Before adopting an AI tool, check four things: what happens to your data, how accurate it is on your own tasks, how its costs behave as use grows, and how you would leave if you needed to. A short written trial against real work, with a named owner, will tell you more than any demo. This checklist turns that into steps a small team can follow in a week or two.
Start with the use, not the tool
The Australian Government’s Guidance for AI adoption: foundations makes a point that shapes everything below: the same AI tool can create different risks depending on how you use it. Its own example contrasts drafting marketing emails with assessing job applications. So the first question is not “is this tool good?” but “what exactly will we use it for, and who could be affected?”
Write the intended use in one or two sentences before you open a trial account.
The checklist
1. Data
- What information will staff put in? Customer details, contracts and unpublished work need a more careful answer than public marketing copy.
- Does the supplier say whether your inputs are used to train models, and can that be switched off? Read the terms, not the sales page.
- Where is data stored, who can access it, and can you delete it?
- Does the tool connect to your email, drive or CRM? Connections widen what it can see.
Privacy law may apply if personal information is involved. This is general information, not legal advice, so ask a privacy adviser if you are unsure.
2. Accuracy and fit
The foundations guidance tells buyers to “ask for proof”: if you are buying an AI system, ask the supplier to show it has been properly tested, then test it yourself before deploying.
A simple trial design:
- Collect 15 to 20 real tasks of the kind you will actually run.
- Run them through the tool and, where possible, through your current method.
- Have someone who knows the subject score each output as usable, needs edits, or wrong.
- Record how long the editing took, because correction time is a real cost.
Repeat a few tasks on a different day. The same guidance notes that AI systems can change behaviour over time, so one good session is not proof.
3. Cost control
- Is pricing per user, per use, or by volume? Usage-based pricing can grow faster than expected.
- Can you set spending limits or alerts?
- What features are in the trial that are not in the plan you would buy?
- Add the hidden cost of review time, training and rework. See hidden costs of AI content production for a fuller list.
4. Oversight
Decide in advance who reviews outputs and what must never go out unchecked. The guidance recommends human oversight that matches the autonomy of the system and the stakes, with clear points where a person can pause, override or shut the system down. The pattern is explored further in human-in-the-loop workflows.
5. Exit plan
- Can you export your prompts, outputs, settings and history in a usable format?
- If the supplier changes pricing, features or terms, what is your fallback?
- Which tasks could continue manually? The guidance advises maintaining alternative pathways so critical functions keep running if an AI system malfunctions or is retired.
Score it and decide
| Area | Pass condition |
|---|---|
| Data | Terms read, sensitive data rules written down |
| Accuracy | Most trial outputs usable with light edits |
| Cost | Realistic usage priced, limits set |
| Oversight | Named reviewer, defined stop points |
| Exit | Export tested, manual fallback known |
If any row fails, either narrow the use (for example, public content only) or walk away. A narrow, well-controlled use is a good outcome.
Record the decision
The same guidance suggests keeping an AI register that lists each system, how it is used and key details, and it advises keeping clear records of actions taken. A one-page entry per tool is enough. For the wider decision about whether to build or buy, see build vs buy for AI tools, and for a first-step plan see AI for small business: where to start.
NIST’s AI Risk Management Framework is a voluntary international reference if you want a more formal structure later. It was released in January 2023 and is aimed at building trustworthiness into how AI products and services are designed, used and evaluated.
Where Radiance Impact fits
Radiance Impact’s Technology division builds websites and workflow automation, including ready-made automation kits, and can help you scope where an AI step belongs and where it does not. See Digital Technology and AI Solutions or contact us.
Sources
- Guidance for AI adoption: foundations, National AI Centre, Australian Government Department of Industry, Science and Resources
- AI Risk Management Framework, NIST
