AI reliability · 2026-10-17
Can AI make mistakes when analysing emails?
Learn how AI can misread conversations, why source links matter and how to verify timelines, summaries and email evidence.
A believable answer can still be wrong
Picture an email thread in which a customer service agent says 'we hope to resolve this by Friday'. A summary that says 'the company guaranteed payment on Friday' sounds convincing but changes the meaning.
Language models are good at producing readable text, not at guaranteeing that every statement is supported by your evidence. They can misread dates, mix together people or fill gaps with an unsupported guess.
The difference between finding and proving
An email may show what a person wrote at a particular time. It does not automatically prove why they wrote it or what happened outside the correspondence.
A useful AI summary separates direct quotations and verifiable facts from interpretations. It should also admit when the relevant message has not been found.
Why links to the original matter
Suppose a report states: 'The second reminder was sent on 12 June.' A source link should open the actual message showing the date and recipients so you can check it.
Without source links, you are left trusting a polished paragraph. With them, you can examine the original material and correct the report if needed.
How reliability can be tested
Engineers test a system with examples whose correct answers are already known. For a timeline, they might measure correct event dates and whether every important claim links to a matching source. For classification, they can measure precision and recall.
One useful fictional test is a set of 20 emails containing five promises, ten ordinary updates and five unrelated messages. The system should identify the promises without treating routine updates as guarantees. Any reported accuracy should come from an actual reproducible test, not an illustration.
A safer way to use AI
Treat an AI-produced timeline as a draft for review. Check the original emails, dates, attachments and context before sharing it or using it to make a decision.
MailTrace is designed around source-linked evidence review, but users should still verify significant findings for themselves.