What can AI realistically do in employment law?
General AI assistants such as Claude or ChatGPT are useful for first-pass work that a lawyer then checks. They are not a substitute for judgement or for reading the source documents. These are the tasks that tend to hold up in employment law.
| Task | What the assistant does | What you give it |
|---|---|---|
| Chronology | Orders events from a client narrative and documents into a dated timeline | The matter timeline and key dates |
| Document summary | Summarises a handbook, contract or policy and flags clauses to review | The text of the document, redacted if possible |
| Issue list | Suggests questions and topics to explore with the client | The matter summary and the client's stated goal |
| First-draft letter | Drafts a letter or internal memo for you to rewrite | Parties, facts agreed and the outcome sought |
| Interview prep | Turns the file into a checklist of gaps in the facts | Open actions and missing evidence |
What should you feed it from MatterOS?
An assistant is only as good as the record it is given. A structured matter page gives it the facts, dates, parties and open tasks in a consistent shape, so you are not pasting a messy email thread each time. From a MatterOS matter you would typically copy across:
- The matter summary: client role, employer, dates of employment and the stage of the dispute.
- Dated events recorded as actions or notes, such as warnings, meetings and the termination date.
- The evidence list, with a short description of each document and its status.
- Open actions and the deadlines you have already calculated from the applicable rules.
MatterOS is a Notion template, so this is a copy-and-paste or export step you control, not an automatic feed. Decide what leaves your workspace each time, and strip anything the task does not need.
Where does AI go wrong in employment law?
- Limitation periods and notice requirements vary widely by country and by claim type. Do not ask an assistant to tell you the deadline; calculate it from the rules and record it yourself.
- Statutes and case references can be wrong or out of date. Pull them from an authoritative source.
- Summaries can smooth over inconsistencies in a witness account, which is exactly what you need to see.
- Documents often contain third parties' personal data, such as colleagues named in complaints.
AI output can read fluently and still be wrong. Treat every citation, date, figure and quoted passage as unverified until you have checked it against the primary source yourself.
A simple workflow to start with
- 1
Open the matter
Record the facts and dates in the matter page as the client tells you.
- 2
Redact
Remove names and identifiers the task does not need.
- 3
Ask for a draft chronology
Give the assistant the timeline and ask it to flag gaps, not fill them.
- 4
Check against documents
Tick every event against a source document before relying on it.
Frequently asked questions
- Can I use ChatGPT or Claude for employment law?
- Many lawyers do, for drafting first versions, summaries and checklists. You remain responsible for the result, so check the tool's terms on confidentiality and data use before putting any client information into it, and verify everything it produces.
- Does MatterOS include AI?
- MatterOS includes an AI prompt pack that works with Notion AI, Claude, ChatGPT or another assistant. The template itself is a set of Notion databases; the assistant is whichever one you choose and are permitted to use.
- Is there professional guidance on lawyers using generative AI?
- Yes. The American Bar Association issued Formal Opinion 512 on generative AI in July 2024, and many other bars have published their own guidance. Rules differ by jurisdiction, so check the rules and guidance that apply to you.
- Should I paste a client's employment contract into an AI tool?
- Only after checking the tool's terms, considering client consent and redacting what you can. Often a clause-by-clause summary of the relevant sections is enough.
About the author
Raghav Handa trained as a lawyer, then spent years building legal technology, from enterprise legal products to custom systems for law firms.
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