Home · Business · Customer Service · Complaints & escalations
As of 13 August 2026, AI can only partly create a monthly complaints report.
This still needs a person who signs their name to it.
Can you do it?
15 minutesto a draft.
1 hourto something you’d act on.
Cost, all in£0
Skill neededchat-fluent
Who has to check ita colleague
What the alternative costsThe supplied tool data gives no price for a human or software alternative.
If this goes wrong: your report presents incomplete or misclassified complaints as a trend, and your team makes the wrong service decision.
What to actually do
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.
Use a tool built for this
Second choiceDo it yourself
The distant thirdA chat interface, chat-fluent skill, and roughly 1 hour until you can act on the result.
How to actually do it
- Open your complaints or customer-service system and export all complaint records for the reporting period, including dates, category, channel, severity, status, response time, resolution time and a record reference where those fields exist.
- Remove names, contact details and unnecessary message content, then check that the export covers the whole period and that dates, categories and statuses use consistent formats.
- Write down your business definitions for a complaint, each category, each severity level, the response or resolution targets and the comparison period you want.
- Paste the definitions and the redacted export into the prompt, then ask the chatbot to produce the report without inventing missing values.
- Compare the report's total, category counts, percentages and date range against the original export, and investigate every mismatch in the source system.
- Open the underlying complaint records for the themes, escalations and unresolved cases cited in the report, checking that the anonymised examples and classifications are fair.
- Send the checked report to a colleague or manager for approval, with the data-quality limitations and any recommendations kept separate from confirmed findings.
Prompt
Create a monthly complaints report for [BUSINESS NAME] covering [MONTH AND YEAR]. Use only the complaints data and business definitions supplied below. Do not invent figures, causes, customer details, outcomes or trends. If a field is missing, say that it is missing. Keep personal data to the minimum needed and do not reproduce names, addresses, telephone numbers or full free-text messages. Business definitions: - Reporting period: [START DATE] to [END DATE] - What counts as a complaint: [DEFINITION] - Complaint categories: [CATEGORY LIST] - Severity levels: [SEVERITY DEFINITIONS] - Target response or resolution times: [TARGETS, IF USED] - Comparisons required: [PREVIOUS MONTH, SAME MONTH LAST YEAR, OR NONE] - Intended reader: [OWNER, MANAGER, BOARD OR TEAM] Source data: [PASTE A REDACTED CSV OR TABLE HERE] Produce the report in this order: 1. A short executive summary that separates confirmed findings from possible explanations. 2. The total number of complaints in the reporting period, with the calculation shown. 3. A table by category showing count and percentage of the total. Check that the counts add to the total and state how you handled uncategorised records. 4. A table by severity showing count and percentage, if severity is present. 5. A table of complaint channels, products or services, and locations only where those fields are present and usable. 6. Response and resolution performance against the supplied targets, without estimating missing times. 7. The main recurring themes, with the number of supporting records for each theme and two short anonymised examples where available. 8. Any month-on-month changes, clearly labelled as comparisons rather than causes. 9. Escalations, unresolved complaints and urgent risks found in the data, quoting record references rather than personal data. 10. Three practical actions, each linked to the evidence and labelled as a recommendation rather than a proven conclusion. 11. A data-quality section listing duplicates, missing fields, inconsistent categories, records outside the reporting period and any other limitation. Before finalising, recalculate every total and percentage from the supplied records, flag any ambiguity instead of resolving it silently, and finish with a short list of checks a manager must complete against the original complaints system.
Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.
What it gets wrong
- AI cannot retrieve a complete export from your complaints system unless you connect or provide the data.
- AI cannot know whether your categories, severity rules or reporting period reflect how your business actually operates.
- AI cannot reliably distinguish a genuine recurring cause from several differently worded complaints without human judgement.
- AI cannot take responsibility for service decisions made from a misleading, incomplete or misclassified report.
- AI cannot make the verification disappear: totals can be checked mechanically, but themes and recommended actions still need a colleague who understands the cases.
What caps this at PARTLY: private data access, judgement under ambiguity 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.
| Axis | Score (0–2) |
|---|---|
| Output | 2 |
| Inputs | 1 |
| Verification | 1 |
| Liability | 1 |
| Effort delta | 2 |
| Total | 7 / 10 |
The methodology and its thresholds are published in full.
FAQ
- Can ChatGPT analyse my complaints data?
- Yes, it can analyse a suitable redacted export and draft tables, themes and a summary. You still need to check the figures against the original system and approve the interpretation of ambiguous complaints.
- Can AI spot trends in customer complaints?
- It can identify repeated words, categories and patterns in the records you provide. It cannot prove why a trend happened, or detect a trend that is missing from an incomplete or inconsistent export.
- Is it safe to put customer complaints into an AI tool?
- Do not paste unnecessary personal data or identifiable free-text messages into a general chatbot. Redact names and contact details, follow your organisation's data-protection rules, and use an approved business setup where your policy requires one.
- Can AI automatically send my monthly complaints report?
- A configured workflow can prepare or distribute a report, but automation does not make its figures or conclusions correct. Keep a human approval step before sending it to managers, customers, regulators or anyone making operational decisions.
Nearby answers
- Can AI draft a final response to a customer complaint?PARTLY
- Can AI draft a response to a payment dispute?PARTLY
- Can AI respond to a negative Google review for my UK business?YES
- Can AI write an apology for a customer complaint?YES
- Can AI check a complaint response against UK consumer law?NO
- Can AI compare possible remedies under UK consumer law?PARTLY
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.
The newsletter
AI news, new answers and product picks, straight to your inbox.