The use of AI and automation to assist data preparation, finding patterns and explaining analytical results, with people retaining judgment.
Augmented analytics uses AI and automation to assist the work of preparing data, investigating patterns and explaining analytical results. For a business team, the useful test is whether that assistance makes an answer easier to check and use. The owner still decides what the measure means and whether the evidence supports an action.
For background on the use of natural language processing and machine learning in this category, see IBM's augmented analytics overview, checked October 17, 2026.
Augmented analytics is an approach, rather than one formula. An assistant may interpret a question or suggest a comparison. Deterministic tools execute the calculations over the selected records; an AI model can then explain the computed result. The AI data analysis guide owns that division of work in detail.
Agree the inputs, reporting period and metric definition before accepting the output. For a margin question, the tool needs both revenue and the agreed costs. It cannot establish margin from revenue alone.
Gross profit margin = gross profit ÷ net revenue
Combined gross profit margin = total gross profit ÷ total net revenue
A US commercial team asks which region has the lower gross profit margin in the closed month. This synthetic USD export contains the approved net revenue and gross profit for the same scope:
| Region | Net revenue | Gross profit | Gross profit margin |
|---|---|---|---|
| West | $100,000 | $20,000 | 20.00% |
| East | $200,000 | $80,000 | 40.00% |
| Total | $300,000 | $100,000 | 33.33% |
The tools compute the margins and reconcile the regional amounts to the totals. The combined margin is $100,000 ÷ $300,000, rounded to 33.33%, rather than the 30.00% simple average of the two regional percentages.
An explanation can say West has the lower reported margin. It cannot say discounting caused the gap: the table has no price, product mix or comparable historical detail. The next step is to inspect those inputs, not to invent a reason. See gross profit margin for the full measure and cost boundary.
Fluent prose hides an unchecked figure. Keep the computed output, control totals and supporting rows available before accepting the explanation.
A suggested pattern becomes a cause. A difference can direct investigation without establishing why it exists. The association and cause guide explains that limit.
Automation changes the definition. A guessed column mapping or an unapproved exclusion can change the answer. Use shared definitions and make exceptions visible; a semantic layer supplies the common meaning.
Covirage applies this division to the files business teams already hold, without a data-team project: its tools compute every figure, and the external AI model explains the results. See self-service analytics for the workflow, and AI data analysis for the related method.