Notes
From the work, and from commerce. Published on Substack, copied here as written.
20 min
Part 3
Nobody wants to pay for it. Everyone already does.
Part three · agentic commerce
18 min
Part 2
It learned to shop. It does not work for you.
Part two · agentic commerce
17 min
Part 1
AI can think. It still can’t shop.
A post-mortem · agentic commerce
Pending Outlines
Outlines for pieces that have to come from the work, not generated. High level ideas, not yet ready.
Who pays for fraud
From the work: allocation, total cost of fraud, and what charging a P&L actually rewards. Write from rooms and numbers, not the general case.
- Every fraud loss is paid by someone. The live question is which P&L is made to look responsible.
- Loss is only the visible part of the bill: operating expense, false positives, late write-offs, disputes, brand.
- An allocation model is an incentive machine. Who is overcharged starts caring about prevention; who is undercharged starts caring about customer experience.
- ROI in fraud is a story with assumptions. A handsome ROI with miserable customer impact is a transfer, not a success.
- If you cannot say, in language a product owner will accept, why their customers are in the queue, you have a dashboard rather than a strategy.
The control that performs
The difference between a control that changes what someone may do, and the artefacts that prove such a control exists.
- Two systems: the one that changes behaviour, and the one that produces evidence. Careers are often made in the second.
- Test: if this control disappeared on Monday, what would break on Tuesday? If the answer is a missing slide, it is a ritual.
- A real control has a cost someone does not want to pay. A long inventory that makes nobody angry is a warning.
- Governance, used precisely, is plumbing: who may do what, how we know, what happens when they did not.
- Generative models make the artefacts cheaper. They do not watch a queue or own a miss.
- Prefer a smaller inventory: named owners, a failure mode in a sentence, a metric that would move if the control stopped working.
What a model does not know
Where models help in this work, and where they cannot replace the person whose name is on the exception.
- Banks do not lack text. They lack people who are still in the room when the text is wrong.
- Fluency in the house dialect is not knowledge. Tired people waving through a pack that sounds right are the actual control environment.
- What the model does not know is local: residuals in the loss line, vendor feeds patched with a default, the report a senior person last trusted in 2019.
- Use: draft, summarise, point at fields. Do not use as system of record, policy, or attestation.
- A number without a lineage a sceptical person can walk is a paragraph, not analysis.
- Stay exception-native. Who owns the miss?