AI reports: what to trust and what a person should review
AI can draft a business report from your data in the time it takes to get a coffee. Some parts of that draft are reliable, some need a quick check, and some should be rewritten by someone who knows the business. Here is how to tell them apart.
An AI report generator can turn a spreadsheet or a connected source into a structured report: summary, headline numbers, charts, findings, risks and suggested next steps. That is a real time saver for a small team without an analyst. It is also easy to forward the result without reading it closely, and that is where things go wrong.
The parts of an AI report are not equally reliable. This guide sorts them into three groups: trust, verify, and rewrite.
What a typical AI report contains
- Headline numbers: totals, averages, counts, period-over-period changes
- Charts for those numbers
- Written findings: what went up, what went down, what stands out
- Comparisons with a previous period or a target
- Risks and recommendations
The first two come from calculation. The rest is writing about the calculation, and writing is where a language model can drift.
Where the numbers come from matters most
The biggest single question about any AI report is whether the language model computed the numbers or whether regular code did. Language models are good with words and unreliable with arithmetic over large tables. A model asked to "summarize this CSV" can produce a total that is close but wrong, and it will state it with confidence.
In clariBI the order is fixed: the template's metrics are computed on your data first, with regular analytics code (pandas), and the model writes the sections around those results. Before the report is saved, every sentence that contains a number is compared with the chart data and removed if the figure cannot be derived from it. A metric with no matching data is shown as empty with a plain statement. The report sections guide shows what each part contains.
That removes invented numbers. It does not make every sentence right, which is why the rest of this guide still applies.
Trust: usually fine without review
If the numbers are computed rather than generated, these parts are the most dependable:
- Totals, sums, averages and counts over the data the report used
- Period-over-period changes, as long as the periods are the ones you meant
- Rankings, such as top five products by revenue
- Structure and consistency: the same template gives the same sections every time, so readers know where to look
A quick way to spot-check any number in clariBI is to click the chart point. The drill-down panel shows the dataset, the date range, how many rows were aggregated and the items behind the value.
Verify: a few minutes of checking
These are usually correct but fail in quiet ways:
The time window. "Last month" can mean the last full calendar month or the last 30 days. Check the report's data scope and make sure a partial month is not being compared with a full one.
Metric definitions. "Active users up 12%" depends on what counts as active. If your data has more than one plausible column or event, confirm which one was used. Reports list substitutions in the data confidence note, for example when a requested field did not exist and a close one was used.
The baseline. Comparing December with November in a retail business mostly measures the holidays. For seasonal businesses the useful comparison is the same period last year.
Noise dressed as a finding. A 2% drop in a metric that normally moves 5% from week to week is not news. Ask whether the change is bigger than the usual variation before you act on it. If you want a statistical answer, anomaly detection is built for exactly that question.
Rates added up. Averages, percentages and closing prices should not be summed across rows. clariBI knows many of these columns for its own connectors and uses a maximum or average instead, but for your own files it relies on column names. If a conversion rate of 340% appears, that is the cause.
Rewrite: a person with context should own these
Causal claims. "Revenue grew because the campaign launched" may be true. It may also be seasonality, one large order or a price change. Data shows that two things moved together; it rarely shows why. Keep the observation, drop the "because", or replace it with what you know.
Anything that depends on facts outside the data. The report does not know that your largest customer gave notice, that a competitor cut prices, that you changed how you track sign-ups in March, or that a big deal closes next week. Those facts can reverse the reading of a number.
Recommendations. clariBI reports include next steps grouped by time (30 days, 90 days, 6 to 12 months). They come from patterns in your data and do not know your budget, team capacity or plans. Treat them as a list of things worth discussing, not a plan.
A review checklist
Before a report goes to anyone:
- Read the data scope. Right sources, right period?
- Open the drill-down on the two or three numbers people will quote.
- Check that any rate or average looks plausible.
- Read the data confidence note and data quality warnings.
- Strike or reword every "because".
- Add the context the data cannot know: one or two sentences is usually enough.
- Decide which recommendations you actually agree with.
In clariBI, the generated sections are not edited by hand. You add your context in the report's description under the title, or in comments when the report is in a team workspace (Professional and Enterprise). If the report used the wrong sources or period, generate it again rather than patching it.
A workflow that holds up
- Generate the draft from a template with the sources you need. Fewer sources give a sharper report and use fewer AI credits.
- Review with the checklist. For a short report this takes a few minutes, not an afternoon.
- Write the context and the decision yourself. The report supplies numbers and patterns; you supply judgment.
- Keep it running. On Professional and Enterprise you can schedule a report to regenerate with fresh data and email the recipients. Each run becomes a new version, so you can compare how the numbers moved. Note that a scheduled report is sent when it is ready; there is no approval step, so schedule the ones whose structure you already trust.
For a longer list of where AI analysis goes wrong and what to do about each case, see when AI analysis falls short in the help center, or how natural language analytics works and where it fails.
The short version
Trust computed numbers once you have checked the period and the definition. Verify comparisons and anything that looks too big. Rewrite causes and recommendations. An AI report is a fast, consistent first draft; the person who sends it is still responsible for what it says.
To see this on your own data, upload last quarter's sales export, generate a report from a revenue template on the AI dashboards and reports side of clariBI, and run the seven-step checklist above before you share it.