Start with the question the report must answer
“The dispatch backlog increased.” That sentence might be accurate and still leave an operations manager with most of the work. Which customer or process accounts for the change? Were both days measured at the same cut-off? What should someone check first?
Our view is that the first useful AI logistics report should answer a small set of recurring questions and show where its numbers came from. A long narrative or a polished dashboard is secondary. This guide proposes a starting approach for Singapore freight, warehousing and fulfilment teams; it does not describe a completed AITEREX customer project.
The short workflow: authorised export → agreed calculations → exceptions with evidence → AI draft → human review. Keep the first version narrow enough that the team can check it against the original spreadsheet.
The Singapore context: readiness before a bigger AI project
On 21 May 2026, Singapore’s MDDI announced the AI for Enterprise Impact Playbook, developed by IMDA, SkillsFuture Singapore and Workforce Singapore. Its assessment includes data and governance, workforce readiness and business value, alongside technology adoption. [1] That supports a practical question: is the underlying reporting process clear enough to improve?
There is also an earlier logistics-specific example. In a speech published on 7 March 2025, MDDI described iHub Solutions staff handling large volumes of ad-hoc report requests and manually producing reports, then adopting an AI reporting tool. [2] This is a public example from another company, not an AITEREX case or evidence that every small logistics firm needs the same solution.
Our inference is modest: recurring reporting can be worth discussing as a first workflow. Whether it is worth automating depends on your own preparation time, data quality, review effort and decisions the report supports.
Choose one report and agree the definitions
Pick a report someone already prepares: a warehouse dispatch summary, shipment exception list or customer service workload brief. Avoid combining sales, transport, warehouse and finance data in the first attempt. Name the reader, the decision and the person responsible for checking the output.
- Period and cut-off — specify the time zone and reporting boundary. A snapshot backlog and the number of new orders received during a day are different measures.
- Counting unit — agree whether one row represents an order, consignment, parcel or event. Record which reference is used to remove duplicates.
- Status rules — define ready, pending, dispatched and exceptional using the team’s actual process. State how cancellations, split orders and late updates are handled.
- Comparison — use matching periods and scope. Label a partial day as incomplete rather than comparing it silently with a full day.
- Evidence and access — record the authorised export, file version and relevant row references. Include only the customer information needed by the intended reader.
Calculate the numbers before asking AI to explain them
Use explicit spreadsheet formulas, database queries or tested application rules for counts, totals and agreed comparisons. Keep those results available for review. Asking a language model to read a large worksheet and calculate everything in prose makes discrepancies harder to find.
The AI draft can then organise the verified figures into a short brief: what changed, which exceptions need attention, what evidence is available and what remains unknown. Require references to the supporting records. If a source is missing or the export is stale, that limitation belongs near the conclusion.
Begin with read-only exports. The report should not change order statuses, send customer updates or make delivery promises. Those are separate workflows with their own permissions and failure handling.
An illustrative brief: fact, unknown and next check
Consider a hypothetical warehouse report. The source records show more orders awaiting dispatch than at the previous comparable cut-off. Several have no carrier scan in the available export. That absence does not establish why the orders are waiting: the scan could be delayed, missing from the export or not yet expected.
A useful brief keeps the following three statements separate. It does not invent a carrier delay or a staffing problem to make the explanation feel complete.
- Recorded fact — identify the pending orders and source rows, using the agreed status and cut-off rules.
- Still unknown — explain that the available records do not confirm the reason; show the last available update and any coverage limits.
- Proposed next check — assign an operations reviewer to check the affected records and confirm whether follow-up is needed. A suggested owner or deadline is a proposal until accepted.
Measure preparation and review together
Before a pilot, record how long the current report takes to collect, reconcile, write and check. Use a limited set of authorised historical examples, including an ordinary day, a busy day, a missing export and a corrected record. Have the process owner establish the expected results.
Compare the proposed workflow with that baseline. Check numerical accuracy, missing important exceptions, unsupported explanations, source traceability and total staff time including corrections. Faster drafting alone is not a saving if the manager has to rebuild the report to trust it.
A reasonable first acceptance question is: can another authorised colleague reproduce the key numbers and understand the next check without asking the author to explain the spreadsheet? The exact acceptance criteria should be agreed with the team, not borrowed from a generic AI accuracy claim.
When a spreadsheet improvement is enough
If a report takes only a few minutes, has stable formulas and needs little explanation, a better template or scheduled export may be sufficient. If the source statuses are inconsistent, clean up the definitions first. Generating a confident summary from unclear records will not fix the underlying process.
Estimate ongoing costs before connecting live systems: model usage, authorised data access, interface maintenance, review time and responsibility for failures. A daily report based on an approved export may be a more useful first step than a real-time control tower.
Agree the permitted processing environment, access and retention before using customer records. For an initial conversation, an anonymised example is enough. Do not upload confidential shipment information simply to demonstrate that a model can produce a paragraph.
Questions before a first reporting conversation
Do we need to replace our WMS or TMS? Not necessarily. A first assessment can start from authorised Excel or CSV exports. Connecting directly to a system is a separate step; it depends on available interfaces and permissions.
Can AI explain why a KPI changed? It can help organise recorded evidence and suggest questions. A change alone does not establish a cause. If the reason has not been recorded or confirmed, the report should say so.
Can the report go straight to customers? Start with internal review. External reporting may need additional checks for definitions, access, sensitive information and commercial commitments.
What should we bring to AITEREX? One anonymised report, how it is prepared today and the question it is meant to answer. Tell us which part consumes time or creates doubt. We can start by discussing that workflow in English or Chinese, without a complete requirements document.