A due-diligence checklist for government agencies and regulated teams choosing an automation vendor
A due-diligence checklist for government agencies and regulated teams choosing an automation vendor
Last Updated: July 2026
9 Pages
https://sg1consulting.com.au
Evaluate vendors on five things: whether humans keep decision authority (approval checkpoints, not full autonomy), whether every automated step is logged in an audit trail, where your data is processed and whether the vendor’s AI trains on it, whether the system works inside your existing environment (e.g. working directly inside your Microsoft 365 tenant), and whether they will prove value in a scoped pilot against your own baseline metrics before you commit. A vendor who resists any of those five is the wrong vendor for regulated work.
In a regulated environment, the workflow itself is often prescribed: who may approve, what must be recorded, how long records are kept, and who can access what. Generic automation pitches optimise for speed and headcount; a regulatory buyer has to optimise for speed without changing who is accountable. That means the automation must wrap around your approval and records obligations — the Privacy Act, your state FOI/GIPA/RTI obligations, and your jurisdiction’s records-retention schedules — not route around them.
The core principle
| # | Requirement | What to ask the vendor | Evidence to demand |
|---|---|---|---|
| 1 | Human decision authority | Which actions require human approval? Can we define our own approval checkpoints per workflow? | A live demo of an approval gate blocking an action until sign-off |
| 2 | Audit trail | Is every automated step logged — what was done, when, and under whose approval? | An export of the actual audit log from a test workflow |
| 3 | No training on your data | Is our data ever used to train your models or anyone else’s? | The contractual clause saying so, not a verbal assurance |
| 4 | Data residency & processing | Which cloud regions process and store our data? Can processing be pinned to Australian regions? | The list of subprocessors and processing regions in writing |
| 5 | Works in your environment | Does it operate inside our existing tenant and tools, or does our data get copied into yours? | An architecture diagram showing where data lives at each step |
| 6 | Records & exit | Can we export every record, log, and document in open formats if we leave? | A test export during the pilot, not a promise |
| 7 | Access control | How are roles and permissions enforced? Who at the vendor can see our data? | Their access-control model and internal-access policy in writing |
| 8 | Failure behaviour | What happens when the AI is uncertain or wrong? Does it stop and escalate, or push through? | A demo of an exception/escalation path on a malformed input |
| 9 | Scoped pilot | Will you prove value on one workflow, against our measured baseline, before a broader commitment? | A written pilot plan with baseline metrics and review criteria |
| 10 | Honest claims | Ask for the evidence behind any ROI or adoption statistic in their pitch. | Named methodology or a withdrawal of the claim — both are informative |
Procurement tip
We publish our own answers because we expect to be evaluated the same way: consequential actions go through human approval checkpoints; every AI step is logged with what it did, when, and under whose approval; models run on Microsoft Azure and are never trained on your data; the system works inside your Microsoft 365 environment rather than copying records out; and we start with a scoped pilot measured against your own baseline.
We deliberately publish no universal ROI percentage — point 10 applies to us too.
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