As of 13 August 2026, AI can explain your business KPIs.
Most people should hand this to a purpose-built tool.
Can you do it?
5 minutesto a draft.
30 minutesto something you’d act on.
Cost, all in£0
Skill neededchat-fluent
Who has to check ityou
What the alternative costsNo alternative price is provided in the supplied tool data.
If this goes wrong: you accept a false explanation for a KPI movement and make a poor commercial decision before checking the underlying figures and business context.
What to actually do
Use a tool built for this
The route this page recommends
Do it yourself
Second choiceA chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.
How to actually do it
- Open the spreadsheet, dashboard or report containing the KPI figures and copy the relevant periods, units, targets and source notes into a new working document.
- Write down what each KPI means, how it is calculated and which changes could affect it, such as pricing, marketing activity, staffing or reporting rules.
- Remove customer names, personal data, passwords and other information that the explanation does not need, then paste the cleaned table, definitions and business context into the prompt.
- Send the prompt to a chatbot or upload the same material to Julius for a table-based analysis and plain-English explanation.
- Compare every reported change and calculation with the original spreadsheet, and correct any mismatch in periods, units, formulas or missing values.
- Ask the model to rewrite the explanation after you supply corrections, then send the final version to a colleague who knows the business to challenge the proposed causes before using it for decisions.
Prompt
Explain the business KPIs in the data below for a non-specialist manager. Use only the figures and definitions I provide, and say when information is missing. First confirm the period, units and formula for each KPI. Then provide: 1) a plain-English explanation of what each KPI measures, 2) the change between periods with the calculation shown, 3) the largest positive and negative movements, 4) possible explanations clearly labelled as hypotheses rather than facts, 5) data-quality issues, unusual values or comparisons that are not valid, and 6) three practical questions or actions for the team. Do not invent benchmarks, targets, causes, dates or missing figures. Distinguish arithmetic findings from business interpretation. If the data is ambiguous, ask focused questions before drawing conclusions. Business context: [business model, team or department, and relevant changes] KPI definitions and targets: [paste definitions and targets, or write 'not provided'] Reporting periods: [state the periods and whether they are comparable] KPI data: [paste the table or upload the spreadsheet]
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
Hand it to a person
The distant thirdA person who owns the outcome does this end to end, worth it when the failure is dear.
What it gets wrong
- AI cannot know whether a KPI definition reflects how your business actually operates unless you provide that context.
- AI cannot establish the real cause of a movement from KPI data alone, so its causes remain hypotheses until checked against operational evidence.
- AI cannot tell whether a target is commercially sensible without your strategy, constraints and market knowledge.
- AI can miss a reporting change, duplicated record or broken data feed that is not visible in the material you provide.
Even on a YES, the friction has a name: judgement under ambiguity, context depth and stakes of error.
How we scored this
Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 2 |
| Verification | 1 |
| Liability | 2 |
| Effort delta | 2 |
| Total | 9 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT explain my business KPIs?
- Yes. Give it the KPI table, definitions, comparable periods and relevant business context, and it can explain movements in plain English and show the arithmetic. Check its calculations and treat any proposed causes as hypotheses.
- Can AI analyse KPI data in Excel?
- Yes. You can paste a cleaned table into a chatbot or use a spreadsheet analysis tool such as Julius, which is designed to analyse uploaded business data. Verify the imported figures, formulas, units and reporting periods against the original workbook.
- Can AI tell me why my KPIs changed?
- It can suggest possible explanations by comparing the figures with the context you provide, but it cannot prove the cause from KPI data alone. Check each explanation against sales records, operational events, customer feedback or other relevant evidence.
- Is it safe to use AI for business reporting?
- It is suitable for a first-pass explanation when you remove unnecessary private data and retain the source figures. Do not publish or act on the interpretation until you have checked the calculations, data quality and business assumptions yourself or with a knowledgeable colleague.
Nearby answers
Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.
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