Why Algorithmic BrAIn exists
I didn’t set up Algorithmic BrAIn to add one more name to the list of AI companies. I set it up because I kept meeting the same problem.
Across central banking, telecoms, manufacturing, public utilities and consulting, organisations were investing in AI, automation and new platforms while the fundamentals stayed unresolved: data scattered across systems that didn’t talk to each other, processes still run by hand, and capable people spending their days on work a machine should do.
I also saw AI initiatives that shone in a pilot or a slide deck, then quietly never became something the business relied on.
So we don’t start by asking where AI can be used. We ask a harder question:
what should work differently here, and what will it take to make that last?
We begin with how an organisation actually works: its processes, data, systems, people, and the regulation it answers to. Only then do we decide what to build.
Sometimes that’s an agentic system running on a private language model. Sometimes it’s fixing the data foundation first. Sometimes it’s automating a workflow that still lives in spreadsheets and inboxes. And sometimes it’s making sure what you already run can stand up to the EU AI Act.
My own grounding is in economics, information systems and data architecture, and in years as an external expert for the European Commission. That shaped how I see technology: AI is only as useful as the data, processes and controls behind it, and the outcome it produces for the business.
I’m particularly keen to talk with:
