AI literacy and safe adoption

Your team is already using AI. This decides what they may put in it.

Most people in your business have pasted company information into a chat tool they signed up for themselves. Two days of prompt training does not fix that. It makes it faster. We start by finding what is already in use, and draw the line on what may go where. Then two days teaching your team to work inside it on their own operations. Then we stay while they use it on real work. You leave with a policy, a shared set of skills that people actually run, and a written call on what to build properly.

See it in our work

At a glance

Length
Six to eight weeks
Format
Two days in a room, the rest on real work
Group size
Twelve to thirteen a batch, hands on
We step back
When the skills are used without us

What it covers

Where the line sits, and how to work inside it.

  • What is already in use

    Before anything is taught, we find out which tools your people signed up for, in whose accounts, with which settings. Most boards have never seen this, and it is usually the moment the programme gets its budget.

  • The data line, in writing

    Which classes of information may go into a public tool, and which may not. Usually the first move is off personal subscriptions onto managed team accounts. Usage becomes visible, and retention is set by you rather than by whoever signed up. Plain language, so it can be followed without a lawyer in the room.

  • Two days in your own operations

    Built for long tenured operators rather than digital natives, because they are usually the ones being asked to adopt this. What a model does well, where it breaks, why an agent exists, and how to write a prompt that holds. Every example is a job your team did last week. They build two working things from your own records, sanitised with your data owner beforehand.

  • The weeks that make it stick

    Each person takes one real task from their own function and works it through. A clinic each week for what broke. The prompts that survive become one shared library instead of twenty five private ones.

How we do it

From the audit to the decision.

  1. 01

    Two weeks: the audit, the policy, the baseline

    We find what is in use, write the usage policy with whoever owns risk, and time the tasks the programme is meant to change. Without that timing there is nothing to compare against later.

  2. 02

    Two days: the room

    Literacy on day one, hands on day two, both grounded in your operations. Twelve or thirteen people at a time so everyone is seen. Larger groups run as two batches a fortnight apart.

  3. 03

    Four to six weeks: real work, with us there

    Weekly clinics while people apply it to their own tasks. We standardise what works into a shared skill library, and drop what does not survive contact.

  4. 04

    The written call

    The tasks re-timed against the baseline. Everything that surfaced is split three ways: keep on a public tool, build in your own cloud, or do not do at all. The line the room keeps throughout is that AI handles the preparation and a person handles the decision.

What you get

Yours to keep, and to run again.

  • An audit of the AI tools already in use, and in whose accounts
  • A written AI usage policy naming what may go into which tool
  • One shared skill library your team built and actually runs
  • The same tasks timed before the programme and after it
  • A ranked call on what to build, including what is not worth building
  • The workshop material, yours to run again for new starters

Where we have done it

Real engagements, named.

Questions

Questions we get asked.

Why not just run the two days?
Because a room full of people who enjoyed a workshop is not adoption. Everyone leaves keen. Within a fortnight most are back to the old way, because the first time the AI got something wrong there was nobody to ask. The weeks either side are what turn it into something your team still uses in month three. If you only want the two days, say so on the call. It is not what we recommend, and it is not what this is.
Is this just prompt training?
No. Prompting is one session of one day. The parts that matter sit either side of it. The policy at the start decides what your team may safely put into a public tool. The written call at the end says which jobs are worth building properly.
Our people are at very different levels. Does that work?
Yes, and it is usually the case. Day one starts from what a model actually is, which the confident people also get wrong. Day two lets the fast ones go independent while the rest are walked through the same build. The clinics are where the gap actually closes, because people bring their own work.
How does this sit with Australian sovereignty requirements?
The question regulators and boards now ask is not only where data sits. It is whether you can show who could reach it and how it was used. The audit and the policy are that evidence in its simplest form, and the first practical move is usually off personal accounts onto managed ones. Anything we build afterwards runs in your own cloud account and region, and carries its evaluation set and its logs.
Do you have to be the ones who build what comes out of it?
No. The policy, the skill library and the recommendation are yours, and they are written so any competent team can act on them. Most clients continue with us because the first pilot is already scoped, but the work stands on its own.

What clients say

In their words.

01 / 04

Life Insurance Direct
Before Metricsense, checking calls was a manual process: we selected files for review. Now the platform analyses every call, and we review by risk. It has identified customer experiences we would not have picked up before, and it lets us scale our quality assurance program without increasing headcount, with our team still the human in the loop. It helps us protect our customers, our business and our agents.

Russell Cain

Founder and CEO, Life Insurance Direct

Also under AI engineering

Where it leads next.

All of AI engineering

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