Over the last few years, Artificial Intelligence became one of the hottest buzzwords in the legal industry. AI is not a seasonal fad but useful technology. It is gaining traction among an increasing number of law firms and in-house legal departments, especially in the UK, USA, and Australia.
On the other hand, in the CEE region, companies implementing Legal Tech solutions based on AI are still considered early adopters. During the last Legal Tech Polska Meetup, I had the privilege to share my personal experience with testing such tools in Bridgestone East Europe Legal Department.
This article summarizes this presentation and attempts to encourage more lawyers to join the legal industry's transformation by searching the market and implementing new technology in their work.
But before we start -
Let's be clear that the below views are only my own. They do not in any way represent positions of any company or organization (including those that I'm associated with).
Now, with disclaimers out of the way, let's dive in:-
Artificial Intelligence has many different practical applications in legal technology, such as chatbots, contract management, and e-discovery. Our research focuses on contract analysis, which probably amounts to the largest part of most lawyers' workload.
When defining your goals for implementing an AI-based contract review software, you need to consider if you will use it for pre-signing or post-signing analysis. That is the first thing you ought to do.
The typical use case of pre-signing software is reviewing incoming third-party contracts, especially procurement contracts, NDAs, data processing agreements, etc. Conversely, post-signing AI legal tech is useful in M&A due diligence and large volumes of legacy contract analysis.
The latter appeared especially helpful during the COVID crisis. Namely, when companies had to quickly identify force majeure clauses in their contract portfolio (and after the Schrems II judgment).
Many vendors do not clearly emphasize the differences between these two use cases. This implies their software might be suitable for both (hence, use your judgment).
The most useful functionalities of pre-signing software are detecting risky provisions, indicating missing clauses, and providing suggestions for alternative wording of contractual terms.
How does the software identify that?
The first step to AI legal tech implementation is to create a playbook. In brief, a playbook is a set of contractual standards and model clauses in your company.
Next, your AI legal tech vendor has to embed the playbook into the software. Some tools can even build a playbook more automatically, based on an uploaded set of legacy contracts.
Finally, using the playbook, Artificial Intelligence detects concepts in contracts, compares them with the playbook, and provides the user with automated analysis.
In the case of post-signing software, it typically provides users an overview of a large volume of documents regarding either existing or missing clauses. Users can quickly identify governing laws, termination periods, non-disclosure obligations, or currencies and amounts without needing to review hundreds of thousands of pages in documents manually. Such an overview usually comes with handy visual dashboards.
Other functionalities, which I found very useful during my tests of different products were:
Finding the right tool for contract analysis is not an easy task. Before any implementation, you should test a couple of solutions and run pilots together with other future users.
To lend you a hand here, I will share a bit of my personal experience. I will also recommend tools that seem to be useful in daily work.
In the case of pre-signing contract review tools, I would like to point out Legal Sifter for its high precision of the algorithm and user-friendly interface. Although this tool's provider is a US-based company, you can customize it to local legislation and your company's playbook. Legal Sifter understands and detects concepts in contracts, indicates missing clauses, and provides the user with help texts, which contains comments to contractual provisions and alternative wording of these clauses.
Legal Sifter is only an example, and there are other tools, such as Thought River, Summize, and Law Geex, which are suitable for pre-execution contract screening.
For post-execution contract analysis, it is worth to mention eBrevia and Luminance. These tools are mostly suitable when you need to review a large volume of documentation. Moreover, they are language agnostic tools capable of analyzing documents in different languages. However, with less frequently used languages, users may need to train new language models.
(e.g. by giving the software a set of examples from a couple of contracts)
A distinguishing feature of these tools is powerful dashboards, showing a visual overview of a contract portfolio (e.g., existing clauses, governing laws, deviations from standards, etc.).
After testing several products and participating in a few pilots, my conclusion is that contract analysis software brings significant time efficiency.
Namely, it ranges from approximately 30% (pre-screening) to 70% (due diligence) acceleration of the review of contracts versus manual review.
An additional advantage is that non-lawyers may use contract review tools in case of a review of low-value contracts. This kind of self-service may be useful to procurement specialists in companies (removing bottlenecks in the procurement process and enabling legal teams to focus on something more valuable than NDAs) and law firms' clients in a subscription model.
I also firmly believe that internal playbooks strengthen compliance with companies' standards in the organization. AI will not replace lawyers in the foreseeable future. However, the combination of legal expertise and technology may significantly increase lawyers' work quality.
(photo by h heyerlein on Unsplash)
Ivan Rasic holds the Transnational Trade Law and Finance LLM, a program by Universidad de Deusto (Bilbao, ES), Universiteit van Tilburg (Tilburg, NL), and Goethe Universität (Frankfurt, DE). After his work in law firms and inhouse, he started a legal tech company.
Nowadays, Ivan leads STP Informationstechnologie GmbH's Sofia RnD center with project/development management, culture, strategy, and special project initiatives.
Ivan is an Ambassador at European Legal Tech Association (ELTA). He closely follows and writes on future of law, legal tech, ALSPs, and new ways of delivering legal services.
Roman Koch is an attorney at law and an in-house counsel in Bridgestone EMIA, previously advising large manufacturing companies, banks, car dealers, and venture capital funds.
He is experienced in international Legal Tech projects, especially in the implementation of contract analysis AI-based software. Roman is focused on optimizing legal advisory and legal operations through digital transformation.
