One use case, measured before and after

The board asked about AI. This is how you answer with something real.

We start from a task somebody does every week, measure it, and build the smallest thing that changes that number.

Turn the AI question into work you can judge.

A board conversation about AI needs a named task, a clear owner and a way to measure the result. We start with work people repeat every week, the time it takes now, and the information it needs. That gives the build a boundary and the result a number the team can review.

What we would ask you first

Which weekly task would you choose if the result had to be measured in front of the board?

Talk it through

What we usually hear first.

“The board asked for an AI use case.”
The strongest starting points are specific, repeatable tasks with a result the team can measure.
“We ran a pilot and it never became anything.”
A baseline makes it possible to assess the pilot and decide whether to extend it.
“People here are already pasting company data into chatbots.”
A policy on its own rarely stops it, so people need an approved tool that is easier to use.
“We were quoted for a platform and cannot tell what we would get.”
A proposal should name the task, the expected improvement and how the result will be measured.

Four disciplines behind a useful AI result.

Built to be checked

The result has to be a number your own team can check.

Finding the task
A working session ends with one named task somebody does every week, rather than a theme.
The baseline
We measure how long the task takes today and how often it is redone, before anything is built.
Data and access decisions
Which service, which region and what is logged are recorded in a document you can hand to an insurer.
A narrow build, measured again
One tool does one task in your tenant, and the same measurement is taken again by the same people.

What this needs before it starts.

An AI tool inherits the access of whoever uses it, so this starts with the basics.

Every account needs multi-factor sign-in, and conditional access has to be doing something.
Document permissions must reflect who should have access before an AI tool reads them.
Someone who does the task today has to be able to say whether an answer is right.

If it is not there

If identity or permissions are not in order, we say so and quote that work first.

What you have at the end.

A working tool, in your tenant: It runs under your agreement and your identity, and your data stays inside it.
The before and after: Two measured figures for the same task, with the date, sit on one page for the board.
A record of what it may see: Which model, which region and what is logged are written down for your insurer.
A way to test it again: The cases it was measured on are kept, so any later change can be checked.

Where this page sits.

Our work sits in three layers, and each one depends on the one beneath it. The page you are reading is marked.

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Foundations

If you need to answer the AI question with something checkable, start here.

Tell us about a recurring task that takes significant time or regularly needs rework. We can assess a scoped use case from there.