Prompt habits
Give staff approved prompt patterns for the chosen workflow instead of broad “try AI” encouragement.
Legal AI implementation plan
Move from AI policy to a supervised rollout your law firm can operate.
A legal AI implementation plan should answer five operator questions: which workflow starts first, who owns it, what data the system may use, how staff are trained, and what proof is required before expansion. FirmOps turns that plan into an AI Concierge or managed-agent pilot with human review built in.
This page is implementation guidance, not legal advice. Ethics, privilege, confidentiality, and jurisdiction-specific obligations should be reviewed by the firm’s responsible attorneys.
Implementation roadmap
A four-phase plan for legal AI adoption.
Most AI implementation advice stops at policy, training, or tool selection. The useful plan ties all three to a named workflow owner and a weekly operating rhythm.
| Phase | Operator move | Owner | Decision to make |
|---|---|---|---|
| 1 | Choose the first workflow | Firm owner + operations lead | Pick one workflow with clear source material, repeatable output, and a human reviewer: intake summaries, records checklists, chronology drafts, or client-update drafts held for approval. |
| 2 | Set the data boundary | Operations lead + responsible attorney | Name which systems the assistant can read, which information cannot be pasted into public tools, and what source references must appear beside the output. |
| 3 | Train around real examples | Workflow owner | Teach staff with actual approved prompts, good and bad outputs, correction examples, and the rule that model work stays preparation until a human approves it. |
| 4 | Pilot before rollout | AI Concierge / managed-agent owner | Run a bounded pilot with a small staff group, collect corrections weekly, and only expand when review burden, source quality, and adoption are visible. |
Staff training plan
Train staff on the approved path, not AI in the abstract.
Data handling
Show exactly what may go into approved tools, what must stay inside firm systems, and when to stop and ask.
Review discipline
Train reviewers to check source references, missing facts, tone, and legal conclusions before any client-facing or record-changing step.
Escalation rules
Name the handoff when output conflicts with the file, staff cannot verify a source, or the tool appears to give legal judgment.
Pilot cadence
Use a weekly operating rhythm before expanding.
- Weekly pilot review: what staff used, what they ignored, what they corrected, and where the assistant lacked context
- Approval-gate review: which drafts or actions require attorney, manager, or intake-lead approval before anything leaves the preparation lane
- Source-quality review: whether summaries, chronologies, and drafts cite the right matter context and flag missing facts clearly
- Expansion decision: whether the workflow is stable enough to standardize, needs tuning, or should stay out of production
Measurement
Measure whether the workflow is ready, not whether the tool is exciting.
- Adoption: staff use the approved path instead of side tools or one-off prompts
- Review burden: reviewers can approve or correct output faster than drafting from scratch
- Source quality: output points to the file context used and flags missing information
- Risk control: no unsupervised client sends, filings, legal advice, or system-of-record changes
- Operating ownership: one person owns exceptions, tuning, training updates, and expansion decisions
Not a fit
An implementation plan should stop weak rollout patterns early.
- Rolling out a firmwide AI tool before the firm chooses one supervised workflow
- Training staff on generic AI tricks without a data boundary or approval rule
- Treating an AI policy as implementation when no approved workflow exists
- Measuring success by logins instead of reviewed output, source quality, and staff adoption
Next reading
Connect implementation, policy, and the first supervised pilot.
Legal AI Adoption
The pillar page for safe legal AI rollout, human review, and supervised assistants.
Law firm AI policy
Write the acceptable-use, data-boundary, and approval-gate rules behind the plan.
AI Concierge
Start with a controlled front-door workflow staff can use instead of shadow AI.
Managed AI Agents
Operate repeatable AI workflows with monitoring, tuning, and approval boundaries.
Fractional COO support
Add operator-led implementation help when ownership, cadence, or change management is the bottleneck.
AI build vs buy
Decide when to buy a tool, build around firm context, or run a hybrid pilot.
Next step
Bring one workflow and one concern. We will turn them into a safe implementation plan.
The demo shows how FirmOps connects policy, data boundaries, staff training, AI Concierge implementation, and approval gates before broader rollout.