Law firms should verify AI-generated time entries by comparing each field against reliable source evidence and actual lawyer knowledge.
An entry can look polished and still be wrong.
For example, AI may produce a professional narrative while:
Selecting the wrong matter
Overstating duration
Choosing the wrong billing status
Suggesting an incorrect task code
Combining unrelated work
Including unsupported details
Accuracy therefore needs to be tested field by field, not as one vague percentage.
What Does “Accurate” Mean?
A legal time entry can include:
Date
Timekeeper
Client
Matter
Duration
Billing status
Narrative
Task code
Activity code
Review status
Each field can have a different accuracy rate.
A product may be strong at matter matching and weaker at duration estimation.
That distinction matters.
Verify Against Source Evidence
The reviewer should be able to compare the draft entry with evidence such as:
Calendar event
Email
Document activity
Matter workspace
Call record
Browser activity
Timer
User note
Prior approved entry
The system should not require blind trust in the generated result.
Source visibility makes correction faster and improves accountability.
Matter Accuracy
Test:
One client with several matters
Several clients with similar names
Internal matters
Closed matters
New matters
Shared participants
Documents moved between workspaces
Measure:
Matter correction rate = Entries with corrected matter ÷ Entries reviewed
A high correction rate can indicate poor source data, weak matter mapping, or insufficient context.
Duration Accuracy
Duration is difficult because digital activity is not identical to focused work.
Test:
Long drafting sessions
Short emails
Interrupted work
Meetings
Timer activity
Overlapping records
Idle applications
Offline work
Measure:
Average duration change
Percentage of entries changed
Overlap corrections
Timer-overrun corrections
Do not treat a document's open time as automatic proof of billable duration.
Narrative Accuracy
Evaluate whether the narrative:
Describes actual work
Uses supported facts
Identifies the purpose
Avoids invented legal conclusions
Avoids excessive confidential detail
Follows client language rules
Avoids vague phrases
Avoids unnecessary block billing
Possible measures include:
Narrative edit rate
Major rewrite rate
Unsupported-detail rate
Client-guideline exception rate
A grammatically strong narrative can still be inaccurate.
Billing Status and Code Accuracy
Test whether AI correctly suggests:
Billable
Non-billable
No charge
Flat-fee/internal status
Task code
Activity code
Matter phase
Measure:
Billing-status correction rate
Task-code correction rate
Activity-code correction rate
Client-specific code sets should be tested separately.
Build a Ground-Truth Test Set
Before deployment, create a sample of known workdays.
Include:
Several lawyers
Several practice areas
Short tasks
Long tasks
Several matters
Internal activity
Personal activity
Flat-fee work
Client-specific guidelines
Offline work
Overlaps
Duplicate signals
Have knowledgeable reviewers establish the expected result.
Then compare AI suggestions against that baseline.
NIST guidance for generative AI recommends evaluating accuracy, quality, reliability, and authenticity against known ground truth where appropriate.
Separate Automation Accuracy from Review Accuracy
There are at least two questions:
How accurate was the AI suggestion?
How accurate was the final approved entry?
A system can have imperfect suggestions and still produce strong final data when the review process is effective.
Conversely, a strong model can still produce bad billing data if users approve entries without reading them.
Measure both layers.
Sampling and Ongoing Audit
After launch, periodically sample:
Random entries
High-value matters
Low-confidence entries
Entries approved without edits
Entries with long duration
Overlapping activity
New users
New practice areas
Client-rule exceptions
The audit should record:
Error
Cause
Severity
Corrective action
Owner
Follow-up
False Positives and False Negatives
For validation alerts:
False positive
The system flags an entry that is actually correct.
False negative
The system fails to flag an incorrect entry.
Too many false positives create alert fatigue.
False negatives allow errors to pass.
Measure both when evaluating rule and AI performance.
Human Approval Is a Control
ABA guidance on AI and legal billing emphasizes that lawyers remain responsible for supervising AI billing outputs.
A useful review interface should allow the lawyer to:
See source evidence
Change matter
Change duration
Change billing status
Rewrite narrative
Correct codes
Split or merge entries
Exclude activity
Approve explicitly
Human review should be meaningful, not a checkbox.
Frequently Asked Questions
What is the most important AI timekeeping accuracy metric?
There is no single metric. Matter, duration, billing status, narrative, and billing-code accuracy should be measured separately.
Should firms trust entries that need no edits?
They should still use sampling and audit because users can approve an incorrect entry without changing it.
How large should a pilot test set be?
It should be large and varied enough to represent the firm's real work patterns, practice areas, matter types, short tasks, long tasks, and exceptions.
Can AI accuracy decline after launch?
Yes. Model, integration, rule, matter-data, and client-guideline changes can affect performance.
How does MIRA support verification?
MIRA presents captured time and AI-assisted suggestions for user review before approved entries are released toward supported finance systems.
