Transcript:
I think applied AI is incredibly interesting. There’s a huge amount of potential in every industry on the planet, but particularly the legal industry, where we perhaps haven’t used technology as much as we could have done in the past.
It’s not about replacing attorneys or replacing business services within the firms. It’s actually about augmenting them and helping them to alleviate some of the more mundane tasks that they’re doing on a day-to-day basis and actually give them time to focus on strategic value-adds for their clients.
I think the firms that are getting best prepared to leverage AI in the future are the ones that are actually focusing back on the basics and the foundations and understanding at the moment: How do we optimize our technology? And how do we really clean our data and get a good set of data that we can then put the AI on top of so it can be much more powerful?
So I think that’s the play that I see the most successful firms running at the moment, is really getting into understanding data, getting ready for whatever this AI may be, and how we realize we’re going to deploy it in the future.
The firms that are perhaps jumping ahead of the game, putting AI right now without looking at that foundational layer — I think you’re going to struggle a little bit, because it’s going to take the AI that a little bit longer to learn what it is the lawyers really need, what it is the clients really need, because the data that it’s learning from isn’t very clean, it’s not very cohesive.
With technology as a whole, you’ve always got to be incredibly careful around the confidentiality of the data and the way it’s used, the way it’s stored. And I think that potentially becomes significantly more important when you have AI looking at data across thousands of sources.
So you’ve really got to understand: What does your AI have access to? How is it processing that? How is it using that data, and how is it providing the end result?
So lots of really exciting potential for AI, but there’s going to be a lot of rigor that needs to go in place to make sure that we’re using that data properly.
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