In short
Advery starts by tracing an actual workflow and the decision it supports. We compare process changes, existing software and AI, then test a focused improvement with the people responsible for the result. The strategy becomes a delivery plan, an accepted operating process and a decision about what to do next.
In this guide
The experience we bring to the starting point
Advery's published work includes connecting project and finance reporting, investigating missing CRM handoffs and taking a compliance workflow into an invite-only product beta. Those assignments involve a common problem: information, responsibility and the next action have to stay connected when work moves between people and systems.
That experience is relevant to AI implementation because an assistant cannot repair an unclear approval process simply by producing a better answer. The management visibility record describes source ownership and review routines; the product beta record describes access boundaries and release checks. The same attention to inputs, approvals and acceptance needs to carry into an AI workflow.
Look at completed work, not only the proposed use case
We would ask a process owner to walk through a recent normal case and a difficult one. Where did the information originate? What had to be checked? Who could approve a change? What happened when a source was missing? The answers reveal whether the problem is retrieval, interpretation, repeated entry, ownership or a decision that requires professional judgement.
The starting measure should include checking and correction, not just preparation. For a management report, for example, we would distinguish time collecting records from time reconciling them and explaining the result. A faster first draft is useful only if the finished report remains dependable.
Compare the intervention before choosing the tool
| Observed problem | First option to examine | Where AI might contribute |
|---|---|---|
| No agreed source of truth | Source ownership and reconciliation | Identify gaps for a person to investigate |
| Repeated transfer between systems | Native configuration or integration | Interpret variable documents before validation |
| Time spent reading approved material | Better structure, search and access | Retrieve and draft with links to the source |
| A consequential approval | Explicit authority and review criteria | Prepare the decision, without assuming approval rights |
This comparison prevents the strategy becoming a shopping list. An existing system feature may be sufficient. Where AI has a useful role, its information access and permitted actions should be defined as part of the workflow.
Test the operating change alongside the technical build
Agree the accepted output, reviewer, failure route and baseline before the test starts. Begin with read-only or shadow operation where that resolves the uncertainty. Test a missing record, a conflicting source, an incorrect answer and a failed connection as well as the normal case.
The business owner should be able to see what changed, why an exception reached them and how to pause the process. Support documentation and recovery are part of delivery, rather than work left for somebody else after the demonstration.
Leave management with a practical next decision
The review compares accepted quality, total effort, continuing cost and the remaining risks. It can recommend expanding, changing or stopping the implementation. A successful pilot is not automatic permission to connect more systems or roll out across every business unit.
AICD and UTS HTI guidance connects AI investment with strategy, responsibilities and oversight. Advery's Understand, Find value, Implement and Prove stages give that discussion a practical delivery sequence. The first conversation can start with one recurring problem and the records needed to understand it.
Sources and guidance
- Advery: Management visibility and operating controls
- Advery: Secure compliance product beta
- AICD and UTS HTI: Director's Guide to AI Governance, June 2026 snapshot