AI Use Cases
Summarization
Condensing long material, such as support threads, reports and meeting notes, into a short version people will read. The main risk is what the summary leaves out.
Part of Cloud, Data & AI
Summarisation uses a language model to reduce long text, such as meeting notes, support threads, reports or contracts, to a shorter version that keeps what matters for the reader.
Where it helps
It helps where there is more to read than people have time for: support handovers, long email threads, regulatory documents and research material. It works best when the summary feeds a decision or a step in a workflow, for example a handover note or a triage queue.
The main risk is omission
Summaries can contain mistakes, but a missing detail is harder to notice. A fluent summary that drops the one clause that mattered gives no sign that anything is missing, and the reader cannot tell without going back to the source. This makes summaries useful for deciding what to read in full, and a poor substitute for reading contracts or regulatory filings.
Making summaries safer
- Keep a link to the source next to every summary.
- Define what a summary must keep for each document type, such as amounts, dates, obligations and parties, and check for those fields automatically where possible.
- Compare a sample of summaries against their sources on a regular schedule, instead of assuming quality holds.