Last week, I was speaking at a conference in Singapore on the impact of artificial intelligence (AI) on the practice of law. On my panel were lawyers from four different continents and it was clear that we were all falling over ourselves to incorporate AI into our respective law firms.
Since every firm was already using AI for drafting (from simple memos and client communication to basic contract drafting), much of the discussion revolved around how to do that effectively: the prompts we were using and the workflows we'd designed. Others had invested in due diligence products, using AI to review documents so that it could quickly surface risks in merger and acquisition transactions that lawyers could then address during negotiations and contract drafting. We were all using AI for research, whether it was to quickly generate summaries of current positions in law or just identify issues that could apply given the particular facts of the case.
For the most part, this drive to incorporate AI into as many aspects of the business of law as possible comes from the pressure we all face from clients to improve the efficiency of our services. But at the same time, these very same clients impose constraints on the ways in which we can use AI, prohibiting us from training it on any information they give us and often going so far as to say that even the queries we ask of AI should not contain any sensitive client information.
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