AI Contract Lifecycle Management for Clause Risk and Approval Workflows
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern business organizations face challenges in managing the lifecycle of contracts due to the lack of standardization, complexity involving multiple stakeholders, and the difficulty in ensuring appropriate transparency and risk management, especially in IT and BPO services, leading to inefficient contract execution and governance.
Innovation Solution
An automated system leveraging AI techniques, including deep learning and natural language processing, to manage the lifecycle of contracts by generating templates, identifying meta-clauses, evaluating risks, and managing workflows for stakeholder approvals, with features like fuzzy logic for tracking contractual commitments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual contract management processes are used, then flexibility in handling diverse contract types is maintained, but productivity and efficiency deteriorate due to the volume of contracts and multiple stakeholders involved
Solution Approach 1:
The contract management process is segmented into distinct functional modules including template selection, digitization, risk evaluation, stakeholder routing, and fulfillment tracking. Each module handles specific aspects of contract lifecycle management independently, improving overall processing efficiency while maintaining manageability through modular architecture
Solution Approach 2:
An automated system acts as an intermediary between multiple stakeholders (legal teams, finance, management, vendors) and contract processes. The system mediates routing, approval workflows, and communication, reducing the complexity of direct multi-party interactions while maintaining coordinated collaboration across all stakeholders
2Manufacturing precision
If standardized contract templates are implemented, then manufacturing precision and consistency are improved, but adaptability deteriorates when dealing with diverse IT and BPO service contracts
Solution Approach 1:
The template selection system is designed to be dynamic and adaptive. The system automatically selects or generates appropriate templates based on the specific contract type, service category, and stakeholder requirements. Templates can be dynamically created or modified to accommodate diverse IT and BPO service contracts while maintaining standardized structures for common contract types
Solution Approach 2:
The system changes parameters such as template structure, clauses, and metadata requirements based on the specific contract type and service category. By dynamically adjusting these parameters, the system maintains high standardization for common contracts while adapting to the unique requirements of diverse service agreements
3Reliability
If comprehensive risk evaluation is performed on all contract clauses, then reliability is improved, but loss of time increases due to the voluminous nature of contracts
Solution Approach 1:
Manual mechanical review of contract clauses by legal and risk teams is replaced with automated digital processing. The system uses natural language processing and machine learning algorithms to automatically evaluate risks across all contract clauses, providing comprehensive risk assessment accuracy while dramatically reducing the time required compared to manual review processes
Solution Approach 2:
The contract review process is designed to be self-service and automated. The system autonomously performs template selection, digitization, risk evaluation, and stakeholder routing without requiring continuous manual intervention. This self-service capability enables comprehensive risk assessment of all clauses while minimizing time loss through automated processing
4Reliability
If multiple stakeholders are involved in contract lifecycle, then reliability of decision-making is improved, but device complexity increases due to coordination requirements
Solution Approach 1:
The multi-stakeholder workflow is segmented into distinct approval stages and routing paths. Each stakeholder group (legal, finance, management, vendors) has defined roles and responsibilities for specific contract aspects. The system automatically routes contracts to appropriate stakeholders based on contract type and content, reducing coordination complexity while maintaining comprehensive decision-making involvement
Data Source
AI summary
Contracts are a fundamental tool for coordinating economic activity and need to be managed throughout the lifecycle of contracts. The existing methods are incomplete, expensive, time-consuming, and error-prone. A method and system for management of lifecycle of contracts have been provided. The system leverages a combination of artificial intelligence (AI) techniques appropriate for different micro services in contract lifecycle management. The deep learning and natural language processing (NLP) techniques help in understanding of clauses of the contract, risk levels involved in the contract. The system is configured to automatically generate contracts for a customer based on the other criteria of the customer. The system also identifies alternate options to risky clauses and mandatory clauses to be included. The system is also configured to manage the workflows based on context of contract to seek exception approvals from appropriate stakeholders during contract creation and alert appropriate stakeholders on delivery governance issues.