline. As a core business skill.
When you teach at scale, you learn what actually matters. Technical depth without business context is useless. Business strategy without technical grounding is fantasy. Real AI adoption sits at the intersection—people who understand both, who can translate between technologists and the business, who know when a tool is the right solution and when it isn't.
From Theory to Systems
By the time ChatGPT arrived, I'd already spent years understanding the gap between AI capability and AI adoption. I'd seen teams at fintech companies (MAB, Circle, Poloniex) build fraud detection systems that were technically excellent but took months to deploy because people didn't trust the decisions. I'd watched education technology companies fail because they built sophistication nobody needed. I'd consulted with regulated professional services firms who wanted AI but didn't know if they were allowed to use it.
That's when I founded Twisthand. Not because I wanted another consulting agency. Because I saw a pattern: Organisations don't need more people talking about AI. They need someone who can build it, explain it, and teach their team to operate it independently.
Why Twisthand? The Nottingham Heritage
Our office sits in a former lace mill in Nottingham's historic Lace Market - where the Industrial Revolution transformed textile production. The revolutionary lace-making machines of the 1800s used simple lever controls to operate 17 tons of machinery with 40,000 moving parts.
The skilled operators were called "twisthands" - craftspeople who mastered these impossibly complex machines through years of apprenticeship. One twisthand with the right machine could produce what hundreds of hand-workers couldn't. The complexity was in the machinery, not in hiring more hands.
That's our entire philosophy. We don't add more consultants to your problem. We build you the right machine and show you exactly how to operate it. Simple controls. Complex results. You become the twisthand of your own transformation.
"From Nottingham's lace looms to modern AI systems - the principle endures: mastery over machinery beats manpower every time."
Our office in Nottingham's historic Lace Market - where innovation meets heritage
What I Believe About AI Adoption
I believe every regulated firm has AI opportunities waiting to be unlocked. Not "AI transformation journeys" that take 12 months and deliver PowerPoints. Real opportunities that create competitive advantage in your specific market.
I believe the gap between firms that adopt AI successfully and those that don't isn't capability. It's clarity. The teams that win are the ones who know exactly what problem they're solving, who understand both the technology and the business context, and who can explain it to their leadership without jargon.
I believe you should see a working prototype in days, not months. I believe your risk assessment should be clear before deployment, not discovered in the compliance review. I believe mentorship matters more than methodology. And I believe the best AI solution is the one that's adopted, documented, and actually running—not the theoretically perfect system nobody understands.
Most importantly, I believe building AI adoption capability is a craft. It requires technical depth, business judgment, and an obsession with making complex things simple enough that your team owns the outcome.
"I'm still that student teaching neural networks to make music. Only now, I'm helping regulated firms build AI that delivers real value."
This Is What I Do
I mentor teams through the UK's leading apprenticeship providers who are learning to think like AI builders. I shape national curriculum to ensure the next generation understands AI adoption, not just AI models. And I work directly with businesses — through Twisthand Intelligence — to move from "We need AI" to "Here's how we use AI to win in our market."
My role is the same in all three contexts: help people see what's actually possible, not what the marketing material promises.