Businesses are increasingly leveraging Machine Learning (ML) and Natural Language Processing (NLP) technology to bring greater accuracy and efficiency to the contract review process. This includes tasks ranging from negotiating contracts and performing large-scale due diligence projects to conducting compliance work, tracking data and managing contract obligations across an organization, and identifying contract renewals or expirations well in advance of their deadlines.
The benefits of ML and NLP are vast. These technologies can perform up to 90 percent faster and with up to 60 percent greater accuracy than traditional manual reviews. While these metrics probably have you eager to get started, it is helpful to think strategically about procuring and implementing this technology to ensure the highest degree of success.
Steps Toward Contract Transformation
Because there are multiple software solutions in the contract analytics space to choose from, a helpful first step to finding the right tool is identifying your business needs and key requirements. One business may focus on tackling a significant post-merger contract migration project, which calls for a system that can analyze a variety of contract types and easily identify agreements with duplicative vendors. Conversely, another company may be looking to address their lease review process and therefore require robust exporting capabilities to generate abstracts and a feature to group leases together with their amendments to ensure a more streamlined review.
Based on these needs, it is important to understand the tasks that each solution can assist with and the areas where there may be gaps in functionality. For example, a tool might be able to parse a contract for standard legal terms but otherwise have a very limited ability to be custom trained by the user. For businesses with unique documents or specific use cases, this solution might not address the full scope of their contract review requirements.
Another key component to consider when choosing a contract analytics solution is the quality of support that the vendor is able to provide to their clients. Working with a provider who offers a support team with significant expertise, not only in utilizing the AI tool but also in the practice of law, is often crucial. Project managers with legal backgrounds have a deep understanding of the pain points of contract review and can provide tailored workflow suggestions that are specific to the user group. Additionally, finding a provider who offers around-the-clock support is important. This ensures that help is always available, even if attorneys are reviewing documents late into the night.
Once you understand the scope of what each potential vendor offers and how their solution aligns with your company’s business needs, you should be in a good position to select a tool. As you prepare for implementation, the following three-step process can be useful to ensure you maximize the benefits of the AI solution:
Adoption: First, you must determine where the new contract analytics system will sit in the business and how it will function within the current workflow. From an internal adoption perspective, this is important because you will want multiple business units to quickly see the value the solution brings to the organization. The logical starting point will be with the department that spearheaded the purchase of the technology (whether legal, procurement, IT, corporate development, etc.). Once this group establishes its use case, internal advocacy is the key to branching the technology into other applications for the broader organization.
User Training: During this step (and throughout the life of the software license), it is important to take advantage of the support resources that are available – whether training sessions, online guides or technical user support. Every system comes with a learning curve, and understanding the ins-and-outs of your new workflow at the start will make your AI-assisted process more successful in the long run.
We recommend that you begin by training a handful of key users rather than attempting a company-wide approach. This will help to ensure consistency and set expectations. And remember to follow these three guidelines:
- Be transparent about the benefits of the software and its limitations—attorneys need to understand that this system is designed to augment their work, not remove them from the process. It’s ultimately there to help them perform their jobs more accurately and efficiently.
- When measuring a solution’s impact on accuracy, don’t compare a human to a machine. Consider how the AI-assisted process compares to the traditional manual non-AI-assisted process.
- Two highly qualified attorneys may disagree on the output needed for a particular review. In these instances, it’s important to remember that the software is not always going to satisfy both of their expectations.
Customization: Once well-versed in using the tool, you can begin to customize the software to your needs. While many view this step as an ongoing process tied to their standard contract review workflow, having an organized plan in place prior to starting can ensure better results. Some questions to ask include:
- Which provisions or data points will you be training?
- How complex are those provisions?
- How varied are the documents you’re looking to analyze?
As you can imagine, training the AI to identify insights around leases will require fewer examples than training the system to identify a provision found in any document in a virtual data room. Maintaining consistency in the parameters of the data points you’re training is key, so establishing guidelines with your user group is a very important component of the process.
Finally, the transformation of your contract review process has begun, and the benefits are within your grasp. But remember, to get the most out of your investment, you must fight the urge to move too quickly. By aligning with experts and implementing a well-planned and executed rollout, you can achieve a review process that is faster and more accurate than ever before.
