We need to look at the application of AI in the legal context. Rather than AI, I’ll use the term ”Machine Learning” (I’ll come back to this point shortly). Machine Learning is now being applied in the legal sector to speed up manual tasks, improve accuracy, and streamline and automate processes.
Large legal firms such as Dentons (with Nextlaw Labs) and Allen and Overy (with Fuse) are investing heavily in legal tech, companies focused on improving the legal sector through the latest advancements in Machine Learning. Thomson Reuters is doing the same with their Elite offerings (including their partners). They are investing in technology that does everything from document digestion, self-populating profiles to be used in expertise finding, automated work allocation, predictive data modeling, transaction mapping, and automated matter management. In that one sentence, I have listed several time-intensive processes that, thanks to Machine Learning, can be either automated or at the very least allow real-time, accurate on-demand information.
Some see the above as negative as it does reduce the need for a human to produce the work. However, the fact it’s called Machine Learning is important, as by it’s very nature it needs to learn. The reason why this is important is Machine Learning isn’t here to replace the human, it’s here to make us better in this context (and I am referring purely to the legal sector at this stage). Let’s look at work allocation. You could have an allocation lead spend hours collating information, review case history, and create a list of the right people. Or, you could allow the ”machine” to do all this for you. The lead will still be needed at the end to make the final decision (based on unique insight around the client), but they have saved significant time in the process. The same can be said for Machine Learning platforms that can ingest significant amounts of written word and find and notate the exact pieces of law or comments necessary. A person could do this at considerable cost/time and perhaps inaccuracy. But the final element still needs the lawyer to apply it in the right way, to the nuisances of the case.
The machine is simply allowing the human to be the best they can. Effectively, Machine Learning is allowing the lawyer to do more lawyering, be more strategic, think for longer, have all the information needed instantly, and apply their brain/tacit knowledge to the case.
This shift toward technology is affecting the sector, as firms are now able to streamline costs and act with more nimble operating cost models. There are certain jobs that associates or back office staff will no longer have to do. It opens up a significant opportunity to expand the knowledge base as law firms are gaining time and brainpower back. This can only be a good thing.
(Re-posted with permission from Thomson Reuters Elite)
About the author
Sebastian Haire is part of ProFinda’s network of thought leaders — an international technology company that helps organisations understand their people better. Connect with him on LinkedIn and Twitter.