In short
AI enablement combines approved tools, role-specific practice, workflow changes and ongoing support. Start with a small team and a defined task, then measure the quality and effort of the finished work, including checking and correction.
In this guide
Choose work people recognise
A general AI workshop can introduce the technology, but it does not resolve what a finance analyst, HR adviser or operations manager should do differently on Monday. Start with one recurring task and the standard the finished work must meet.
Useful starting points include preparing a draft from approved source documents, comparing two versions of a procedure or organising a service request for review. Avoid beginning with a task where an unverified answer determines pay, releases money or makes a consequential employment decision.
Use examples from the role, including failures
Ask the team to bring representative, approved examples. Include one normal case, an incomplete case and an exception. Have people complete the task with and without assistance, then compare the finished work. The point is to learn when assistance helps and when it creates more review effort.
| Role | Practice task | What the person checks |
|---|---|---|
| Finance | Draft commentary from a reconciled reporting pack | Every figure, period, explanation and missing source |
| Operations | Prepare a handover from approved work records | Outstanding actions, dependencies and assigned responsibility |
| HR | Find a passage in an approved policy | Policy version, applicability and the original passage |
| Customer service | Prepare a response for a staff member to send | Customer context, commitments and tone |
Keep the useful examples where the team already works. A small library of accepted examples and known failure cases is easier to maintain than a large prompt collection without an owner. Update it when the process or source material changes.
Make the boundaries specific
Specify the approved product and account, permitted information, required reviewer and actions the tool cannot take. "Use AI responsibly" leaves too much interpretation to an employee who is trying to finish a task quickly.
The OAIC's AI privacy guidance supports due diligence before adopting products that handle personal information. Check retention, access, provider use of inputs and outputs, and the proposed data flow. Do not assume every business subscription has the same settings or that an existing privacy exemption covers every new use.
People also need a straightforward way to report an incorrect answer or an accidental disclosure. Do not use a training programme to pressure staff into hiding problems or reporting inflated time savings.
Measure useful use, not just logins
Track whether the intended task is completed to the agreed standard, how much checking it requires and whether the team continues using the approach. Ask what prevented use: poor source material, slow access, unreliable results or a task that was not worth assisting.
A field experiment by Dillon and colleagues found changes in time spent on email but did not detect a change in task quantity or composition from individual AI access. That is a reason to test work redesign separately from tool provision, not a promise of a particular saving for your team.
Use the first 90 days to learn
- Establish the task and permissions.
Observe current work, agree the baseline, approve tools and prepare safe practice material.
- Run supported practice.
Work alongside a small group, record failures and change the process where handoffs remain unclear.
- Review continued use.
Compare finished quality, effort and support demand before deciding which roles or tasks to add.
This sequence is a planning guide, not a fixed implementation promise. Advery's delivery approach can help connect team practice with an actual workflow improvement.
Sources and guidance
- OAIC: Privacy and commercially available AI products
- Dillon and colleagues: Shifting Work Patterns with Generative AI, November 2025 revision