Skip to content

Litigation Associate

Track billable hours and manage billing

Enhances✓ Available Now

What You Do Today

Record time contemporaneously (or more realistically, reconstruct your day at night), write descriptions that comply with client billing guidelines

AI That Applies

AI captures time from your calendar, email, and document activity — generating entries with descriptions that comply with billing guidelines

Technologies

How It Works

For track billable hours and manage billing, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Time capture is passive; AI drafts entries from your digital footprint, you review and approve instead of trying to remember at 11pm

What Stays

Billing judgment — deciding what's billable, managing write-offs, and the reality that realization rate affects your compensation

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for track billable hours and manage billing, understand your current state.

Map your current process: Document how track billable hours and manage billing works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Billing judgment — deciding what's billable, managing write-offs, and the reality that realization rate affects your compensation. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Smokeball tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long track billable hours and manage billing takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your general counsel or managing partner

What data do we already have that could improve how we handle track billable hours and manage billing?

They set the firm's AI adoption posture

your legal technology manager

Who on our team has the deepest experience with track billable hours and manage billing, and what tools are they already using?

They manage the tools and can show you capabilities you don't know exist

a client who's adopted AI in their legal department

If we brought in AI tools for track billable hours and manage billing, what would we measure before and after to know it actually helped?

Their expectations for outside counsel are shifting

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.