About
Which applications of artificial intelligence survive contact with a regulated institution — a legacy data estate, an audit trail, and a delivery plan that has to hold?
That question is the reason demystifAI exists. The field does not suffer from a shortage of enthusiasm. It suffers from a shortage of people willing to say plainly which claims hold up once they meet a control environment and a date in a plan.
What you will find here
demystifAI is written for the people who carry the consequences of the decision: programme directors, product owners, heads of data, and the executives who sponsor the work. Every argument is grounded in two things:
- Data. Claims are tested against evidence rather than repeated because they are popular.
- Delivery. An idea that cannot be built, governed, and operated is not a strategy. It is a slide.
What follows from that is a narrower and more useful question than "what can AI do?" — namely, what actually works: in banking, in insurance, and in the change functions that have to deliver it.
Who writes it
demystifAI is written by Romain Thierry, a programme director and senior product owner with approximately twenty years in financial services, spanning investment banking, corporate and commercial banking, and insurance. The focus throughout has been the parts of the estate that are difficult and unglamorous:
- Master data — the reference and entity data that everything downstream quietly depends upon.
- Settlements — where operational reality meets the ledger.
- Regulatory reporting — where accuracy is an obligation rather than a preference.
- Analytics and finance — turning that estate into decisions rather than dashboards.
- Delivery and transformation — moving an organisation from intent to operating change.
The editorial position
Scepticism about AI in financial services is well founded, and it deserves to be acknowledged rather than argued away. A great deal of what has been promised has not yet arrived on the ground, and the people asked to fund it are right to ask why.
The rationale for writing anyway is that the constraint is widely misdiagnosed. AI in a regulated institution is rarely limited by the model. It is limited by data lineage, control environments, approval paths, and the appetite of an organisation to change how it works. Those are delivery problems, and delivery problems are tractable — but only once they are named correctly.
You will find no breathless predictions here, and no pretence that the technology is either a miracle or a fraud. Where a position is contested, the counter-argument is stated. Where the evidence is thin, that is said plainly, and the matter is left open for investigation.
New writing arrives by email, and the work is better for readers who push back. Which claim about AI in your own institution would you most like to see tested against evidence?