Summary
- Lawyers record less than 40% of their workday as billable time, and searching for information across disconnected systems is the top time drain at US midsize law firms.
- Legal document management software stores, organizes, and secures documents. It rarely gathers, validates, or matches the data inside them across the firm's other systems.
- Automating that work means validating data at intake, orchestrating the full pipeline between systems and building analytics into the same flow, without replacing current platforms.
- TEAM International helped a UK-based group litigation firm process 153,072 claims with zero downtime and build data pipelines designed to cut information processing time almost in half.
- A practical starting point: map where manual data handling slows cases, automate validation first, then reinvest the freed capacity in analytics.
Most law firms already run legal document management software. Their lawyers and paralegals still spend hours gathering, validating, matching, and filtering data by hand, because that work happens between systems, where no single tool owns it. None of it requires legal judgment, and all of it competes with billable work.
For Legal Operations Directors, Managing Partners, and COOs, the cost stays hidden until case volume rises. That is when the firm finds its real limit on throughput: the manual data work every case has to pass through.
This article explains where that work comes from, why document management leaves it in place, and how one group litigation firm automated it with TEAM International.
Manual data work is the billable-hour drain most firms never measure
Lawyers bill a small share of their day, and a large part of the rest goes to moving and checking information across tools. The average lawyer recorded 39 percent of their workday as billable in 2025, up from 28 percent in 2016, according to Clio's Legal Trends Report.
Much of the remaining time is work software can already do. Clio's analysis for its 2024 Legal Trends Report found that up to 74 percent of hourly billable tasks could be automated with AI, with information gathering and data analysis among the clearest examples.
Fragmented tools multiply the hours spent gathering data
In Actionstep's 2026 US Report, 83 percent of firms said they use three or more tools to manage a single matter, and 34 percent use six or more. Searching for information across disconnected systems was the top time drain, cited by 40 percent of respondents.
Each tool holds its own version of the client, the matter, or the claim. Someone has to reconcile those versions, and in most firms that someone is a lawyer or a paralegal. Adding more law office software rarely helps, because each new tool becomes another place where the same data must be entered.
Legacy integrations quietly tax every case that touches them
The same report links tool fragmentation to duplicate records (35%), higher error potential (34%), and time lost switching between systems (34%). Legacy integrations add their own cost: connectors that break when one system updates, batch jobs that leave data hours out of date, and manual exports that fill the gaps.
At low volume, staff absorb that friction. At high volume, it sets a hard ceiling on how many cases the firm can open, process, and close.
Where legal teams lose time:
| Finding | Figure |
|---|---|
| Share of the workday lawyers record as billable (2025) | 39% |
| Hourly billable tasks that could be automated with AI | Up to 74% |
| Midsize firms that use three or more tools to manage a single matter | 83% |
| Firms that name searching across disconnected systems as their top time drain | 40% |
Why doesn't legal document management software solve the problem?
Legal document management software is built to store, version, secure and retrieve documents. The manual work that drains lawyers' time happens to the data inside those documents and in the systems around them: intake forms, CRMs, case management platforms, billing tools, and external verification sources.
A document management system (DMS) knows that a claim form exists. It usually doesn't check whether the claimant's details match the CRM record, flag a missing field before the case moves forward or push clean data into the next system. That is why firms that invest in document management software for law firms still see paralegals copying data from one screen to another.
| Legal document management software | Legal data automation | |
|---|---|---|
| Main job | Store, organize, version and secure documents | Move, validate and match data across systems |
| Scope | One document repository | Intake, CRM, case management, billing and external sources |
| Data quality | Metadata and naming rules | Validation and matching rules applied at intake |
| Output | Documents that are easy to find | Consistent records, ready for the next step in the case |
| Analytics | Document activity reports | Legal data analytics on cases, claims, and throughput |
The two work together. Legal document management remains the system of record for documents, and automation handles the flow of data between that repository and everything else the firm runs.
Automating legal data work starts at intake and runs end to end
Automating legal data work comes down to three moves: validating data where it enters, orchestrating the full pipeline between systems, and building analytics into the same flow. With the right intelligent automation solutions, firms can do all three on top of the platforms they already use.
Extract and validate data at the point of intake
An error costs the least where it starts. Validation rules at intake check required fields, formats, and duplicates before a record reaches any downstream system. The same layer can handle contract or claim data extraction, pulling structured fields from documents so nobody has to retype them.
Verification against external sources can also run here, through automated API (application programming interface) calls. Lawyers then receive records that have already been checked, rather than discovering gaps halfway through a case.
Orchestrate the full data pipeline
Automating a single step usually moves the bottleneck to the next one. Orchestration connects the whole sequence: intake, validation, matching, loading into the CRM or case platform and notifications back to the client. Queues absorb traffic peaks, so a spike in submissions doesn't overload the systems behind them.
This is the core of data engineering services: pipelines that are governed, monitored, and repeatable. The pattern isn't unique to law firms. Back-office teams in banking face the same issue, as Allica Bank's automation work shows.
Build analytics into the same pipeline
Once data flows clean and structured, it becomes usable for legal analytics. Firms can track case volume by stage, time to close, and recurring bottlenecks without launching a separate reporting project.
Law firm data analytics depends on the same validated records that automation produces. The investment that removes manual work also gives leadership a clearer view of how the firm runs.
How TEAM International automated legal data work for a group litigation firm
TEAM International's Digital Software Studio worked with a UK-based group litigation firm to replace manual data cleansing with automated pipelines, an approach designed to cut information processing time almost in half. The same platform processed 153,072 claims with zero downtime during a single campaign.
Results at a glance:
| Area | What TEAM built | Result |
|---|---|---|
| Claim intake at peak demand | AWS-based queuing system between the website and Salesforce | 153,072 claims processed with zero downtime, against roughly 100,000 expected |
| Throughput | Microservices that release submissions to Salesforce in a steady flow | 60 sign-ups per second and 300x throughput |
| Data processing | Automated pipelines built with Azure Data Factory and Data Vault 2.0 | Designed to cut information processing time almost in half |
| Litigation processes | Automated API calls, data pipelines, and external data verification | 30% automated, with work in progress toward 99% |
A litigation platform built for peak demand
The customer planned its largest claims campaign during an important campaign in 2022 and expected around 100,000 submissions through an online form. Performance testing showed the legacy system wouldn't handle that volume. TEAM engineers built an AWS-based queuing system of microservices between the website and the firm's Salesforce environment.
Instead of sending hundreds of requests at once, the queue passed submissions to Salesforce in a steady flow, with every record stored in the cloud so none were lost. The platform handled 60 sign-ups per second, improved throughput 300 times, and processed 153,072 claims with zero downtime.
From manual data cleansing to governed pipelines
The customer's next goal was the lawyers' own workflow. TEAM's data engineers and scientists began automating manual data analysis and processing using Azure Data Factory and the Data Vault 2.0 modeling method. The new modular architecture connected to external sources for data verification, enabling automation of API calls and data pipelines.
On top of that foundation, the team set up data cleansing that responds to each lawyer's specific requests and parameters. The approach is designed to cut information processing time almost in half and to remove the need to gather, validate, match, or filter data by hand. Adding, processing, and closing cases takes less lawyer effort, and the Salesforce integration receives claims data without manual handling.
30 percent of litigation processes have been automated, with work in progress toward a 99 percent benchmark. Most litigation management software doesn't deliver this out of the box, because the pipeline has to follow the firm's own case flow.
Where should legal ops teams start automating data work?
Start with one measurable problem: find where manual data handling slows cases down, automate validation first, then extend into full orchestration and analytics. This order delivers results early and keeps each step low risk.
Map where manual data handling slows case throughput
List every point where someone copies, checks, or reconciles data by hand, from intake to case closure. Measure the time each step takes per case and how often it causes rework. The step with the highest volume and the most rework is usually the best first candidate.
Automate validation before everything else
Validation pays back fastest, because every downstream system inherits its result. Clean data at intake reduces rework in the CRM, the case platform and billing at the same time. It also makes every later automation safer, since each new step starts from records the firm can trust.
Reinvest freed capacity in an analytics layer
Treat the hours you recover as capacity for growth. Part of it can go into a legal analytics view of case flow, throughput and time to close, so leadership sees the next bottleneck before it limits the firm. Firms moving from pilots to scaled automation can also learn from past technology cycles.
Put your lawyers' time back on legal work
If manual data work is limiting how many cases your firm can handle, TEAM International can help you map and automate it within the systems you already use. Let's talk.