My career has taken me from writing code to designing products to leading strategy at the highest levels of business. Each shift changed how I think - and the combination is what I bring to every conversation about AI.

I started my professional life as a software developer after graduating with a Computer Science degree. Then made the unusual decision to walk away from engineering and pursue product design - a transition that confused almost everyone in the 2000s. Spent a decade building digital products, leading teams, and learning that the gap between technology that works and technology people actually want to use is almost always a human problem, not a technical one.

That conviction led me to McKinsey & Company, where I led the European design practice. Over eight years, I rose to Partner.

My work with AI started at McKinsey in 2018. I was part of teams of data scientists, engineers, and designers building machine learning models for customer behaviour prediction, and went on to lead a series of AI implementation projects for large companies - spanning financial services, consumer brands, and some of McKinsey’s earliest generative AI client work. That hands-on experience building AI systems that had to actually work in the real world is what shaped everything I do now.

Along the way, I kept coming back to the same lesson: the organisations that succeed with AI are the ones that start with how people actually make decisions, not with what the technology can theoretically do.

Today, I work independently - advising, building, teaching, and writing about the practical realities of making AI work within human systems. I advise companies and leaders on AI strategy and human-centred AI implementation. I’m lead lecturer and course creator for iF Academy’s AI strategy programme for design leaders, a lecturer on D&AD’s AI course, and faculty at the Marketing Academy. I co-authored The AI Revolution, a book that reached #1 on Amazon’s global AI charts. And I’ve developed practical tools - like the Four Modes and the practical Human in the Loop framework - now used by thousands of practitioners worldwide.

But the tools are expressions of something deeper: a way of thinking about technology that centres human judgment, respects cultural complexity, and insists on practical clarity over impressive abstraction. That’s what I bring to advisory relationships. And it’s what drives the question I keep returning to: how do we build technology that genuinely serves people?

When I’m not advising, I’m building - small tools, experiments, and things that help people. It’s the part of my work that keeps me honest about what technology actually feels like from the inside, and keeps my hands in the work.

What I believe

  • Every failed AI initiative I've studied started with the wrong question.

    They asked "what can this technology do?" instead of "what decision is this person actually trying to make?" Starting with the human context changes everything - the architecture, the interface, the governance, the adoption.

  • The most important AI skill isn't technical.

    It's the ability to see your own thinking clearly enough to know where a machine can genuinely help. That's why the Four Modes resonates with so many people - it's not really about AI. It's about understanding how you work.

  • Cultural context isn't a nice-to-have in AI deployment.

    An AI system trained predominantly on English-language data will embed assumptions about communication, trust, and respect that fail silently in other cultures. I've seen this derail global rollouts that were technically flawless.

  • Ethics is an engineering problem, not a compliance exercise.

    Responsible AI requires building trust calibration systems, testing for cultural intelligence, and designing governance that enables progress rather than creating bureaucratic theatre.

  • Complexity is easy. Clarity is hard. And worth it.

    The most impactful work I've done has always involved making something simpler, not more sophisticated. The best idea is the one people actually use on Monday morning.

  • The goal isn't AI adoption. The goal is better decisions.

    Technology is the enabler, not the destination. The organisations that understand this will build AI systems people trust. The rest will build expensive tools that sit unused.

Selected client work

Human + AI: reimagining financial guidance together

Led the creation of a hybrid advisory platform that enhanced human financial advisors with AI-powered insights. By developing both customer-facing AI guidance and advisor tools, we created a seamless ecosystem where technology amplified human expertise rather than replacing it. Advisors gained deeper customer understanding while clients received personalised guidance that evolved with their life needs.

Result: 40% uplift in sales across multiple product lines. 6-month intensive build, interim Chief Product Officer.

Banking on the unbanked: creating 100,000 first-time investors

Spearheaded an “attacker” wealth management platform that disrupted Central Europe’s savings-dominated market. By transforming complex investing into an intuitive, gamified experience, we turned financial novices into confident investors - in a market where fewer than 5% of adults had ever invested.

Result: Outperformed year-one targets by 2.5x. Founding Head of Design, 14 months.

Feeding 10 million: the digital delivery revolution

Led a comprehensive digital experience transformation for a global food chain, scaling across 16 countries. Established design systems, data-driven decision frameworks, and cross-regional coordination that defended market share while opening new revenue streams.

Result: Best-in-class platform serving 10+ million customers. Founding Head of Customer Experience, 14 months.

The trust exchange: turning data privacy into brand advantage

Led a transformative initiative with a multinational cosmetics conglomerate that redefined how their portfolio of iconic brands approaches consumer data. Designed an experience that made the data value exchange transparent and intuitive - allowing consumers to see precisely what was collected, understand how it benefited them, and control preferences seamlessly.

Result: Substantially improved consumer trust metrics and positioned prestige brands as leaders in consumer-centric data practices before regulatory changes made it mandatory. 3-month global initiative.

Pioneering generative AI for global brands

Core to building and scaling McKinsey’s groundbreaking generative AI client work, creating solutions that transformed how global clients approach personalisation. From developing Formula E’s revolutionary fan experiences to establishing new frameworks for AI-driven customer engagement.

Result: 18 months of ongoing innovation redefining what’s possible at the intersection of data science and human behaviour.