Your firm's knowledge is the asset
A lot of the legal AI conversation starts with the model. Which one is more powerful? Should we build our own? Where should we keep the data?
To me, there is a more useful starting point. What does your firm know that a generic model does not?
Think of the lender conditions your team always checks. The wording that needs a second look. The steps you insist on before exchange, on completion and after completion. Much of this knowledge sits in people's heads. Some sits in precedents. Some is buried in old files.
Putting those documents into an AI tool is only the beginning. The harder work is turning that knowledge into rules the system can use: what to flag, what evidence to check and when to ask a lawyer.
This is where I believe a firm's long-term value will sit. That knowledge has been built through people asking questions, being corrected and learning from real files. Let the model do the heavy lifting, but keep that legal judgment at the centre. Review the exceptions. Improve the rules. Make the learning available to the next person handling a similar matter.
Put simply: the model is becoming a replaceable line item. The knowledge your people have built should not be.
Measure the work, not the AI
A quicker AI answer is not the same as a better operation. Measure the cost of reaching a checked result.
A plausible answer is not a checked file
A file can look complete while important points remain open. Check the evidence, then track what still needs action.
The trust layer has to do some work
Governance should control what happens before an output becomes an action, not merely describe the policy afterwards.