In part 1 of this series, I explored the landscape of AI technology within legal; where we are now, why we are here, and where we are going next.
In this article, I want to focus on bringing the theoretical to life by exploring some common use cases within in-house legal teams to help you to think about how this can become a reality.
As with any other tech implementation or transformation project, the key to success is in identifying a genuine problem to solve and not just introducing AI technology for the sake of it. It’s also critical to ensure that you are using the right technology. There are lots of great tools out there but they are typically designed with specific use cases in mind. You wouldn’t use a power screwdriver to drill a hole and it’s no different when deploying technology.
The lawyer in me is screaming out to remind you that the following list is by no means exhaustive, it’s just intended to give you some ideas of where others are successfully using AI to solve their legal process challenges.
Contract Repositories / Contract Lifecycle Management / Contract Intelligence
An obvious hot topic at present. AI tools can be a very powerful solution for enabling a cost-effective (and quick) migration of legacy contracts into a new CLM; doing the heavy lifting in extracting key datapoints and obligations.
The contract management platform can be anything from a simple Sharepoint List through to a heavily-integrated CLM platform. Many CLM solutions offer AI capabilities but third-party products, including native solutions such as Microsoft’s Cognitive Search, are also available to support.
Due Diligence / Volume Document Review
One of the earliest and most common use cases for AI in legal is to assist with volume document review – extracting key datapoints such as parties and dates, as well as identifying key legal provisions such as assignment, termination for convenience and force majeure. As an example, SYKE recently helped to save a client 6,500 human hours in an automated review of 20,000 contracts.
Complying with macro regulatory / legal changes
Another popular use case is using AI to assist with the review of large volumes of contracts in respect of changing regulatory requirements to ensure compliance with the likes of GDPR, LIBOR and SCHREMS II. Maximum value can be delivered when integrating with other technologies such as document automation and logic tools, to provide an end-to-end remediation solution for re-papering.
Complying with internal business policy changes
Similar to the above, but one teams often don’t think about is responding to internal business policy changes. My team at SYKE recently supported a customer with the review of 5,000 contracts to help ascertain which contained downstream supplier Responsible Sourcing policy provisions.
Risk based contract review
Another hot area for AI is reviewing and risk scoring third-party contracts against a standard playbook. Again, many CLM products offer AI based components here, in addition to other specialist third-party products. One of the critical components for success here is being realistic about what the technology can achieve and investing a lot of time in the design phase of the project. A lot of value can be derived from a “first pass” review and auto-triaging to the right person to progress.
Reducing manual data input for internal processes
Data input sits at the heart of a lot of legal business processes and is both time consuming and prone to human error. We should always think more broadly than just contracts when thinking about opportunities for data extraction and AI. SYKE recently developed some custom models utilising Microsoft Cognitive Search to lift key datapoints from the very unstructured e-mails and letters exchanged in the course of IP litigation claims – not only saving time and effort but significantly changing human behaviour in terms of encouraging their engagement with a wider digital transformation project.
In summary, the key to successfully building, configuring and implementing solutions to solve any of the above use cases is:
- Diagnosing and articulating the problem to solve – what is the desired outcome?
- Analysing how that problem can be solved – how can the data deliver the required outcome?
- Exploring which technologies are designed for your use case or can be configured to deliver the required outcome.
- Engaging legal engineers who understand both the legal process and the technology (and its limitations).
- Defining a Minimum Viable Product for an initial release, and then iterating.
In the third and final part of this series, I’ll explore and dispel some of the common myths associated with AI in legal. If you’d like to explore these topics further, please get in touch on LinkedIn.
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.
