Artificial intelligence is indeed the go-to technology for automating repetitive, time-consuming tasks, and contract redlining is no exception. AI-powered tools have been making an impact in every sphere of Contract Lifecycle Management (CLM), including redlining, remarkable support for legal professionals in seamlessly navigating through various phases of a contract, and ensuring adherence to defined terms and clauses. Whatever has been achieved in the digitization of the CLM process, the credit ultimately belongs to the legal tech companies. Your constant striving to build and deliver tools to perform various non-core legal tasks has made AI-powered solutions for contracts negotiation comprising redlining a reality today.

AI tools are not born ready

ThoughtRiver, a Contract Acceleration Platform (CAP) provider, has stated in an article published on Cambridge Network – AI delivers predictions based on its understanding of the world around it, and the training examples that it has been trained on. This statement implies that AI is designed to function as humans do, but its understandability or predictions are as good as the training. It strongly indicates the need to involve human expertise to help AI tools understand their expectations. In the case of redlining, the tools must be taught first on the content flow of contracts specific to an organization, and the integral clauses, terms, references, sections, price details, tax, and accounting information.

This means automating redlining requires significant data from similar contracts for the tool to clearly understand the key components that must be tracked or redlined in case of modifications. Take an NDA document, for example; it is a prerequisite to creating a training model for the tool to identify and understand confidentiality, governing law, indemnification clauses, etc. This will help the tool to compare the final output with a client’s contract playbook and ensure that every significant modification or update is captured for error-free review. So, the conclusion is that to achieve perfection and execute a fool-proof contract redlining each time, exhaustive AI training should be the cornerstone.

Managed AI – The best bet!

Yes, it is a fact that AI is built to outperform humans, but it can attain this capability only with the assistance of human intelligence. In the case of contract redlining AI tools, it needs lawyers’ support during the pre-training stage. They help AI in getting trained to understand concepts and boundaries within different types of contracts and rectify errors (or) validate AI redlined predictions as a part of quality assurance, facilitating them to improve their accuracy. With being taught to identify and understand the nuances of contract redlining, AI is primed to deliver right since its deployment. This enables huge time-saving and seamless onboarding to work towards business goals.

Optimizing AI with lawyers in the loop

Several legal tech experts have reiterated the significance of human collaboration for improving the efficiency and accuracy of AI. But not any expertise would do here. An expert with the right domain knowledge and proven industry experience is imperative to ensure AI is rightly trained. And, for contract redlining, it ought to be well-trained lawyers. Wondering why? Here are the top five benefits to substantiate this need.

  • Flawless precision: The primary objective of AI is to ensure accuracy. If it lacks this capability, then it becomes redundant. Lawyers’ involvement helps AI fill the knowledge gap to reach the intended capacity and quality, elevating its reliability for your clients to assign the task with utmost confidence.
  • Mitigate risk: The essence of managed AI is accuracy, and the discerning quality acquired with the assistance of lawyers allows no space for red flags during the entire process. Its efficacy to rigorously record every alteration made in a contract to sustain its integrity through the process is indeed a result of collaborative learning.
  • Tremendous time saving: A pre-trained AI is readily deployable. It can be integrated into an enterprise system to commence the redlining process without much ado. This means an in-house legal team can get started with the tool, right away!
  • Better ROI guaranteed: Time is an invaluable asset of lawyers and enabling them to utilize it for high-value tasks increments their contributions towards business goals. As your AI tool is equipped to precisely deal with contract redlining, as taught by lawyers, it streamlines the review process and expedites clearance from the legal team to avoid any delay in the signing of a deal.
  • Improve business operations: An AI platform is comprehensive enough to manage redlining on its own, with its algorithms fine-tuned through supervised learning to understand specific clauses and concepts. Such is a tool that enables swift redlining, highlights potential errors, and alerts the legal teams to initiate required actions. It helps speed up the entire process, get started with the operations on time, improve productivity, and enable the organization to achieve its goals. This eventually strengthens the system for sustaining business excellence, which is strategically important to enable organizational growth and success.

This is exactly how Cenza’s AI/ML training team, comprising lawyers and quality assurance experts with contract redlining/negotiation expertise has been supporting Contract Negotiation AI platform providers for over a decade now. We have worked with several legal AI companies, assisting them in training their tools specific to a client’s requirements. With speed, accuracy (exceeding the 99% threshold), and cost savings as the key priorities of the service, we have enabled our clients to be distinguished for their perfectly trained, readily deployable AI tools.

If you would like to know more about our legal AI/ML training services, do visit our website: Cenza. For more details,click here to connect with our experts.


About the Author: Jayashree Nair

Jayashree has managed various engagements at Cenza for clients across the world. She led scoping and solution development for more than 20 client engagements, including some complex contract management-related requirements for clients and works on a daily basis with Ironclad and their clients on contract migration projects. She has successfully transitioned many complex projects for a variety of clients in the managed legal, AI and ML training, and Contract management space. She has an overall 12 years of experience, including previous stints at Accenture and R.R. Donnelly and has a strong understanding of project management and contract management projects.