AI in legal departments becomes useful when a defined workflow, accountable review and measurable outcome replace isolated experimentation. Start with a recurring task whose input and expected output are known. Set data and quality rules before the first live matter, compare the new process with a baseline and scale only the use cases that pass.
Choose a workflow, not a general promise
“Use AI” is not an operating model. A legal department needs to identify the work step that should change. Good candidates are frequent enough to test, structured enough to compare and important enough that improvement matters.
Examples include a first review of recurring agreements, comparison with an approved playbook, extraction of defined clauses from a document set, source-based research or a final consistency check. Open strategic advice may benefit from assistance, but it is harder to standardise and measure at the start.
Describe the chosen workflow in one sentence: input, task, output and responsible reviewer. If the sentence remains ambiguous, the pilot is not ready.
Map risk before selecting the technology
The same tool can present different risks in different workflows. Public legal sources, anonymised clauses and a repository of live contracts do not require the same controls. Classify the planned data and decide what may enter the system before product access is granted.
The assessment should cover confidentiality, personal data, commercially sensitive information, retention, access, deletion, subprocessors and incident handling. Use current contractual and technical evidence from the provider. Do not assume that a familiar brand, hosting region or enterprise label answers every question.
Define who can approve a new workflow and who can suspend it. Governance works best as a small number of clear gates connected to actual tasks, not as a policy document that users cannot translate into decisions.
Make human review specific
“A human remains in the loop” is too vague. State what must be checked, by whom and before which consequence. A clause suggestion, internal summary and external legal position have different thresholds.
For each workflow, specify:
the parts of the output that require original-source or document verification;
material errors that prevent use;
who can approve the result;
when a second reviewer is required;
how unresolved uncertainty is recorded.
The review step belongs inside the measured workflow. If quality control is treated as invisible overhead, the business case will overstate the benefit.
Build a baseline before the pilot
Choose completed tasks that represent the planned work. Record the current active time, waiting time where relevant, review effort, rework and error types. The baseline must use the same task boundaries as the AI-assisted version.
Do not measure only generation speed. Include preparation, uploading, prompting, source checking, correction, formatting and transfer into the final document. The detailed guide to measuring AI time savings provides a reusable structure.
A small pilot should be treated as directional evidence. Report individual results or a range rather than turning a few tasks into a universal efficiency percentage.
Design the pilot around decisions
A useful pilot should answer three questions:
Is the output usable at the agreed quality threshold?
Does the complete workflow reduce effort or improve another defined outcome?
Can the organisation operate it with acceptable governance and support?
Use a fixed task set and record failures, not only successes. Include at least one difficult example, one common example and one example expected to be unsuitable. This prevents the test from becoming a demonstration of hand-picked cases.
The operational 6-week Legal AI pilot playbook translates these questions into phases, owners and go/no-go criteria.
Measure adoption, quality and impact separately
These measures should not be collapsed into one dashboard.
Level | Example measure | Decision supported |
|---|---|---|
Access | approved users and available workflows | whether the pilot is reachable |
Use | recurring tasks started and completed | whether the process is being used |
Quality | material corrections, completeness, source support | whether outputs are usable |
Effort | total working and review time | whether the workflow changes capacity |
Outcome | cycle time, risk visibility, internal client experience | whether the process creates value |
High usage can coexist with weak quality. Low usage can reflect poor training, a rare task or a tool that does not fit. Interpret every metric together with the workflow and the review record.
Create a minimum operating model
Before expansion, document the essentials on one page:
approved tools and workflows;
permitted and prohibited data;
required review for each output type;
owner for access and provider changes;
channel for reporting problems;
cadence for reviewing evidence and controls.
Add a short working template for each use case. Users need examples of acceptable inputs, expected outputs and escalation rules. Training should use the team’s real workflow and show how to detect weak results, not only how to obtain a polished answer.
Connect AI work to existing systems
The legal team’s work continues in email, document management, contract systems and Microsoft Word. Every transfer can create lost context, duplicate files or missing sources. Map where the AI step begins and ends, which system remains authoritative and how the approved result returns to it.
Integrated document work can reduce copying and preserve context, but integration itself does not prove quality. The same review and access rules still apply. CASUS supports document review, legal research and related work in one environment; teams should test the specific path they intend to use.
Scale by workflow, not by licence count
After the pilot, decide separately for each use case. One may be ready for broader use, another may require narrower inputs and a third may stop. Record the decision, conditions and owner.
Expansion should add users together with training, support and monitoring. Licence allocation alone is not adoption. Review the workflow after material product changes, new data categories, repeated quality issues or a change in the organisation’s risk requirements.
For cooperation with external counsel, agree how AI-assisted work is documented and which source or review evidence is expected. The goal is not to demand a specific tool, but to maintain a clear quality and accountability chain.
What a credible result looks like
A credible pilot result is specific. It states the task, sample, baseline, quality threshold, review burden, observed variation and decision. It also says what was not measured.
For example, a team may find that a structured first pass is usable for one recurring agreement type, subject to a defined review, while open research questions remain too variable. This is more actionable than a claim that “AI makes Legal faster”.
Teams can test CASUS with a defined legal workflow. Use the same inputs, reviewers and measurement rules as the existing process.
Frequently asked questions
Where should a legal department start with AI?
Start with one recurring, bounded workflow whose input, expected output and review standard are already understood. Avoid a department-wide rollout as the first test.
Which metrics matter in a pilot?
Track use, quality, total effort and the intended operational outcome separately. A login or generated answer is not evidence of time savings or quality.
Who should own the workflow?
One legal owner should be accountable for the use case and review standard. Security, privacy, procurement and IT contribute according to the data and integration involved.
How long should a pilot run?
Long enough to include a representative set of real tasks and normal working conditions. A six-week structure provides a clear frame, but task volume and decision quality matter more than the calendar alone.
When is a workflow ready to scale?
When it meets the predefined quality threshold, the complete effort is understood, controls operate in practice and an owner accepts the operating responsibility.
