In this series of blogs, Jonny Badrock, Data Services Practice Director at SYKE, explores the impact of AI in legal tech and how it’s continued evolution can help to solve some of the complex challenges facing in-house legal teams in the years to come.
“AI in legal is dead!” Or so a commercial in-house lawyer claimed in a really insightful discussion I had a couple of weeks ago. Of course, I couldn’t disagree more, but that conversation, coupled with some conflicting messages in Gartner’s 2021 Hype Cycle Report, pushed me to reflect on the use of AI in legal, particularly for in-house teams.
There is a lot of ambiguity in a number of the categories in the Gartner report and I empathise with GCs and Legal Operations leaders trying to benchmark themselves against the market. When it comes to AI contract review and contract intelligence – where does that sit? Advanced Contract Analytics? Text Analytics? Contract Life Cycle Management? AI in Corporate Legal Practice? All of the above?
Here’s a summary of my view of the landscape…
WHERE ARE WE NOW?
Legal technology has come a long way in the last four to five years and we’ve seen some really interesting developments in the market – lots of investment, acquisitions and consolidations and sadly a few who didn’t survive the journey.
There are a huge range of use cases for AI in legal, and some people are simply lumping them all together and declaring them all a waste of time and money. Others declare AI as the saviour for all our problems.
In reality, we need to be mindful and realistic in terms of our goals and expectations. For example, take the relatively narrow area of contract review. Tasks range from large-scale extraction exercises through to the risk analysis and review of a single third-party contract against standard terms. The resource and time investment for humans to carry out those two exercises would vary greatly, and it’s no different for technology. The magic is in making sure you are using the right tool for the right job.
There are plenty of success stories: great point solutions, powerful AI functionality baked into CLM products and exceptional outcomes being achieved in ‘native’ technologies such as Microsoft Cognitive Search and Google Cloud AI. There are probably an equal number of failed projects and implementations.
WHY ARE WE HERE?
- Disconnect between vendors and buyers
There is often a lack of clarity and understanding around the ‘problem to solve’, leading to vendors over-promising and underdelivering with the end product.
. - A lack of understanding around the limitations
Linked to the above disconnect, but also fuelled by poor press releases and sales misrepresentations, there is a misperception around what AI can and can’t do and where its limitations lie.
. - Resourcing & expertise challenges for in-house teams
A lot of the early successful AI implementations have been within large law firms who have teams of legal engineers with the sole purpose of increasing efficiency and margin. Lean in-house legal operations teams do not have the budget to hire large teams of legal engineers, nor does it make sense for them to do so.
WHAT’S NEXT?
- Avoid the disconnect
Buyers need to clearly articulate their specific aims and objectives, and vendors need to be realistic about whether their product is designed to solve that problem. There probably is an AI solution that can (at least partially) solve the problem, but it’s important to find the right tech for the job and not to try and fit a square peg in a round hole.
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- Understand the limitations – but don’t let them put you off
In-house corporate teams are making great use of AI technology and incredible value can be delivered through good implementation. A recent due diligence exercise saw SYKE save a client 6,500 human hours and several months’ time in terms of project timescales. However, just as with human resource – its critical to understand what the limitations are. I’m going to analyse this in a bit more detail in part 3 of this series.
. - Overcome the resource challenge
The legal technology consultancy model works really well to fill this void – it allows in-house teams to bring in resource as and when they need it, leveraging expertise and insight from many other companies, without carrying permanent overhead where there isn’t an everyday requirement. I always encourage my clients to start small and iterate. In-house teams can even benefit from the power of AI without investing in technology by utilising legal technology partners to provide ‘AI Review as a Service’.
Although it’s part of my job, I really am optimistic about the future of AI in legal and if I was to create my own hype-curve, I would place AI more towards the beginning of the Slope of Enlightenment.
In part 2 of this series, I’ll explore in more detail some of the common use cases for AI to explain why I think we’re not far away from AI moving into a cost-effective mechanism for increasing productivity.
Jonny is an expert in delivering and leading legal innovation and automation projects, having previously led the innovation function at global law firm DWF. Jonny leads our data services practice and has a track record of helping clients to enable insight-driven decision making from their data.
Jonny has supported FTSE 100 companies with the implementation of AI technologies and has supported one of the largest global legal technology companies with the development of AI models for its market-leading platform. A lawyer by background, Jonny leads a team of highly skilled legal engineers operating in multiple jurisdictions.
