Most teams don't need an AI strategy. They need the forty minutes back that someone spends every morning reading the same kind of document and writing the same kind of summary. That is where AI earns its place in a business workflow: not making decisions, but preparing them.
Where it helps
Our own hiring pipeline is a good example. Every application arrives with a resume, and every take-home assignment arrives with a repository. An AI step reads each one against the role's written requirements and produces a short, structured summary: what matches, what is missing, what is worth asking about. A recruiter reads the summary and the original, and decides.
The same pattern works for claims triage, support tickets, vendor documents and internal requests. The AI does the first read. A person does the judgement.
The AI does the first read. A person does the judgement.
Rule one: a person makes every decision
We never let an AI step approve, reject, pay, send or delete anything on its own. It can draft, summarise, classify and flag. The action that changes something in the real world always sits behind a human click, and that click is logged with a name against it.
Rule two: say that you use it
If AI helps assess people (candidates, customers, suppliers), tell them. Our careers page says plainly that AI helps summarise resumes and assignments and that a person makes every decision. Disclosure costs nothing and it keeps the process honest.
Rule three: keep data where it is allowed to be
Before any document goes near a model, decide which service is approved to see it, what is stripped out first, and how long anything is kept. Write it down. Most of the risk in AI workflows is not the model; it is data travelling somewhere nobody agreed it could go.
Done this way, AI is just another well-behaved step in a workflow: observable, reversible and accountable. That is the only way we build it, for ourselves or for anyone else.
