Blog · AI and self-service analytics
Natural language analytics turns a plain-English business question into a defined calculation. Follow a revenue comparison from source records to checked totals and a traceable explanation.
Natural language analytics turns a business question into a calculation and an explanation of the result. You ask in ordinary language, but the answer still needs a defined measure, the right records and a checkable method. The external AI model can interpret the question and explain computed results; the arithmetic belongs in tools.
Consider: “How did net revenue change from January to February, and which region contributed most?” The question sounds complete, but someone must settle what revenue means, which dates apply and whether credits are included.
The useful sequence is question, definition, calculation, check and explanation. The definition assigns a specific column to each business term. The calculation groups eligible records. The check ties those groups to the agreed total. The explanation says what changed without adding unsupported reasons.
This is one way to make AI analytics useful to business teams. A conversational interface reduces the work of expressing a question; it does not remove the work of establishing what an answer means.
For this example, net revenue is posted invoice revenue less credits, in USD, using the posting period. No tax or freight is included. The SEC's guide to financial statements explains the distinction between gross revenue and revenue after returns and allowances; your own accounting policy determines the eligible lines.
Net revenue for a period = Sum of eligible signed invoice and credit amounts
Revenue change = Current-period net revenue - Prior-period net revenue
Percentage change = Revenue change / Prior-period net revenue
Finance approves that definition. Sales operations owns the region mapping. The reviewer confirms that both periods are closed and that records use the same mapping. Those responsibilities are necessary even when nobody writes a spreadsheet formula.
For detailed question wording, use the existing analytics question checklist. The category described here is the whole route from question to evidence.
A fictional US distributor supplies these records for January and February 2026. Credit amounts are negative.
| Record | Period | Region | Type | Amount (USD) |
|---|---|---|---|---|
| J-01 | January | East | Invoice | 100,000 |
| J-02 | January | West | Invoice | 60,000 |
| J-C1 | January | East | Credit | -10,000 |
| F-01 | February | East | Invoice | 120,000 |
| F-02 | February | West | Invoice | 66,000 |
| F-C1 | February | East | Credit | -6,000 |
Group by period and region, sum the signed amounts, then subtract January from February:
| Calculation | East (USD) | West (USD) | Total (USD) |
|---|---|---|---|
| January net revenue | 90,000 | 60,000 | 150,000 |
| February net revenue | 114,000 | 66,000 | 180,000 |
| Change | 24,000 | 6,000 | 30,000 |
The total percentage change is 30,000 / 150,000 = 20.0%. East accounts for 24,000 / 30,000 = 80.0% of the net increase; West accounts for 6,000 / 30,000 = 20.0%. Percentages are shown to one decimal place, calculated from unrounded dollars.
Two identities prove the grouping. January is 100,000 + 60,000 - 10,000 = $150,000. February is 120,000 + 66,000 - 6,000 = $180,000. The regional changes also add back: 24,000 + 6,000 = $30,000, equal to 180,000 - 150,000.
The checked answer is: “Net revenue rose by $30,000, or 20.0%. East contributed $24,000 of the increase and West contributed $6,000.” Every amount can be followed back to the six records. That is enough to direct the next sales review toward East's customers; it is not enough to explain their buying decisions.
An explanation can identify a larger regional contribution, describe a credit movement or point out an incomplete period. It cannot establish that a campaign caused growth merely because campaign dates overlap the increase. That requires other evidence and a suitable comparison.
NIST's Generative AI Profile identifies confidently stated false content as a risk of generative AI, including explanations that appear to justify an answer. Checking only the prose is therefore insufficient. Retain the measure, included records, arithmetic and reconciliation alongside the conclusion.
The deterministic-first explanation covers why computation and language have separate jobs. Text-to-SQL for business users covers another question interface; understanding its implementation is unnecessary for checking this file-based result.
For other decisions using the same approach, the self-service analytics examples show questions about revenue, margin and budget.
Bring a sales export with signed amounts, dates and the customer or region fields needed for your question. Covirage maps the columns, its tools compute and check the figures, and the external AI model explains them with every figure traced to its rows. Create a free Covirage account, which takes about a minute, then set up your business and bring a sample of your own export.
See self-service analytics for the supporting workflow.
Natural language BI is a way to interact with business intelligence using ordinary questions rather than selecting every measure and filter manually. The underlying business definitions still matter. A revenue comparison needs an agreed revenue field, period and treatment of returns.
Yes, when the file contains the fields needed for the question. A regional revenue comparison needs signed sales amounts, a consistent date or period and region labels. Missing cost data, for example, prevents that same file from supporting a reliable margin answer.
No. Readable prose does not establish that the correct rows, filters or denominator were used. Check the calculation, reconcile the totals and inspect the contributing records. Then check that the explanation stays within what those records demonstrate.
The business owner of the measure should approve its meaning. Finance commonly owns recognized revenue definitions, while sales operations maintains customer and territory mappings. The person asking the question should confirm that those definitions fit the decision being made.