AI Time Capture for Email and Documents: What ‘Good’ Actually Looks Like
Key design principles for AI-driven time capture that fee earners trust, focusing on email, documents and background work.
Time capture is one of the most painful points in most firms. Everyone agrees it matters, nobody enjoys doing it, and almost every partner suspects they are leaving money on the table.
AI-driven time capture promises salvation: “We’ll just read all your emails and documents and create the timesheets for you.” That sounds attractive – but also slightly terrifying if you are the COLP, COFA or managing partner.
This article looks at what “good” actually looks like for AI time capture focused on email and documents in a UK firm:
- what data it should use;
- how it should behave from a fee-earner’s point of view; and
- the controls you need so that you can trust the results.
1. Start from the fee-earner’s reality
A realistic time capture system should accept that:
- fee-earners work from their inbox and document editor, not from a separate timesheet window;
- much real work happens in unstructured bursts (thinking, reading, short calls); and
- people will always do some work that never makes it onto a timesheet.
Good AI time capture therefore:
- watches the work you already do – drafting, reviewing, sending emails, editing documents;
- proposes time entries automatically; and
- makes it easy to accept, edit or reject suggestions in seconds.
If the system requires more effort than manual entry, it will be quietly ignored.
2. Use sensible signals, not guesswork
For email and documents, useful signals include:
- time spent with a document open and actively edited;
- proportion of new vs pasted text;
- number and depth of review passes;
- length and complexity of emails;
- related activity (for example, drafting an email after editing a document).
A time capture engine can combine these to suggest:
- matter;
- duration; and
- a draft narrative.
“Bad” AI time capture simply counts windows and minutes and throws out arbitrary numbers. “Good” time capture focuses on meaningful work segments.
3. Keep humans in charge of narratives and matter allocation
AI can propose a narrative like:
“Review and amend draft witness statement and related correspondence.”
That’s helpful – but only the fee-earner knows:
- which issue they were focusing on;
- whether the work was for the Smith or Jones matter;
- whether it should be recorded as billable or non-billable.
A trustworthy system therefore:
- makes matter selection easy (for example, suggesting likely matters based on recent work or email headers);
- lets fee-earners edit narratives freely;
- never posts time without explicit confirmation (“click to accept”).
The role of AI is to remove the blank box, not to overrule the lawyer’s judgment.
4. Avoid turning every minute into a unit
Partners often worry that automated time capture will:
- flood the system with tiny entries;
- make files look padded; or
- capture “thinking time” that should have remained overhead.
Better systems:
- bundle related activity (for example, 6+ short emails in a chain) into a single suggested entry;
- allow firms to set sensible minimum thresholds;
- support clear labels for non-chargeable work (BD, know-how, supervision) so that partners can still see where time goes without over-billing clients.
The goal is better visibility and less leakage, not gaming bills.
5. Get data protection and confidentiality right
Because AI time capture touches email and documents, you must be comfortable with:
- where analysis happens (on-prem, UK/EU cloud, elsewhere);
- whether content is stored, and if so, in what form;
- whether vendors use your data to train general models.
A cautious approach includes:
- preferring systems that analyse content within your existing document/email environment or in a segregated tenant;
- ensuring that only metadata and derived summaries are stored long-term for time capture, not full message bodies;
- making sure there is a clear DPA and that the provider acts as a processor.
These are the same questions you would ask of any serious DMS or case management system; AI does not get a free pass.
6. Measure impact, not just novelty
To know whether AI time capture is worthwhile, track:
- reduction in unrecorded time (for example, by comparing before/after billing data);
- changes in write-off rates (are bills more defensible?);
- fee-earner satisfaction (“Does this save you time?” not “Do you like the idea of AI?”).
It may be sensible to run a pilot with a few teams and compare:
- hours recorded per matter type;
- partner confidence in narratives;
- admin time spent on timesheets.
Where OrdoLux fits
OrdoLux is being designed so that AI time capture:
- works directly from email and documents linked to matters;
- proposes draft entries with sensible narratives rather than raw “minutes”;
- lets fee-earners confirm and edit entries quickly; and
- gives partners and finance data that is detailed enough to be useful, but not so granular that it becomes noise.
That way, time capture becomes more accurate and less painful – without compromising ethics or client trust.
This article is general information for practitioners — not legal advice.
Looking for legal case management software?
OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined-up system. Learn more on the OrdoLux website.