How law firms are using AI-assisted LegalTech solutions: A conversation with Slaughter and May’s Knowledge and Innovation team
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How is your firm currently using AI?
The main AI tool we’re currently using is Luminance. The solution was originally created to help with contract reviews, particularly in relation to due diligence exercises during mergers and acquisitions (M&A). But, because Luminance has a host of additional functionalities – and because you can train it– we’re now using it in lots of different ways around Slaughter and May, including within the firm’s support functions to optimise a variety of business processes.
Besides Luminance, we’re also using other AI solutions on a more experimental basis, including as part of know-how projects and market analysis. The use of predictive analytics in litigation – effectively predicting a dispute’s outcome - is a technology that is at a very early stage of development, but it’s something we’re exploring.
What are the benefits of using AI-assisted software tools?
It’s not just about cutting down on costs, time saving is also important. The ability to save time might be very important to clients who are acting on time-sensitive transactions, or on matters of strategic importance. It also has an impact on our lawyers, who are able to spend more time doing more valuable and rewarding work.
Improving the working lives of our lawyers is a huge driver for introducing these types of tools.
There’s also an element of being able to do things that you previously couldn’t. For example, because AI-assisted LegalTech solutions allows us to review much larger datasets than previously, they enable us to become better at discovering “unknown unknowns” within datasets – those things we didn’t even know we should be looking for.
How did your firm’s use of AI-assisted technology come about?
It originally came about via a firm contact although we were at that time actively looking at other contract analysis tools. We could see the way that the legal market was developing, and the opportunity to explore Luminance’s technology early on was too good to pass up – it would allow us to help shape the way the tool was developed.
How are AI solutions deployed within your firm’s workflows? Can they just be plugged into existing workflows – or do those workflows first need to be reengineered?
It depends on the use case. But, in relation to M&A due diligence, Luminance fits quite nicely into existing workflows. The documents that Luminance helps to review are already stored within a virtual data room – the only extra step needed by using Luminance is to bring the documents into the solution itself. Our fee earners still review the documents identified by Luminance on screen, and they still produce the same type of report as before once they’ve used it.
All that’s really changed is the interface that is used to gathering the insights into a particular dataset, and the speed and ease of gathering those insights.
In your experience, do fee earners need a lot of training to use LegalTech tools?
For us, it’s quite important our people should be able to open LegalTech software tools and be able to work with them straight away. We get the impression that a lot of money is now being spent on ensuring LegalTech platforms are very intuitive to use, requiring minimal training.
For us, it’s important our people should be able to open LegalTech software tools and be able to work with them straight away
That’s not to say that our firm’s legal tech team doesn’t need additional skills, over and above the skills of regular fee earners. However, these additional skills are more focused on how to train the software, how to deploy it, and how to advise fee earners about when to use it. It’s useful to have experts available, who are able to answer the trickier questions regarding LegalTech usage.
If there was any AI solution that you’d like to deploy – but haven’t yet seen developed – what would it be?
I’d love to see a solution that offered automated process mapping – something that could review your matters files, emails etc, and work out what an average matter looked like in terms of the tasks performed.