When performing contract analysis manually, the lengthy chunks of text that outline a contract’s terms, conditions, provisions, responsibilities, and potential risks can be difficult to wade through and interpret accurately.
This is where artificial intelligence can make a difference. In fact, many businesses have already started leveraging AI contract management technology to perform contract analysis.
With capabilities like intelligent search, automatic data extraction, clause-level text recommendations, and more, AI has proved to streamline contract analysis saving hours, reducing the possibilities of human error and minimizing risk issues.
Challenges and limitations of manual contract analysis
Traditional contract analysis processes can be heavily detrimental to your contracting business. Here’s why:
#1 Lot of time invested in going through each line of the contract with attention to detail
Your in-house legal team can only correctly identify important clauses and their constituent parts (entities) after thoroughly analyzing the contract, page by page and section by section. Furthermore, the length and number of clauses, schedules, and exhibits in a single contract can vary. Now imagine going through a large volume of these complicated contracts. No wonder it takes more than 2 hours for lawyers to locate specific language in a contract.
#2 A risk of human error while extracting data
Contracts are lengthy and tedious, sometimes spanning hundreds of pages. They are generally heavy on legal lingo, may contain ambiguous language that is hard to interpret, refer to other documents, regulations, or statutes that need to be cross-checked, or even require translation if they are in another language. Performing all of these actions error-free can be a huge undertaking in contract analysis, not to mention, take up a lot of time and effort.
#3 Complexities of contracts make contract analysis difficult
Contracts can come with many complexities that make analysis significantly more difficult than it already is.
- Bespoke structures: A contract is typically written in free-form natural language. It may have a number of sections, subsections, paragraphs, and logos, in addition to a variety of clauses, elements, intricate tables, and other things. Analysing such varied details in the contract with the same level of efficiency can be difficult.
- Multiple parties: The parent company may sign contracts with various partners. For instance, there can be a number of vendors and related vendor contracts. This makes contract dealings and operations more difficult, resulting in more time being spent to analyze every aspect.
- Local and cultural differences: Contracts in different countries are usually written in their native language, which makes translation a necessary evil. Agreements, therefore, must be interpreted semantically, which makes it complicated for lawyers not proficient in the specific language, thereby, making contract analysis time-consuming and error-prone.
“Owing to our operations in multiple geographies, a lot of work our team gets is on the lines of contracting and compliance. We are also required to be extremely familiar with licensing requirements of the countries within which we plan to expand. Understanding licensing and applying for the same considering all compliance requirements is a major part of what we do.”
~ Juliette Thirsk, Head of Legal, Peach Payments
Streamlining Legal at a FinTech Startup
#4 Potential risks and compliance challenges
Contract analysis primarily focuses on identifying possible fute risks, that may cause your business to incur financial as well as other losses. Due to the dynamic nature of the legal landscape, contractual terms may also lose enforceability over a period of time. Manually analyzing whether your contract is adherent to all local and global legislations and compliance laws can, therefore, be a challenging task. If your contract is poorly organized or lacks a standardized structure, finding such amended clauses or important details manually can spell unnecessary risks in the future. Not to mention, organizations frequently find themselves in a stitch due to non-compliance with deadlines or other contractual terms, leading to them paying some hefty financial penalties.
Companies may use contract playbooks that break down contract terms, outline “fallback clauses” for negotiations, and state clearly when the company can “walk away” from a deal—to avoid such potential risks and compliance challenges.
“A properly prepared contract playbook allows the Legal and Sales teams to stay aligned and close contracts faster (or know when it’s time to walk). The contract playbook ties into the contract risk scoring document and the contract review committee. The most valuable feature of a contract playbook is that it explains “why” certain provisions exit and “why” they matter. This can help the Sales team be a better client of Legal as, hopefully, they will stop negotiating with Legal and start negotiating with the customer.”
~ Sterling Miller, CEO and Senior Counsel, Hilgers Graben PLLC
Ten Things: Minimizing Risk in Commercial Contracts
AI in contract analysis: How does it work?
Signing contracts is not the end of it; they must be constantly monitored and reviewed throughout their lifecycle, to ensure that the involved stakeholders are keeping up with their respective end of the bargain.
This can be done with contract analysis—the process of reviewing a contract and determining the various rights and obligations of the parties involved.
It is generally a tedious process; however, with the advent of artificial intelligence, it can be simplified, and the important provisions of any contract may be rapidly and precisely identified with the help of AI-powered contract analysis software. Let’s understand how it works.
Use cases of AI in contract analysis
- Predictive coding: This is the most common type of contract analysis performed using artificial intelligence. It uses algorithms to read and analyze documents before determining which are most pertinent to the current project
- Clustering: For easier review, comparable texts are grouped together by clustering algorithms. As it can assist in prioritizing which papers are more likely to be pertinent to the project at hand, this is frequently used in conjunction with predictive coding
- Concept search: Users can enter a list of concepts or keywords they want to look for in a set of documents while conducting this type of search. A list of all the documents that include those concepts will then be returned when the AI software has searched through all of the documents
- Topic modeling: Topic modeling algorithms assess a series of documents to find recurring themes or subjects. This knowledge can be used to train the AI tool to recognize particular key terms, clauses, and risks to future-proof your contract analysis processes
Common AI technologies for contract analysis
#1 Machine Learning (ML)
For training, ML algorithms need a labeled dataset. This dataset, which is relevant to contract analysis, includes contracts that have been manually examined and annotated with particular information such as key phrases, clauses, or risk levels.
Next, the algorithm extracts features that can be textual contract information or metadata. Once this is done, the ML algorithms begin the training to recognize the features extracted and correlate them to the labeled datasets. This helps them identify key terms, clauses, and obligations or assess contract risks.
The trained ML model is then evaluated for accuracy; if it performs unsatisfactorily, you can assume there is still room for optimization. ML models can be trained to incorporate feedback loops so they keep learning from their mistakes until you can effectively apply them to unseen contracts and they get the analysis done.
#2 Natural Language Processing (NLP)
NLP techniques are primarily helpful in enabling AI systems to understand better the vocabulary, grammar, and semantics of contract text. The AI can comprehend the structure and meaning of the contract's words and clauses thanks to tasks like part-of-speech tagging, sentence parsing, and semantic analysis.
One example of an NLP approach is named entity recognition (NER) technology that detects names of stakeholders, dates, transaction amounts, and other key information in contracts for organization or extraction. NLP also pinpoints common terms like those relating to termination, indemnity, secrecy, or dispute resolution. This facilitates the automated identification of significant contractual clauses.
SpotDraft uses a Smart Data Capture feature that quickly extracts metadata from uploaded contracts using AI, producing a succinct summary that justifies any relevant contract wording. In order to assist you in comprehending the reasoning behind the suggestions, it also provides thorough summaries with sequential descriptions and explanations.
AI for automating contract review and extraction
Conducting contract reviews and extracting key terms is critical to the success of your agreement. These two aspects of contract analysis can greatly benefit from AI technologies that streamline the process, lower the amount of manual labor required, and increase efficiency by automating evaluation and key-term extraction.
This, in turn, makes it easier for organizations to process enormous volumes of contracts, speed up analyses, and guarantee precision and consistency in identifying potential risk clauses.
AI systems can automatically parse contract documents into machine-readable text using optical character recognition (OCR) technology. Next, using machine learning (ML), algorithms analyze the data to identify structural elements and patterns, like headings, subheadings, blocks of repetitive text, and key clauses. Following this, natural language processing (NLP) techniques come into play that help the algorithms understand the language through entity recognition, semantic analysis, and syntactic parsing.
AI software can also be trained on labeled datasets to identify specific language or patterns related to important obligations, clauses, or legal requirements. This makes it possible to recognize and extract vital information automatically, leading to effective contract analysis for every individual contract.
SpotDraft has its own Microsoft Word plug-in called DraftMate AI which makes it significantly faster and easier to redline contracts. You may compare variations of a clause that have already been executed from your whole contract library and based on what you have already agreed upon, decide what modifications to make and what to negotiate.
Moreover, you may quickly search for different iterations of particular clauses and phrases using SpotDraft's AI review add-in by highlighting a clause and searching for related content, which can enable you to identify the contracts for which they were utilized readily.
Benefits of AI in contract analysis
Investing in AI technologies for your contracting business can be daunting, however, it is worth every penny and has many advantages for your in-house legal team.
#1 Improved efficiency and time-saving
Organizations can process a larger volume of contracts in much shorter time since AI can analyze contracts far faster than humans can. Implementing an effective ML program can take the brunt of data analysis tasks off the shoulders of your legal experts. AI technology can spot contract anomalies, unearth fraudulent activities, and highlight trends or correlations humans might overlook. It can also recommend ways to improve terms in high-value contracts to speed up the signing process, thus, leading to an overall increase in efficiency and productivity.
Reducing the efforts required by your in-house legal team to analyze contracts indirectly saves time and money. Furthermore, you can then dedicate time to more pertinent activities that move the needle toward business growth.
“Legal should be pushing businesses forward, not just receiving tasks. So, if a legal team doesn't equally view themselves as part of the business, actively making business decisions along with the marketing, product, and finance teams, then the symbiotic relationship between legal and business is lost. And I question really the value of legal over the long term of a company unless I can propel it differently than it's operating today.”
~ Megan Neidermeyer, Chief Legal Officer, Apollo.io
Aligning Legal's Limitless Potential to Business Goals
#2 Enhanced accuracy and reduced human errors
Automated contract analysis using AI algorithms is not subject to prior assumptions that could influence their results. The contract analysis is carried out objectively by using pre-established logic and standards, and without any biases, inconsistent judgments, and human mistakes that could occur during a manual contract review.
Moreover, AI software can perform repeated assessments on large volumes of data with the same level of precision. This ensures that all contract terms and clauses are standardized and have consistent formatting, which in turn, contributes to better readability and maintains a unified approach, while also warrantying compliance with local regulatory requirements.
#3 Scalability and handling large volumes of contracts
For contracting businesses on a journey of scaling through digital transformation, rule-based automation can be highly effective. AI-powered contract management and analysis can save your legal experts from hours of repetitive tasks and, instead, help them dedicate their time toward more developmental business activities.
AI systems can also quickly extract pertinent contract data to help create contract metadata or manage contract records. Big companies find such AI capabilities very beneficial to drive efficient operations continuously and scale their business to greater heights.
HCC Ltd., a large-scale construction company that handles numerous litigations in multiple jurisdictions, at any given point of time, attests to the need of an AI tool in increasing efficiency, and aims to reduce their current legal spend on settling litigations by 20% of the allocated budget.
“There are various judgements in courts that refer to our precedents. We have a good litigation database, and we want to leverage it better by implementing an AI-powered tool that would help us with relevant precedents from the cases we have previously settled and ultimately reduce the time we clock with external counsel.”
~ Sandeep Chowdhury, Group GC, HCC Ltd.
Transforming the Legal Function at a Large Enterprise
With SpotDraft, you can store, organize, and search your contracts in a single secure repository, visualize, export, and share key contract details with custom reporting capabilities, and bulk-send personalized contracts to multiple counterparties in minutes.
#4 Identification of key terms, clauses, and risks
AI technologies, like natural language processing (NLP) and machine learning (ML), are especially useful when it comes to recognizing and extracting key terms and clauses in a contract.
For starters, AI can parse text from headings, subheadings, and large sections to extricate relevant contract information and employ named entity recognition (NER) models to detect specific terms like names of contractual parties, dates, monetary numbers, and other specific details.
AI algorithms can be trained to identify and classify frequently used contract clauses and terms like termination, indemnification, confidentiality, or dispute resolution by examining trends, keywords, or structural components. ML models can be further trained to recognize contract text based on syntax, grammar, and context, thereby, further familiarizing the software with similar terms in unseen contracts.
Lastly, AI-powered contract management and analysis systems can be programmed with incorporated feedback loops, thus, ensuring that the accuracy of the identified key terms keeps improving.
Once the AI software can pick up on specific terms, you may implement its capabilities in evaluating terms for risk assessments. This will help your business speed up negotiations and shorten the sales cycle. You can set up particular triggers to automatically approve contracts that meet certain criteria, like transaction volumes, deadlines, pricing and payment terms, and more.
SpotDraft recently announced the Advanced Search feature, which can help you extract key information from your contracts, such as, proposals and quotes for a specific customer, contracts with a clause that needs modification, customers who received quotes with a particular product line item, the expiration date of a contract, and much more.
Best practices for utilizing AI in contract analysis
#1 Selecting the right AI tools and technologies for data privacy and safety
Choosing the right AI tools and technologies for contract analysis is the first step you may take in ensuring the successful enforcement of your contract throughout its lifecycle. If your organization is considering adopting AI tools, you must examine the security risks associated with processing large data volumes.
“For those organizations that are ingesting new tooling, you need to figure out where you are on the risk meter from the data use perspective.”
~ Ken Priore, Ex-Director of Privacy, Atlassian
Mastering the Intersection of Law, Technology, and Privacy
The ideal AI software for contract analysis must support bulk document ingestion, parse and categorize relevant information by analyzing recurrent patterns and structural elements, identify key terms, clauses, obligations, allow integration with other systems and tools, enable customization of contracts based on contract types, legal jurisdictions, organizational interests, and much more—all without raising concerns about data privacy or security.
They must also be adaptive and flexible to continuously learn from each activity and optimize time for the best results.
You can find all this and more in SpotDraft AI. With our purpose-driven software, you can:
- Convert your Word docs into contract templates in seconds with SpotDraft’s Microsoft Word plugin, DraftMate AI
- Check IP rights, control provisions, legal obligations, and regulatory requirements to turn hours of contract review and assessments into minutes
- Utilize large language models (LLMs) to detect 1000+ unique key pointers and other relevant contract text to identify data trends, extract valuable insights and facilitate informed decision-making
- Redline and revise contracts 7x faster with AI Clause Match and decrease turnaround time
#2 Training and validating AI models for accuracy and reliability
AI models are not perfect from the start; however, over time, you can optimize them to suit your unique business requirements just right. When ML algorithms collect a diverse representative set of labeled contracts, they annotate specific terms, key clauses, provisions and obligations, and even potential risks.
Next, the dataset is split into training, validation, and test sets. The AI algorithms practice with the training sets to learn how to recognize patterns and trends, grammar and syntax, structural elements, and more, and based on the performance, you can fine-tune the model. Once the model feels attuned to specific parameters, you can use the validation and test sets (that contain unlabeled contracts) to check for accuracy and recall.
Training and validating your AI contract analytics model for reliable results requires a lot of iterative improvement. You may classify the errors made during the testing phase, to further optimize performance and accuracy, and refine for edge cases using user feedback.
As contract language, legal requirements, or business demands change, you must keep the AI model current. You can do that through ongoing training and learning, to keep up with the dynamic landscape of contract management today. These are a few steps to follow:
- regularly evaluating its performance and outputs against validated contract samples
- implementing mechanisms for incorporating any new regulatory requirements, legal precedents, or industry-specific terminology into the AI system’s database
- employing quality assurance and error analysis techniques to make sure the results generated by the system match your desired outcomes and that you can learn about the areas you need to refine and optimize with more information
- maintaining version control and comprehensive documentation of every alteration, including updates and improvements made to the AI system
- engaging stakeholders, including your in-house legal team, C-level management, and end-users, in the monitoring and updating process
#3 Collaborating between legal and technology teams
Though contract management and analysis rely solely on legal counsels, contracts are collaborative by nature and involve multiple stakeholders, including sales, marketing, technology, and other teams in the company.
The most important of these associations is the collaboration between legal and technology teams. AI contract analysis software can automate contract evaluations for legal professionals, and technology teams can prove to be essential in customizing the software to ensure that it accurately identifies key provisions, and potential risks. Moreover, tech teams can be involved in training contract personnels on how to use the software to its fullest capabilities, and implement new updates for continuous improvement. Legal professionals, in turn, may ensure that the software complies with all necessary data privacy and security standards.
Overall, collaboration between legal and tech teams can help your contracting business become more productive, keep up with the changing times, and ensure accuracy in contract analyses in all stages of the contract lifecycle.
SpotDraft offers contract collaboration features like:
- Automated request management: It might be challenging to keep track of all contract requests, particularly when they come from several teams, directions, and channels. Lawyers can monitor ongoing projects with the help of an integrated request dashboard, giving high-impact or urgent contracts priority over others
- Contract repository for knowledge sharing: Lawyers need a centralized knowledge base with multi-access capabilities linked to the requisition dashboard, that acts as a single source of truth. This may reduce the time they spend looking for context, resulting in a shorter contract lifecycle and compliance with necessary regulatory requirements
- Inclusive editing to speed up contract review: Sharing successive drafts via email or messaging services may also result in losing important data. Using a contract editor can improve accountability, make the editing process more inclusive, and prevent working in silos by providing real-time document updates with an audit tool to trace changes made by any stakeholder
Optimize the efficiency of your contract analysis processes with SpotDraft AI!
Utilizing AI to analyze contracts is a no-brainer for modern legal teams looking to adapt to the changing contracting ecosystem. With the right AI tools and technologies, you can track contract data, locate agreements in seconds, extract valuable insights from them, and enhance your business value—while cutting back on hours of work and reducing financial spends on labor and other resources.
With SpotDraft AI, you can take a glimpse into the future of contract management and analysis. This intelligent software has multiple features, such as instant contract template creation, comprehensive due diligence conduction, smart data capture, and streamlined contract review and analysis.
Are you ready to enable your in-house legal team to seamlessly manage and analyze contracts within significantly less time and optimize their efficiency? Sign up for a personalized demo today or join the waitlist for early-bird access to SpotDraft AI.