Machine learning and artificial intelligence offer numerous advantages within contract lifecycle management, including speed and efficiency. Machine learning automation has streamlined many tasks within contract management, while AI has begun helping lawyers draft and negotiate agreements.
As contract automation software improves with new machine learning and AI capabilities, private fund managers and investment banks are right to look into it.
Here’s an overview of contract automation with a quick look at Ontra Synapse, Ontra’s purpose-built AI that powers contract automation features for private markets’ legal work.
What is contract automation?
It’s helpful to distinguish between AI, machine learning, and automation to understand contract automation. AI systems can carry out tasks that usually require human capabilities, like recognizing and generating speech. Machine learning is a subfield of AI that can learn from data and repetitive tasks to solve routine and complex problems. Automation is a general term used to describe when technology performs or supports tasks.
Contract automation encompasses machine learning and AI technology that can perform tasks within the contract management lifecycle. In many cases, contract automation reduces the number of manual tasks lawyers and other professionals have to perform during drafting, negotiation, execution, enforcement, and compliance.
Types of contract automation software
These contract automation tools or services target a narrow aspect of the contract lifecycle and tend to be very good at solving a particular problem. For example, certain contract automation solutions create contract drafts by pulling information from another source — a form, spreadsheet, or chatbot — eliminating the need for manual data entry. As a result, a routine or boilerplate agreement might be mostly complete before the parties finalize it.
Some contract automation software helps organizations with the entire contract lifecycle, from the first draft to expiration or renewal. These software solutions typically include centralized contract storage, robust search features, various automation features, real-time collaboration environments, improved visibility into documents, and electronic signature integrations.
How does contract automation software work?
Contract automation based on AI works by using algorithms to analyze data and identify patterns and relationships in that data. Developers train algorithms by using machine learning techniques, which involves providing the AI system with large amounts of data and adjusting the system’s parameters until it can accurately perform a given task on new data. Once trained, the AI model might offer suggestions, predictions, answers to questions, or another output.
Under the umbrella of AI, there are predictive and generative AI models. Predictive models make decisions or predictions about future outcomes by identifying patterns and trends in data and are generally considered to offer consistent, accurate results when trained on a large volume of relevant information.
Generative AI models can create unique text, images, audio, and synthetic data by mimicking content they previously analyzed. While generative AI has led to exciting results, it is limited by the fact that it often generates inaccurate, irrelevant, or inconsistent outputs. Due to these limitations, generative AI requires ongoing fact-checking.
What’s the history and evolution of machine learning in contracts?
Contract automation and CLM software started with storage. Central repositories, initially in a network and now in the cloud, significantly improved the contracting process by enabling businesses to better store, organize, and track their executed contracts and ongoing obligations.
Next came improvements to the search feature of those repositories. CLM solution vendors focused on making their search features faster and more accurate through natural language processing, optical character recognition, and other AI tools for contract management. Business people and lawyers could then find any document they needed without remembering the exact file.
In recent years, machine learning and AI models for contracts have progressed by leaps and bounds, offering businesses automation, generated text features, and much more. Check out the current contract automation landscape for the private markets.
Why is contract automation useful?
Within the contract lifecycle, AI and machine learning automation can:
- Generate contracts through autofill functions
- Recommend predefined contract terms or provisions
- Pull precedent from similar executed contracts
- Highlight ambiguous language
- Send automatic notifications during negotiations
- Notify users of pending and completed actions, contract renewals, and data-driven events
- Extract material information from executed contracts
- Timestamp user actions create an audit trail
Whether a contract automation tool is truly useful for private markets firms depends on the quality of the vendor’s AI models and training data set. To accurately address legal use cases for the private markets, an AI vendor must fine-tune its model to industry-specific legal use cases. The vendor also needs to train its AI models on a large quantity of industry-specific data.
Contract automation based on models built for generic use cases and trained on generic data, like Wikipedia, will yield inferior results than purpose-built models trained on data highly relevant to the intended use case.
What are the benefits of contract automation software?
Contract negotiations are time consuming when private fund managers and investment banks handle them internally. Routine contracts like NDAs often pull in-house legal and business professionals away from strategic work. These professionals spend hours a week negotiating routine agreements that could be handled more quickly and efficiently with contract automation tools.
By using contract automation, firms can:
- Reduce time spent on manual tasks
- Improve efficiency
- Accelerate negotiations
- Reduce contract turnaround time
- Improve accuracy and consistency of contract language
- Strengthen relationships between parties
- Enhance visibility throughout the contract lifecycle
- Improve contract management and enforcement
- Reduce reliance on internal resources or outside counsel
- Reduce spend on outside counsel
Ontra’s contract automation capabilities
Ontra Synapse is AI purpose-built for the private markets. Ontra combined proprietary AI models with industry-specific training data from over 800,000 anonymized industry negotiations to create various AI tools for private markets’ legal workflows. Our AI models provide outputs in Contract Automation and Insight when and where information can help our legal partners and customers the most.
What’s the future of contract automation?
As contract automation evolves, most businesses will take advantage of contract intelligence — a more advanced approach to CLM. Firms that aren’t already will begin pulling and taking advantage of data from their many agreements. Coupled with other business and financial information, structured contract data will streamline contract processes and improve deal outcomes.
Contract automation will likely evolve to the point where lawyers spend less and less time on routine and boilerplate agreements, while they focus their energy on complex and nuanced contracts. Legal AI might one day be able to compare the terms of parties’ contract playbooks and insert mutually agreed-upon provisions into the contract draft. The lawyers would then conduct a minimal review before the parties executed the agreement.
Another likelihood is smart legal contracts. As contracts are increasingly digitized, the parties will turn all or some of the contract provisions into code, automating aspects of contract performance. SLCs are years in the future, though, as it’ll take lawyers and business people time to translate contract provisions into machine-readable language.
Continue to explore legal AI
Ontra offers a wealth of content surrounding AI for the private markets. Explore our blog to learn more about the fundamentals and limitations of AI; how to evaluate AI vendors; and how to champion AI adoption inside a private markets firm.
This article was originally published on April 28, 2022.