An AI time tracker uses activity records and contextual information to prepare suggestions about how work may be recorded.
For legal teams, those suggestions may include:
Client
Matter
Date
Duration
Billable status
Billing narrative
Task code
Activity code
Duplicate or unusual entry
The output should be a draft.
The lawyer or authorized reviewer should confirm the actual work, matter, duration, billing treatment, description, codes, client rules, and confidentiality before release.
What Is an AI Time Tracker?
An AI time tracker combines automatic activity capture with machine-assisted classification, drafting, and exception detection.
A traditional timer records elapsed time after the user starts and stops it.
An AI tracker may use records from:
Calendar
Email
Documents
Matter workspaces
Browser activity
Calls
Meetings
Timers
User notes
It then prepares a review queue rather than requiring every entry to begin from a blank form.
AI Tracker Workflow
A practical workflow is:
Approved data sources are connected.
Activity records are normalized.
Duplicate and overlapping records are identified.
Related activity is grouped.
Client and matter are suggested.
Duration is proposed.
Billing status is suggested.
A narrative is drafted.
Task and activity codes are suggested.
Rules and validation run.
A lawyer reviews the entry.
Approved time moves toward the finance system.
Each stage can introduce error.
The system should expose enough information for review.
Data Sources
Ask which sources the product supports:
Outlook Calendar
Email
Microsoft Teams
OneDrive
iManage Work
Browser
Calls
Practice-management software
Matter-management software
Timers
Also ask what the product processes:
Metadata
File names
Document titles
Document content
Email subjects
Email bodies
Participants
Browser URLs
Screenshots
Keystrokes
Application duration
Different data creates different privacy and accuracy risks.
Matter Classification
Matter suggestions may use:
Matter workspace
Document metadata
Email filing
Client name
Participants
Matter alias
Prior approved entries
User selection
Test difficult situations:
One client with several active matters
Internal conference covering several matters
A document copied between workspaces
A participant involved in several cases
Work with no digital matter identifier
The tracker should show uncertainty rather than forcing a confident answer.
Duration and Overlap
Duration may come from:
Calendar start and end
Timer interval
Call duration
Document activity
Activity grouping
User correction
The system should handle:
Idle applications
Interrupted work
Overlapping meetings
Timer and document duplication
Short tasks
Offline work
Work across several devices
An overlap is an exception for review, not automatic proof that time is improper.
Narratives and Billing Codes
AI may draft a description and suggest task or activity codes.
Example source activity:
Reviewed revised agreement
Focused on termination language
Sent comments to partner
Possible draft:
Reviewed revised agreement and analyzed termination provisions for partner comments.
The reviewer should confirm:
The purpose is supported
The matter is correct
The language does not invent legal conclusions
Confidential detail is limited
The task code reflects the workstream
The activity code reflects the action
Client-specific language rules are followed
Lawyer Review
The lawyer should be able to:
View source activity
Change the matter
Adjust duration
Change billing status
Rewrite the narrative
Correct codes
Split entries
Merge related work
Exclude personal activity
Save a draft
Approve or release
The review interface should be fast enough for daily use.
Approval should be an explicit action.
Finance and Billing-Team Review
Finance and billing teams may need to review:
Missing time
Unreleased drafts
Invalid matters
Unapproved timekeepers
Rate readiness
Missing codes
Client guideline exceptions
Submission lag
Integration failures
Finance should not be expected to reconstruct legal context that the lawyer did not review.
The AI tracker should improve billing readiness, not shift all correction work downstream.
Administrator and Governance Roles
Define responsibility for:
Connecting data sources
Managing users
Configuring rules
Managing client guidelines
Reviewing security
Reviewing privacy
Testing accuracy
Handling model changes
Resolving integration failures
Monitoring audit logs
Supporting users
Approving data retention
The matter owner and billing attorney should remain responsible for matter-specific judgment.
Privacy and Confidentiality
Evaluate:
Data collected
Content versus metadata
Model provider
Shared model training
Storage
Encryption
Retention
Deletion
Subprocessors
Data location
Administrator visibility
User pause and exclusion
Source permissions
Audit logs
Export on termination
ABA Formal Opinion 512 explains that existing professional duties continue to apply when lawyers use generative AI, including competence, confidentiality, supervision, and reasonable fees.
Accuracy Metrics
Measure by field:
Matter acceptance rate
Matter correction rate
Duration correction rate
Billing-status correction rate
Narrative edit rate
Task-code correction rate
Activity-code correction rate
Duplicate detection accuracy
Personal exclusion rate
Entries approved without change
Entries deleted
Review time per user
One overall accuracy percentage can hide important weaknesses.
Integration Requirements
Evaluate connections with:
Microsoft 365
Document-management system
Practice-management system
Matter-management system
Billing and finance system
Identity provider
Reporting platform
Ask:
Which fields move?
Is synchronization one-way or two-way?
How often does it run?
How are errors reported?
Is an API available?
Are stable IDs preserved?
Can approved entries be updated?
Is a complete audit trail available?
Pilot Plan
A useful pilot includes:
Several practice areas
Several lawyer roles
One client with strict guidelines
One client with several matters
Short email work
Long drafting work
Calendar meetings
Document review
Offline activity
Personal activity
Billing-code requirements
Measure:
Completeness
Corrections
Review time
User trust
Privacy concerns
Integration reliability
Prebill correction rate
Red Flags
Red flags include:
Captured activity is automatically billable
Users cannot view source activity
Personal activity cannot be excluded
The product hides duration logic
Matter confidence is not shown
AI drafts cannot be edited
Firm data trains shared models without clear controls
Screenshots or keystrokes are used without necessity
Human approval can be bypassed
Data cannot be exported
Vendor integration claims cannot be demonstrated
How MIRA Supports Legal Teams
MIRA is designed as a lawyer-controlled AI timekeeping solution.
It can:
Capture activity from supported systems such as OneDrive, Microsoft 365 Calendar, iManage Work, Chrome, and other integrations
Prepare billable and non-billable time suggestions
Draft billing descriptions
Suggest task and activity codes
Present captured time inside Microsoft Teams
Let users edit, merge, exclude, save, or release entries
Send approved entries toward supported finance systems
MIRA does not replace invoice generation, accounting, eBilling portals, or lawyer judgment.
Related Resources
Authoritative References
This guide is for educational purposes only. AI, billing, monitoring, privacy, employment, and professional-conduct requirements vary. Conduct legal, ethics, privacy, security, and operational review before deployment.
Frequently Asked Questions
What does an AI time tracker do?
It uses work activity and context to prepare draft suggestions for matter, duration, billing status, narrative, and billing codes.
Can an AI time tracker bill clients automatically?
It should not. Captured activity and generated entries should receive human review and approval before billing.
Is AI time tracking the same as employee monitoring?
Not necessarily. Products differ. Firms should examine whether the system uses work metadata, content, screenshots, keystrokes, or other monitoring methods.
Who should review AI-generated legal time entries?
The lawyer or authorized reviewer who understands the matter, work, client requirements, and billing treatment.
Is MIRA a full billing system?
No. MIRA focuses on AI-assisted time capture, preparation, review, and release toward supported finance systems.
