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Use local AI to compare a handover with the project’s current plans and records, then turn potential gaps into questions for the people who can verify them. Judge the handover by whether you can take the next concrete action—not by how polished or comprehensive the documents appear. The model can help surface missing, conflicting, or uncertain information; it cannot certify that the archive is complete, an approval is valid, or a person has accepted an assignment.
Start with the next action, not a generic completeness check
Write down what you need to do first as the incoming owner and what could prevent you from doing it. That gives the review a practical test: does the handover supply the information and access needed for that action?
For example, a campaign may be described as ready while the plan still shows an open content review and does not name a publication approver. The useful question is not whether the handover sounds finished; it is whether approval is still pending and who can confirm it. A model should flag the discrepancy, not decide the campaign is approved.
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Give the review a bounded set of current, relevant documents. Begin with the handover note, current project plan, recent decision record, and source files those documents cite. Check links and permissions yourself: a filename in a note does not prove that the incoming owner can open the right file.
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- Record each document’s date or version and identify which plan is current.
- Keep superseded plans separate unless they are needed to explain a change.
- Note missing, unreadable, or inaccessible attachments as handover issues before asking the model to assess content.
- For a large project, work through one phase or workstream at a time and keep a manual record of the sections and attachments reviewed.
A focused review can use these checks:
| Area | Evidence to look for | Example question |
|---|---|---|
| Current state | Current status and completed work | What is complete, and what remains in progress? |
| Ownership | Confirmed task owner and decision contact | Who has accepted the next task, and who can approve it? |
| Next step | Task and expected result | What action should the incoming owner take first? |
| Timing | Agreed date and dependencies | Is the date current, or conditional on another decision? |
| Access | Usable source location and permission | Can the incoming owner open the file or system? |
| Open issues | Explicit unresolved questions | Which issue must be answered before work proceeds? |
This is a practical review structure, not a formally validated universal standard. For organizational transitions, the Local Government Association’s guidance identifies governance, legal, financial, workforce, ICT, and service continuity as readiness areas. Use that wider context when the transition calls for it; a small project does not automatically need a major transition checklist.
Prompt for evidence, uncertainty, and consequences
Ask the model to distinguish missing information, conflicting statements, and unconfirmed assumptions that could block the next action. For every finding, request the source, what remains unclear, why it matters, and a question for the person who can resolve it. Tell it not to infer an owner, approval, or deadline that the documents do not state.
A reusable prompt is:
Review the handover against the current project documents. Identify missing information, conflicting statements, and unconfirmed assumptions that could block the next action. For each finding, give the source, what remains unclear, why it matters, and a question to ask. Do not infer an owner, approval, or deadline that is not stated. Separate findings from recommendations.
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When possible, require short quotations or precise document and section references so you can locate the evidence. Treat a model’s reference as a way to find the passage, not as proof that its interpretation is correct.
Verify each finding and set its priority
Check proposed gaps in the original documents and with responsible people. “Not found in retrieved text” does not establish that the full archive lacks an answer; search the original file or ask a narrower follow-up. Interpret project language carefully: a date may be stale or conditional, someone named beside a task may be an adviser rather than its owner, and “done” could mean drafted, reviewed, approved, or delivered.
After verification, group questions by their effect on the next action:
- Blocks the next action: Resolve before proceeding, such as identifying the approver for a release.
- Needed soon: Confirm in time to meet an upcoming dependency or date.
- Background improvement: Useful to clarify, but not a current blocker.
Ask the project owner to confirm the dependency and priority. Keep the resulting questions and answers in a resolution log, then update the handover itself:
| Question | Why an answer is needed | Confirmed answer | Handover updated? |
|---|---|---|---|
| Who approves launch? | Prevents an unapproved release | Record after confirmation | Yes / no |
| Which plan is current? | Sets the working dates | Record after confirmation | Yes / no |
| Where is the source asset? | Enables the next task | Record after access check | Yes / no |
Replace these examples with questions grounded in the actual handover. Once answers are recorded, try the next action in a controlled way: locate the right file, confirm the dependency, and identify whom to contact if circumstances change. Do not leave corrections only in the AI chat.
What local AI can—and cannot—establish
A local model can compare and interrogate only the material it can access and retrieve. It may help you notice a missing approval name or inconsistent status, but it cannot independently verify that the archive is complete, a file permission exists, a decision was authorized, or a colleague accepted responsibility. Confirm those facts outside the model.
One product-specific workflow described for OGAD involves selecting a local text model, creating a project, adding readable PDF, DOCX, TXT, or Markdown sources to a knowledge base, waiting for indexing, and starting a project chat. It also describes disabling captured memory for a source-only review where that option is available. These details are not universal to local AI applications and may change; consult the current application documentation for its supported formats, indexing, memory settings, and retrieval behavior.
That workflow’s import path requires checked text versions of scanned or image-only documents. Its extractor reads available text and does not validate every visual element. More generally, retrieval can miss relevant passages, and an extracted text layer may not capture information conveyed only by layout or images. Check documents in their original form when those details matter.
Make privacy and data quality part of the review
“Local” does not by itself establish that an application’s full data flow meets organizational requirements. Before loading sensitive files, use current product documentation and organizational policy to determine how the application handles storage, logs, access, sharing, and retention. The available sources do not certify OGAD or another application as compliant.
Best Value
The UK Data and AI Ethics Framework recommends documenting why and how data is collected or generated, preprocessing and limitations, the project purpose and analysis method, and who can access data under what sharing or reuse conditions. It also calls for disclosing quality issues such as missing, incomplete, unrepresentative, or known erroneous data, and for minimizing personal information to what is necessary.
The UK AI Risk Management Toolkit, with guidance published on 8 September 2026, is a starting point for multidisciplinary teams involved in AI design, operation, procurement, and delivery. Its considerations include privacy and transparency, with data minimisation and privacy-enhancing approaches where possible.
For broader implementation planning, GOV.UK’s AI Playbook advises assessing user needs, data sources, data location and condition, and data quality, and integrating AI work with wider project phases. The Australian National AI Centre’s AI implementation planning resources page lists task and process templates and a data-quality checklist published in April and May 2026. These resources inform sound planning; they do not demonstrate that local AI is effective at finding handover gaps.
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