Poor contract management corrodes your company’s bottom line. It has detrimental effects on a business due to the significant financial commitments involved. Mismanagement often results in missed deadlines and failure to comply with contract terms. These issues lead to financial penalties, legal disputes, and the potential loss of valuable business opportunities. In the summer of 2021, retail giant Amazon’s financial records revealed that officials in Luxembourg issued €746 million ($877 million) for breaches of the GDPR.
Businesses must leverage the power of AI to minimize errors and data discrepancies and manage the challenges of version control and security measures. Document AI is a branch of machine learning and artificial intelligence that optimizes contract management by automating the most tedious tasks and processes in the workflow. It performs data extraction, risk analysis, and compliance, and derives valuable insights from contract data.
Read along to learn more about document AI for contract management and how companies can achieve enhanced compliance with its correct implementation.
A significant portion of business activities are governed by contracts and agreements. Contract management is integral to businesses for the following reasons.
Contracts serve as legally binding agreements that outline the rights, responsibilities, and obligations of involved parties. By managing contracts effectively, businesses can ensure compliance with applicable laws and regulations, reducing the risk of legal disputes, penalties, and financial liabilities. Similarly, securing the right insurance through BizInsure can safeguard businesses against unforeseen risks, further enhancing the stability and compliance of their operations.
Effective contract management optimizes processes and ensures the efficient creation, review, approval, and execution of contracts. It enables organizations to streamline their operations, minimizing delays, bottlenecks, and errors in contract administration.
Contract management is crucial for businesses, as contracts often include financial aspects like pricing, payment schedules, and penalties. Effective contract management enables companies to optimize their financial outcomes by ensuring precise invoicing, on-time payments, and adherence to contractual obligations. Doing so helps reduce revenue leakage, minimize financial risks, and increase profitability.
By establishing transparent processes and documentation, businesses can facilitate smoother negotiations, better collaboration, and consensus-building. Consolidating contracts and agreements brings immediate benefits and strengthens your negotiating position for future endeavors.
Effective contract management monitors vendor performance, tracks service-level agreements, and oversees contract renewals or terminations. Doing so mitigates risks related to vendor non-compliance, subpar quality, or disruptions in the supply chain.
For example, a tech firm relying on third-party IT services uses contract management software to monitor SLA compliance, ensuring a reliable service infrastructure and minimizing downtime. Additionally, a company booking print-on-demand conference swag leverages a document management system to control timely delivery, payment compliance, and quality control, guaranteeing promotional items meet company standards.
The contract workflow encompasses taking a legal document from its initial creation to the final stage of signature and execution. It has the following stages:
This stage involves identifying the parties involved, clearly defining the scope and terms of an agreement, and incorporating any necessary legal and regulatory requirements.
For example, in the case of a loan agreement, the financial institution's legal team prepares the loan agreement document. The document includes sections on loan disbursement, interest calculations, default provisions, collateral details, and any other specific provisions relevant to the loan.
Once the initial draft is ready, the contract goes through a review and negotiation phase. Internal teams review it for accuracy, completeness, and compliance.
After the review and negotiation stage, the contract moves to the approval phase. Authorized individuals and legal stakeholders review and provide their approval or signatures to finalize the contract. This step ensures that all parties are legally bound by the terms and conditions outlined in the agreement.
Monitoring the contract throughout its lifecycle is crucial to ensure adherence to terms and obligations. It involves tracking and managing key dates, milestones, deliverables, and performance metrics to ensure that both parties fulfill their responsibilities.
Throughout the contract management process, data and insights from contracts are analyzed to identify trends, risks, and opportunities. Contract analytics and reporting provide valuable information for decision-making, contract optimization, and strategic planning.
Regular audits are conducted to ensure contract compliance and adherence to regulatory requirements.
In the concluding stage of the contract workflow, attention is given to the disposition of the contract at the end of its lifecycle. Favorable contracts eligible for renewal can be renewed, while others may be renegotiated based on changing circumstances. Agreements that have reached their natural expiration may be allowed to expire.
Traditional contract management comes with several challenges. Let’s explore the most significant ones.
Due to its reliance on manual document creation, review, and storage, traditional document management is error-prone and time-consuming. It results in inefficiencies, delays, and increased potential for errors. Paper-based processes can make contract management cumbersome, prone to loss or misplacement of documents, and challenging to search and retrieve information.
Reviews, approvals, and signatures happen across numerous spreadsheets, drives, and folders. Contract-related information is scattered across various departments or individuals, making it challenging to maintain a holistic view of contract obligations, performance, and risks. It results in missed deadlines, non-compliance, and difficulty tracking contract status and progress.
Moreover, traditional methods like email attachments or physical copies can lead to version conflicts, loss of visibility, and difficulty tracking changes or maintaining a centralized record of activities. These factors also lead to delays in contract turnaround time. Users may face challenges in locating the appropriate template, redlines exchanged with suppliers can become lost within email threads, or bottlenecks may arise during the approval process within the legal department.
Compliance with changing legal and regulatory requirements is a significant challenge in traditional contract management. Ensuring that contracts align with evolving laws and regulations requires continuous monitoring and updating. Manual processes struggle to keep pace with changing compliance requirements, resulting in increased risks and potential legal implications.
Contract documents encompass sensitive and confidential information that necessitates safeguarding against unauthorized access or disclosure. Ensuring document security, restricting access solely to authorized personnel, and upholding robust data privacy measures can be intricate and susceptible to human error.
Contract managers may also miss errors and inaccuracies that could indicate possible fraudulent activity.
The traditional approach to contract management often lacks comprehensive audit trails or logs that track activities related to contract documents. This poses difficulties in determining who accessed, modified, or shared contract documents, impeding the effectiveness of security monitoring and compliance auditing.
Manually monitoring contract expiration dates and managing renewal or amendment clauses is tedious and susceptible to errors. The high volume of contracts, various renewal timelines, and complex notification requirements make it challenging to track and manage contract renewals and amendments effectively.
Additionally, handling multiple versions of contract documents and accurately tracking changes becomes crucial during the renewal or amendment process. Without robust document management and version control mechanisms, organizations may encounter challenges such as version conflicts, errors, and difficulties in identifying the most recent contract terms.
Document AI, or intelligent document processing systems, utilize advanced algorithms, such as NLP, OCR, and ICR, to analyze and extract vital information from various documents, including contracts and legal texts. These technologies streamline contract management processes, improve accuracy, and provide valuable insights by automating contract creation, review, analysis, metadata extraction, search and retrieval, risk assessment, and analytics.
Let’s understand the benefits.
Performing manual tasks in contract management diverts attention from strategic and impactful business initiatives. Contract managers often spend significant time manually searching, organizing, extracting, and updating contract information and data.
However, with the advent of AI, manual tasks that involved redlining, approvals, contract review, and more are now obsolete, allowing contract managers to dedicate their time to more critical matters. Automation in contract management resolves tedious and costly manual data entry and information retrieval processes. It enables contract managers to access the necessary information within seconds, eliminating the need for hours or even days of manual effort.
Intelligent document processing tools analyze contracts and identify clauses that may pose risks to the involved parties. They detect clauses that exhibit ambiguous language and contradictions or deviate from industry norms.
For instance, when reviewing lease agreements, AI-powered platform analysis can identify clauses that may pose risks or deviate from industry standards. It can flag ambiguous language or contradictory provisions that could lead to potential disputes between the landlord and tenants. This early detection allows the real estate company's legal team to review and revise the clauses to ensure clarity, fairness, and compliance with legal and regulatory requirements.
Document AI expedites the contract lifecycle by automating tasks such as data extraction from contracts and generating notifications for critical milestones and renewals. This automation streamlines the process, eliminates bottlenecks, and guarantees that contracts are executed on time.
Moreover, its centralized platform enhances collaboration and communication among contract stakeholders. For instance, multiple parties involved in the closing process, such as legal teams, finance teams, and borrowers, access and review loan agreements simultaneously.
Document AI establishes robust audit trails and version control for contracts, meticulously recording and monitoring all contract-related activities such as creation, modification, and approval. It captures essential details like timestamps and user information, building a transparent and accountable record of changes and actions. Such accountability and transparency within contract management ensure clear visibility into the evolution of contracts over time.
Unlike traditional ways of storing documents in filing cabinets, spreadsheets, drives, and across disparate sources, document AI ensures secure storage by leveraging encryption and access controls. It safeguards sensitive contract data from unauthorized access, reducing the risk of data breaches or leaks.
Organizations can also achieve enhanced compliance with document AI. Its privacy features align with protection regulations like the CCPA and GDPR. The pre-trained algorithms manage and protect personally identifiable information (PII) within contracts, ensuring compliance with data privacy laws.
Clearly define your objectives for implementing document AI. Identify the specific use cases where document AI will be applied, such as contract review and analysis, renewal tracking, data extraction, and search and retrieval. It helps you plan for scalability and integration and train the ML models appropriately.
Evaluate the availability and state of your existing document data. Check if the documents are already in digital formats or require conversion. It helps ensure quality and potential limitations for training AI models.
When evaluating various document AI tools, consider accuracy, turnaround time, ease of use, integration, and, most importantly, straight-through-processing (STP) rates. A reasonable STP rate ranges from 90% to 95%, per industry standards.
Factor in your company's budget and requirements while weighing your options. Consider the business's size and scale and ensure that the software leads to a positive ROI.
Ask potential vendors about pricing tiers based on features and scalability. You also need to determine whether there are additional training and technical support charges.
It is mandatory to assess the data's cleanliness, organization, and relevance to avoid errors and strengthen the model's accuracy. Cleanse and organize the data, ensuring consistent formatting and labeling. Identify and label key data points like party names, dates, and financial terms to automate extraction.
Determine a learning algorithm (supervised or unsupervised) based on the specific tasks and goals within the contract management process. While supervised learning algorithms learn patterns and make predictions based on labeled examples, unsupervised learning is more suited to exploratory data analysis and pattern detection across unlabeled data.
Fine-tune and optimize various model parameters, such as complexity, learning rate, regularization, etc. By leveraging techniques like feature engineering and hyperparameter tuning, you can improve the accuracy and performance of a model while maintaining control over different aspects of the learning process.
Once the best-performing models have undergone thorough testing, they are prepared for deployment in a production environment where they can continuously learn from live data. This process involves integrating the trained model into larger systems, such as web or mobile applications or existing CRM platforms.
The deployment process is influenced by available data, hardware resources, and DevOps practices in the deployment environment. Performance, scalability, data traffic, security, and version control are essential to ensuring optimal deployment.
Establish strong security measures to protect sensitive document data. Implement access controls, encryption, and data privacy protocols. Ensure compliance with relevant regulations, such as GDPR or industry-specific guidelines.
It is essential to continuously monitor the solution's performance, accuracy, and effectiveness. This includes tracking key metrics and collecting feedback from users and stakeholders to identify areas for improvement.
Offer holistic training to users who will interact with the document AI system. Educate them on how to leverage its features effectively. Provide ongoing support to address any questions, issues, or enhancements required. Let them take charge of the workflow design and establish a feedback loop.
Document AI offers significant benefits for contract management, streamlining processes, improving efficiency, and enhancing accuracy. Organizations can automate contract analysis and data extraction by leveraging advanced technologies like NLP, ML, and OCR. Leveraging them enhances accountability, visibility, security, and compliance.
However, successful implementation requires data source assessment, thorough testing, and seamless integration. With ongoing monitoring and continuous improvement, document AI revolutionizes contract management, optimizing workflows and enabling informed decisions.
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