Field notes
Whostill curious.
Architect, operator, governance practitioner
I don’t make AI sound magical. I make it operational.
Architect by training. Global AI and Data Governance Lead at dsm-firmenich. Still curious about what systems mean for people.
01 · The throughline
More than twenty years. One recurring question.
How do we make complex systems work for people, and keep responsibility human?
For more than twenty years I have worked across solution architecture, global customer experience, technology operations, data quality, and governance. Different roles. One pattern: connect the technical system to the people who depend on it.
The work has taken me from Barcelona to Tokyo, from Lima to New York. Across markets and disciplines I keep returning to the same things: clear meaning, accountable systems, and trust earned in practice.
Barcelona · Tokyo · Lima · New York
Networks. Architecture. AI. Governance.
Networks and people
I started in technical data analysis and incident operations for enterprise networks, working between customers and engineers across Europe and Asia, where reliability had to hold under pressure.
Architecture
A different kind of systems thinking: how parts fit, how people move through spaces, and how a plan meets real constraints.
Trust and AI quality
Trust and Safety, process optimization, and human-in-the-loop AI across multilingual teams. The work made feedback matter: a quality initiative raised KPIs by 15 points.
Governance
Today, as D&T Global Data Governance Lead at dsm-firmenich, I help turn policies into daily practice: clear ownership, trusted data, accountable AI.
02 · Things I keep returning to
Some beliefs are less like answers than useful instruments.
- 01 · Enable
Good governance should enable, not merely mandate.
- 02 · Meaning
People negotiate meaning. Systems should make that meaning usable.
- 03 · Accountability
AI can assist a decision. It cannot take responsibility for its consequences.
03 · Outside the title
People are the point. Curiosity is the method.
I have spent years close to the operational reality of technology: the customer waiting for a fix, the team interpreting a policy, the person expected to trust an AI system. That is why the work has to make sense to people, not only on paper.
I contribute to DAMA España’s AI and Data Governance Working Group, write about the questions I meet in practice, and keep learning by building. Most recently, exploring agentic AI from the inside.
“Data is our greatest asset.” It’s a good line. It’s also wrong. What’s actually greatest: people.
A little more context