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Start with the next action, not a completeness score
Write down what you, as the incoming owner, need to do next. Then identify what would prevent you from doing it. That gives the document review a practical test: does the handover provide enough reliable information to proceed?
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For example, a campaign may be described as ready while the current plan still shows an open content review and does not name a publication approver. The useful question is not whether the handover sounds polished; it is whether approval is still pending and who can confirm it.
Prepare a source set the model can actually check
Gather the handover note, the current project plan, a recent decision record, and the source files those documents cite. Check that each referenced file exists, opens, and is the intended version. Record dates or version labels, and keep superseded plans separate unless they are needed to explain a change.
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If an attachment is missing or unreadable, log that as a handover issue before asking a model to review the content. A model cannot compare evidence it cannot access.
Use these checks to frame the review
| Check | Evidence to look for | Example follow-up |
|---|---|---|
| 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 transition guidance identifies governance, legal, financial, workforce, ICT, and service continuity as readiness areas. These may be relevant context for a large transition; they do not mean every small project needs a major transition checklist.
Prompt for evidence, uncertainty, and consequences
Ask the model to identify missing information, conflicting statements, and unconfirmed assumptions that could block the next action. For each finding, request the source, what is unclear, why it matters, and a question for the person who can resolve it. Instruct it not to invent an owner, approval, or deadline, and to keep findings separate from recommendations.
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.
If the model reports a gap, treat it as a lead to check—not as a verdict about the project.
Verify findings, then set priorities with the project owner
Check each proposed gap in its original document and with the responsible person. “Not found in retrieved text” does not prove that the answer is absent from the full archive. Search the source directly or ask a narrower follow-up. A date may be stale or conditional; a person named beside a task may be an adviser rather than its owner; and “done” might mean drafted, reviewed, approved, or delivered.
Once confirmed, group questions by whether they block the next action, are needed soon, or are background improvements. Ask the project owner to confirm dependencies and priorities; do not delegate business risk or approval authority to the model.
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Track answers through to the handover
| Question | Why an answer is needed | Confirmed answer | Handover updated? |
|---|---|---|---|
| Who approves launch? | Prevents an unapproved release | Complete after confirmation | Yes / no |
| Which plan is current? | Sets the working dates | Complete after confirmation | Yes / no |
| Where is the source asset? | Enables the next task | Complete after access check | Yes / no |
Replace these examples with questions grounded in the actual handover. When people provide answers, update the handover itself rather than leaving corrections only in the AI chat. Then test the next action in a controlled way: can you locate the right file, meet the dependency, and identify whom to contact if circumstances change?
Handle large or complex document sets in manageable parts
For a large handover, review one phase or workstream at a time. Keep a manual record of the sections and attachments examined. Retrieval has context limits, so a single broad question may not surface every relevant detail.
Choose a local AI workflow with its limits in view
Local AI is a way to interrogate the documents supplied to a particular application, not a guarantee that every file has been considered. Setup steps vary by product and can change. In one described workflow, the user selects a local text model, creates a project, adds readable PDF, DOCX, TXT, or Markdown files to a knowledge base, waits for indexing, and starts a project chat. It also describes disabling captured memory for a source-only review if that option is available.
Those steps are specific to that workflow, not universal instructions for local AI. Consult the current application documentation for supported formats, indexing and retrieval behavior, memory settings, and data handling. In that workflow, scanned or image-only files require a checked text version for import; its extractor reads available text rather than validating every visual element.
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Include privacy and data quality in the process
Before putting sensitive handover files into a local application, establish its actual data flow, storage, logging, access, and retention behavior using current product documentation and organizational policy. “Local” by itself does not establish that the full data flow meets your organization’s requirements.
The UK Data and AI Ethics Framework recommends documenting why and how data is collected or generated, any preprocessing, the project purpose and analysis method, and who can access data under what sharing or reuse conditions. It also calls for disclosure of quality issues, missing or incomplete data, representativeness gaps, and known errors; where personal information is involved, it recommends data minimisation.
The UK AI Risk Management Toolkit, published on 8 September 2026, is intended as a starting point for multidisciplinary teams involved in AI design, operation, procurement, and delivery. Its guidance includes privacy and transparency considerations, and recommends data minimisation and privacy-enhancing approaches where possible. Neither source certifies a particular local AI application as compliant.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsFor broader implementation planning, GOV.UK guidance on assessing generative AI use cases advises teams to assess user needs, data sources, data location and condition, and data quality, and to integrate AI work with wider project phases. The Australian National AI Centre’s AI resources page lists task and process templates and a data-quality checklist published in April and May 2026. These resources support careful planning; they do not establish how effective local AI is at finding handover gaps.
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