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YES

As of 13 August 2026, AI can identify trends in your business data.

This still needs a person who signs their name to it.

Can you do it?

5 minutesto a draft.

30 minutesto something you’d act on.

Cost, all in£0/month

Skill neededchat-fluent

Who has to check ityou

What the alternative costsA manual spreadsheet analysis is the alternative; no price is specified in the available tool information.

If this goes wrong: you act on a false pattern or miss a real change, and the resulting business decision can waste money or damage performance.

What to actually do

  1. Hand it to a person

    The route this page recommends

    A person who owns the outcome does this end to end, worth it when the failure is dear.

  2. Use a tool built for this

    Second choice

  3. Do it yourself

    The distant third

    A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

    How to actually do it

    1. Open the original spreadsheet or export and make a copy that removes unnecessary personal data, customer identifiers and confidential fields.
    2. Check that the copy has clear column headings, consistent dates, meaningful categories and a defined measure such as sales, orders, costs or conversion rate.
    3. Add the business context, reporting period, important events and the decision you are considering to the prompt, then upload the copy to an AI data-analysis tool.
    4. Paste the prompt and ask the tool to analyse the full dataset rather than a screenshot or a few selected rows.
    5. Check each reported figure against the source spreadsheet by filtering the stated dates, categories and records and reproducing the calculation.
    6. Investigate flagged missing values, duplicated rows, changes in how figures were recorded and unusual events before accepting a trend.
    7. Ask a colleague who knows the business to challenge the proposed explanations, then record which findings are evidence and which are only hypotheses.
    8. Use only the verified findings in your report or decision, with the data period, limitations and follow-up checks written beside them.

    Prompt

    Analyse the attached business data and identify meaningful trends. First describe the columns, date range, number of records and any missing or duplicated values. Then report changes over time, notable differences between relevant products, regions, channels or customer groups, unusual spikes or drops, and any apparent seasonal pattern. Show the calculation or source rows behind every numerical claim. Separate observed patterns from possible explanations, do not claim that one factor caused another without evidence, and do not invent missing values or business context. Flag data-quality problems that could change the result. Finish with: 1) the three strongest findings, 2) the evidence for each, 3) what additional data would test the possible explanations, and 4) checks I should complete before using this analysis for a business decision. Use the business context below only where it is explicitly provided: [business context].

    Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.

What it gets wrong

  • AI cannot know whether a pattern is commercially important without your targets, constraints and knowledge of what changed in the business.
  • It can mistake a reporting change, missing data or a one-off event for a genuine trend.
  • It cannot establish causation from ordinary business data unless the data and method support that conclusion.
  • You still have to trace claims back to source rows and decide whether the evidence is strong enough for the decision.
  • It cannot take responsibility for a pricing, staffing, stock or budget decision based on its analysis.

Even on a YES, the friction has a name: judgement under ambiguity, context depth and verification cost.

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.

AxisScore (0–2)
Output2
Inputs2
Verification1
Liability1
Effort delta2
Total8 / 10

The methodology and its thresholds are published in full.

FAQ

Can ChatGPT analyse my business data?
Yes. Upload a clean spreadsheet and ask it to show the calculations, identify data-quality problems and separate observed patterns from possible explanations. Check every important figure against the original data before acting on it.
What business data can AI analyse?
It can analyse structured data such as dated sales, orders, costs, customers, products, regions and channels when the columns are clearly labelled. The result becomes unreliable when categories change, records are missing or the file does not explain what each measure means.
Can AI predict business trends?
AI can identify patterns that may continue and can produce forecasts when the data and method support them. A forecast is not proof of what will happen, so test its assumptions against seasonality, one-off events and current business conditions.
Is it safe to upload business data to AI?
Only upload data that the chosen service and your organisation allow you to share, and remove unnecessary personal or confidential information first. Check the service's data-handling terms and use an approved account where your workplace requires one.

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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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