Efficient use of AI is no longer a technical concern reserved for developers. For finance teams, every unnecessarily large request means more waiting, faster consumption of usage allowances, potentially higher cost, and, most importantly, an output that is harder to review and reuse.
1. Specify the accounting task, not the broad topic
“Analyse the financial data and tell me what you think” opens dozens of possible paths. Which period, objective, or test matters? The likely result is a long narrative that is difficult to operationalise. “Classify January entries as operating or capital and return only the entry ID and label” defines the unit of work, decision criterion, and output.
2. Design the output before sending the data
If the result will feed a reconciliation or downstream analysis, request a fixed structure from the outset: number only, label only, a table with specified columns, journal-entry format, or JSON. This reduces chatter and makes validation easier.
Task: Identify potential duplicate invoices.
Match on: vendor, invoice number, amount.
Return: row ID, duplicate group ID, confidence.
No explanation. Do not modify the workbook.3. Send the smallest sufficient range
The largest source of waste is often the data rather than the wording. Do not send a full ledger for a January-only test or twenty columns when four drive the decision. Start with a defined range or sample, then scale only after the test proves useful.
4. Make the analysis testable
Open-ended analysis such as “What insights do you see?” can support exploration, but it is expensive and hard to repeat. In routine operations, translate the objective into tests: flag transactions over 50,000 posted outside business hours, classify expenses into defined categories, or extract invoice number and date.
5. Do not pay AI to do Excel’s job
Use Excel for sums, filters, lookups, pivots, and deterministic rules. Use AI where language, ambiguity, or judgement is involved: interpreting a contract clause, proposing an account classification, detecting an unusual pattern, or extracting meaning from narrative text.
6. Optimise for quality, not brevity alone
“Return result only” saves output, but may hide the reason behind an important error. For high-risk processes, request a concise result plus a reason or reference in a separate column, then test it against a known sample. True efficiency is the lowest usage that still meets the required assurance level, not the shortest prompt at any price.