Legal AI innovation, in plain language.
What the work involves
- 01
Capture how the work actually gets done
Map how a practice area really handles a task: the documents, decision points and judgment calls.
- 02
Turn it into workflows and playbooks
Step-by-step processes that say where AI helps and where a lawyer decides.
- 03
Choose and configure the tools
Legal AI platforms such as Harvey and Legora, plus frontier models such as ChatGPT, Claude and Copilot, configured for the task and the team's confidentiality requirements.
- 04
Build prompts and templates
Reusable prompts, templates and checklists for recurring tasks.
- 05
Test outputs against practitioner judgment
Experienced lawyers check AI output against what a good lawyer in that area would produce, and refine until it holds up.
- 06
Train the team
Hands-on training so lawyers know how to use the tools, when to trust them and how to verify.
- 07
Measure results
Track adoption, time saved, quality and risk, and adjust as tools change.
Why domain expertise decides whether it works
Each of these steps depends on knowing the practice. A litigator knows what a defensible privilege review looks like. A restructuring lawyer knows which provisions matter in a credit agreement. An M&A lawyer knows what diligence has to catch. Generalists can't make these models work for law firms; practitioners can.
The job titles behind the work
Firms are hiring for this work under titles like Legal Innovation Counsel, Practice Innovation Attorney, Director of Legal Innovation and legal engineer. In our view, much of it is project work: it changes as the tools change. Counselerate provides innovation lawyers with domain expertise for those projects, instead of a permanent innovation hire.