This article explores the benefits of using AI to efficiently manage a contract repository. It covers how human interaction enhances AI’s power to accurately get the job done faster. It explores practical use cases that demonstrate how you can use AI to streamline related processes to provide a sharper understanding of contractual obligations tied to supplier-specific relationship management. Within that, two questions arise with most users:
- What are the basics of AI and the related challenges?
- How, exactly, does AI revolutionize contract repositories?
LET’S FIND OUT!
Managing contract repositories is a critical function for businesses of all sizes, and the advent of Artificial Intelligence (AI) is revolutionizing contract repository management within Contract Management Systems (CMS).1
But old challenges remain. Managing contract repositories has always been a time-consuming and error-prone task. The sheer volume and complexity of contracts require a sophisticated approach especially when using AI capabilities to streamline contract repositories. That much we know.
In the past CMS provided a huge improvement in managing a contract repository, but today, with the advent of AI, the traditional CMS system now has compatibility issues with AI and can become very confusing. For example, only slight variations in a contract party’s name can reappear or reinvent itself or be misinterpreted as a separate supplier for vendor management. Moreover, a CMS system can also become very unpredictable for accurately tracking contract terms, collating amendments, and ensuring contract terms are applied consistently across related contracts.
A bit of a conundrum, yes?
WHAT TO DO? LEARN THE BASICS, GET THE BENEFITS OF AI
The basics of AI, rooted in the emulation of human intelligence within computer systems, enable AI to process contract-related data with efficiency and precision, making AI an indispensable tool in modern contract repository management.
AI can simulate human intelligence in machines that are programmed to think and learn like humans. This technology can process vast amounts of data, identify patterns, and make predictions based on that data.
AI encompasses a wide range of technologies and techniques that enable machines to mimic cognitive functions typically associated with human beings. Such cognitive functions include tasks like reasoning, problem-solving, learning, language understanding, and perception. AI systems are designed to process information, draw inferences, and adapt to new data – similarly to how humans learn facts and make decisions.2
One key strength of AI systems is its ability to handle enormous volumes of data efficiently and analyze text and vast datasets much faster and more accurately than humans.3 This capability is particularly relevant in the context of contract management, because it allows AI to handle the large volumes of textual and structured data found in contracts. And once ingesting and comprehending this data, AI can identify patterns, extract critical information, and derive insights that are invaluable for improving contract repository management.
AI can scan through contracts to identify specific clauses, keywords, or data points. This capability is vital for automating processes like categorization, indexing, and data retrieval. AI can also make predictions based on historical data, offering valuable insights into contract performance and potential risks.
How AI’s learning capabilities lean into contract management
Training databases - AI systems can be trained on historical contract data (databases) which is a crucial step in enhancing contract automation processes. By ingesting and analyzing a vast corpus of past contracts, AI systems can learn to recognize the patterns in the language used, the structural elements of contracts, and the prevalence of common clauses. This learning process equips AI with the knowledge and understanding required for efficient contract management.
When AI systems are trained on historical contract data, they develop the ability to identify and extract vital information. They can recognize key terms, clauses, and concepts within contracts. This is invaluable for automating tasks like categorization and indexing. An AI system can efficiently sort contracts into predefined categories and tag them with relevant labels based on the insights gained during AI’s training process.
The significance of training databases lies in their capacity to save time and reduce the margin of error. AI can process large volumes of contracts much faster than humans can, and its ability to consistently apply predefined criteria ensures accuracy and consistency in categorizing and indexing. This streamlines the contract management process, making it more efficient and reliable.
Live data searches - AI's contribution to contract management doesn't end with the initial training phase; it extends into real-time contract data utilization. AI enables real-time search and retrieval of contract information, revolutionizing the way organizations access and use their contractual data. This functionality drastically reduces the time and effort previously spent searching for specific clauses or terms within contracts.
When using AI-powered live data search, users can simply input their search queries, and the AI system will swiftly scan through a multitude of contracts to retrieve the relevant information. This instantaneous access to contract data is a game-changer for decision-making processes. It allows quick
responses to queries, accelerates due diligence tasks, and provides valuable insights during negotiations.
The benefits of live data searches go beyond just speed. AI also enhances the accuracy of search results by considering context and relevance. This ensures that users receive not only fast but also highly precise information. The ability to access contract data in real-time empowers organizations to make informed decisions swiftly to reduce delays. This enables them to seize opportunities in a dynamic business environment.
Training databases and real-time data searches are pivotal components of AI-driven contract management. The training of AI on historical contract data equips AI with the knowledge to categorize and index contracts accurately.4 Meanwhile, the real-time data search capabilities of AI transform the way organizations access, search, and retrieve contract information. This leads to increased efficiency and more informed decision making.
Human and AI interaction can manage a contract repository using excellent data hygiene
While AI can automate many aspects of CMS repository management, it is essential to maintain good data hygiene. Human oversight is crucial for managing AI systems to ensure that the information fed into these systems is accurate and current.
Managing the data - In today's fast-paced business world, organizations deal with an ever-increasing number of contracts. Managing and categorizing these contracts efficiently is a critical task that can be time-consuming and error prone. This is where AI offers a valuable solution for initially tagging and categorizing contracts. AI can significantly streamline this process, but it's important to recognize that it's not a fully autonomous endeavor.
But remember again: human oversight remains essential to fine-tune categorizations and ensure that contracts are labeled correctly.
AI-based contract management systems use predefined criteria to tag and categorize contracts. These criteria can include keywords, clauses, dates, parties involved, and contract types. AI algorithms can swiftly scan through vast volumes of contracts, identify relevant information, and assign appropriate tags and categories. This automation accelerates the sorting process, reducing the burden on human resources.
AI plays a significant role in the initial tagging, categorization, and consolidation of contracts, offering efficiency and productivity gains. Nevertheless, human oversight and expertise remain essential throughout these processes to ensure accurate categorizations and strategic decision-making. The collaboration between AI and human professionals in contract management maximizes the benefits of both automation and human judgment, leading to more effective contract management and better business outcomes.
Fine-tuning categorizations often requires human intervention, because contracts can be complex and nuanced. A machine might miss contextual subtleties or industry-specific knowledge that a human expert can grasp. For example, an AI system might struggle to differentiate between similar-sounding terms or understand the significance of certain clauses. Human expertise is necessary to ensure that contracts are accurately categorized, which is crucial for future reference and decision making.5
Additionally, legal and regulatory landscapes are continually evolving. AI systems may not always be up to date with the latest legal changes or specific requirements. Human experts can keep contracts compliant with the latest regulations and contractual obligations to ensure that the categorization process remains aligned with legal standards.
Consolidation based on supplier, category, or business unit
Once contracts are tagged and categorized, organizations can benefit from AI in another important aspect of contract management: consolidation. AI can identify patterns and commonalities among contracts, making it easier to consolidate them based on various parameters, such as supplier, category, or business unit. This analytical capability streamlines contract management, making it easier to access and track contracts for specific suppliers tied to certain categories or business units.
AI's data analysis and pattern recognition skills enable organizations to make informed decisions about contract consolidation. This can lead to more efficient procurement, better supplier management, and improved risk management. For instance, when contracts with a particular supplier are consolidated, it becomes easier to negotiate better terms and conditions to ensure favorable terms for the organization.
However, although AI can provide valuable insights and suggestions for consolidation, the final decisions should still be made by human experts. AI can identify potential groupings, but AI may not be able to consider certain unique circumstances or specific business strategies. Human judgment is crucial in determining the most suitable contract consolidation approach, considering the organization's overall goals and objectives.
Consolidation and easy access to contract requirements
Understanding the obligations
Now, let's explore some practical use cases to illustrate the power of AI in contract repository management. Often contracts with suppliers can be overly complex with multiple agreements. A large supplier may offer numerous services and have separate master agreements for the various offerings. As examples, a non-disclosure agreement might exist under that master agreement or a master services agreement might exist, or numerous amendments to that master agreement, as well as several work orders or statements of work (SOWs).
So, this can all get complex very quickly. AI can parse through the various contractual documents and consolidate contract requirements from a multitude of documents. It can break down which terms are currently active and enforceable for a particular service offering. This simplifies the process of understanding the obligations of each party to reduce the risk of overlooking critical clauses.6
Tracking notices, terminations, expirations
Most pre-AI CMSs can track expirations and due dates. However, data entry discrepancies between the CMS and the date listed in the contract can occur, but AI can verify that the information entered into the contract management software matches what was on the executed document.
AI can also send automated alerts to ensure that important actions are taken on time. AI can also track incoming notices and terminations to ensure the contract owner and the legal and finance departments are involved in case any contract is terminated, amended, or otherwise modified.
SUPPLIER-SPECIFIC RELATIONSHIP MANAGEMENT
Finding overlaps and gaps in contracts
Often, a company will have several active master agreements with a single vendor. But this can confuse the scope of each individual contract and the services being contracted for under each agreement. AI can identify overlaps and gaps in contracts with the same supplier.
So, how can AI help resolve this issue?
- identify the scope of each contract and ensure that consistent negotiated terms are included in all contracts with the supplier;
- categorize and understand the scope of each agreement and find gaps where certain provisions are not covered in any contract; and/or
- help to find overlaps or duplication in contracts, then reveal where the contracts can be consolidated or one of the overlapping contracts can be terminated.
Such benefits are invaluable in streamlining contracts to eliminate redundancies and address any inconsistencies.
Finding supplier-specific negotiated terms
Many companies have standard terms that they accept in their contracts. However, certain key suppliers may have different negotiated terms in their contracts that deviate from those standard terms. AI can quickly identify these variations, allowing organizations to better understand and manage supplier relationships. The previously negotiated terms can be easily ported into new agreements with the same supplier. The terms of different contracts can also be compared against one another (or each other) to ensure the contract terms are consistent with the supplier, and the work order terms or the SOW terms do not contradict the terms of the master agreement -- unless the parties explicitly intend to do so.
Streamlining and consolidating amendments
Amendments to contracts are common, and AI can help manage these changes. Some contracts can involve dozens of amendments to them. So, it can become very cumbersome for a human to sift through all the amendments to figure out which terms in the contract are still in effect.7 AI can identify which contracts have been amended, and AI can provide a consolidated view of the current terms. All amendments can be consolidated into a single document with all the current active and enforceable terms clearly laid out. This provides greater clarity for contract management, obligation tracking, and negotiations.
Improving your order tracking
CMS repositories often contain information about orders and deliveries. AI can track these data points and provide insights into order history, delivery timelines, and any discrepancies. These insights can ensure that vendors are meeting the obligations that they agreed to fulfill during contract negotiations.
In conclusion, integrating AI into a CMS for contract repository management can have a transformative impact, because AI:
- enhances data processing;
- improves search capabilities;
- streamlines the categorization of contracts; and
- enables businesses to gain a deeper understanding of contract obligations to optimize supplier relationships and improve overall contract management.
As AI technology continues to evolve, it will play an even more prominent role in contract repository management, offering new opportunities for increased efficiency and risk mitigation. Businesses that embrace AI in this context are well-positioned to excel in an increasingly complex and competitive business environment.
1. CMS “…helps companies manage digital content…to create, edit, organize, and publish content. It acts as a single place to store content and provides automated processes for collaborative digital content management and creation using built-in (or designed) workflows.” Extracted from Oracle article titled, What is a Contract Management System (CMS)?
2. Artificial Intelligence, IBM article https://www.ibm.com/design/ai/basics/ai/. See also IBM Watson
3. Warwick, Kevin book titled, Artificial intelligence: the basics. Routledge, 2013.
4. Understanding basic principles of artificial intelligence: a practical guide for intensivists
5. AI in Legal, a guidebook for legal operations, Steven Choi,
6. How to choose a contract management solution, ContractPod AI Solutions 7. What is AI for Contract Management, Ironclad Journal,
ABOUT THE AUTHOR
Chris is an Illinois licensed attorney with a Juris Doctor from the University of Illinois Chicago John Marshall Law School. Chris works on complex IT SaaS, Software License, IT Services, and IT Infrastructure master services agreement agreements and amendments for one of Nexdigm's largest client's, a Fortune 50 healthcare company. He has been involved in Nexdigm's Technology Assessment Group and has a strong interest in implementing AI improvements that enhance contract negotiation and lifecycle management. Chris has over 12 years of legal experience in corporate contract negotiation, litigation, and management.
Nexdigm is an employee-owned, privately held, independent global organization that helps companies across geographies meet the needs of a dynamic business environment. Our focus on problem-solving, supported by our multifunctional expertise enables us to provide customized solutions for our clients. We provide integrated, digitally driven solutions encompassing Business and Professional Services that help companies navigate challenges across all stages of their life cycle. Through our direct operations in the USA, Poland, UAE, and India, we serve a diverse range of clients, spanning multinationals, listed companies, privately-owned companies, and family-owned businesses from over fifty countries. Our multidisciplinary teams serve a wide range of industries, with a specific focus on healthcare, food processing, and banking and financial services. Over the last decade, we have built and leveraged capabilities across key global markets to provide transnational support to numerous clients.