AI conversations often begin with a product demonstration. The demo is impressive, the team imagines dozens of uses, and only later does someone ask what information entered the tool or who is accountable for the output.
Begin with a bounded job.
“Use AI in intake” is not a use case. “Summarize a completed intake questionnaire for attorney review, without making a legal assessment or sending anything to the client” is. A bounded job has an input, an output, a user, a decision, and a clear line the tool must not cross.
The narrower the first job, the easier it is to evaluate quality, protect information, train users, and understand whether the system actually saves time.
Map the judgment points.
Every workflow contains moments where context, professional responsibility, empathy, or legal judgment changes the answer. Mark those moments explicitly. AI may prepare information for the decision, but a named person should make and own it.
A practical workflow distinguishes among three kinds of steps:
- Mechanical: moving data, formatting, reminders, and status changes.
- Assistive: summarization, comparison, classification, and draft preparation.
- Judgment: legal conclusions, strategic advice, material client communication, and exception handling.
This separation helps the firm automate confidently without pretending every task has the same risk.
Set the information boundary before connecting the tool.
Ask what data the system receives, where it travels, how long it remains, who can access it, whether it is used to train models, and what appears in logs. Then apply the firm’s confidentiality, security, retention, and vendor-review requirements.
The safest architecture often sends less information, not more. A workflow may use structured fields, redaction, or retrieval from an approved knowledge source instead of placing an entire matter file into a general-purpose prompt.
Design review into the work.
“A human is in the loop” means little unless the human has enough context, time, and authority to challenge the output. Define what the reviewer checks, what evidence accompanies the draft, how corrections are captured, and when the system must stop and escalate.
Track the outcome too. Did the assisted step reduce cycle time? Did errors change? Were outputs accepted without revision? Did client communication improve? A responsible system is measurable, observable, and reversible.
Governance should make useful work easier.
Good governance is not a binder that says no. It gives the team approved paths: which tools may be used, which data is allowed, which uses require review, how incidents are reported, and who can approve a new workflow.
When those boundaries are clear, people are less likely to improvise in the shadows. The firm can move faster because the rules travel with the work.