Introduction
Law firms handle a plethora of complex legal tasks daily, and managing multiple contracts simultaneously is one of the essential chores of lawyers. Managing a lot of contracts right from their initiation to execution and renewal/expiry can be challenging, especially when one adopts a manual approach towards it. This is where artificial intelligence comes into play.
Managing contracts using artificial intelligence does not replace the role of lawyers completely but helps in reducing their workload by automating repetitive tasks. This enables them to focus on business competency and growth. Apart from this, law firms gain from the intelligent recommendations provided by AI, enhancing their expertise through increased knowledge of relevant information.
The Contract Management Challenges
a). Most of the people are familiar with common contract management challenges. These include; dealing with a plethora of contracts that reside in various areas like software applications and hard drives. Mismanagement of contracts can make a firm lose 40% of deal value. Even the minutest of tasks like counting the number of contracts can be challenging since it involves communicating with various departments and collecting information manually.
b). As per the DocuSign’s State of Contract Management 2019 report, the typical negotiation time for a contract is approximately 30 hours as per one-third of surveyed employees. A quarter of them stated that their contracts go through changes or modifications every week, with 52% reporting that 3-4 contract versions are there, on average, before a contract gets finalized.
c). Furthermore, contract management inefficiencies can lead to regulation and compliance violations, missed deadlines, and unauthorized access.
How can MI (Machine Learning) and AI (Artificial Intelligence) Help You Address Common Contract Management Challenges?
As per the Harvard Business Review, AI used in contract management “has the potential to improve how all firms contract – and it will do so in three ways: by changing the tools firms use to contract, influencing the content of contracts, and affecting the processes by which firms’ contract.”
The transformation, influence, and outcome of a contract management solution as a result of AI and ML collaboration can be described as ‘consistency’.
A Single Tool
Since only a single tool will be available for everyone to use in a law firm, employees get a shared solution. This, in turn, paves the way for built-in best practices and a core rules set.
AI used for building a CLM permits access controls for designated people to ensure checking and approval of the right contracts by the right people.
Collective Supervision of Every Contract
The capacity of an AI-built CLM is unlimited. It not only comprises knowledge of a vast number of contracts but also uses every contract’s information while reviewing new agreements. Apart from this, it distinguishes the patterns in every agreement and uses the same for creating, editing, and finalizing contracts in the future.
Following One Standard Process
By getting rid of the traditional CLM knowledge, AI-powered CLM becomes the proficient contract workflow source. Time and money get saved considerably with the elimination of manual efforts.
A clearly defined contract management process will accurately and consistently permit the contracting movement. This means when employees do not have to manage the repetitive and administrative tasks, they can focus on core business tasks.
How is AI Changing Contract Management?
1. Bots for Voice or Instant Messaging (IM) User Interaction with Systems
By using virtual agent AI solutions, and enhanced communication channel can be established by CLM vendors to foster a user’s interaction with the system when it comes to initiating a contract or reviewing and approving a draft.
2. AI-Powered Wizards for Guiding Users to the Right Clauses and Templates
With the implementation of tools for decision management and platforms for machine learning enable CLM vendors in creating AI-powered wizards that are capable of providing a better responsive and intuitive experience of contract guidance. On the other hand, typical wizards often lead to a rigid process of contract guidance but AI can deal with multiple individual challenges to ensure a smooth development journey.
3. Automated Capture and Legacy/Third-Party Contracts’ Metadata Clause Tagging
Through the use of AI, the legacy manual processes used to import previous/counterparty contracts and apply individual metadata tags to the related within-contract-clauses can be elevated. Using the AI tools, a PLM system can easily display contracts that are imported, along with previous clause libraries, leading to a streamlined process with the help of drag and drop tagging. AI-assisted machine learning and natural language processing (NLP) will also make the metadata tagging automated.
4. Semantic Analysis AI to Recognize New Contracts’ Issues & Apply New Metadata Tags
In maximum CLM systems, Repository set-up metatags are most of the time the only metatags that are available for use. With changes that are quite unpredictable like tax-law or regulatory, the single possible method of finding the affected contracts lies in ineffective multivariable searches. By implementing semantic analysis and NLP, specific issue related words can be traced by CLM vendors, along with applying new metadata tags seamlessly.
5. Robotic Process Automation for the Approval of Standard Contract Language’s Changes
Standard terms and conditions for approval are used by the new contracts’ creators and initiators and is commonly seen when a different or new language is required for revising contract drafting to fulfill the requirements and demands of another party during negotiation. With multiple approval levels required, language and commercial terms will witness different change processes regularly. This approval process can be streamlined by the CLM system with the use of robotic process automation (RPA) by taking lessons from past decisions, altering the workflow based on such lessons, and providing meaningful recommendations to the approvers.
6. Advanced Analytics to Recognize New Risks, Opportunities, and Compulsions
In an average contract, a business relationship is defined by 10% of the language, and the rest 90% is used to assign the responsibility in case things do not go as expected. Most languages will explain risks, opportunities, and compulsions relating to the business relationship. A few examples of AI tools like machine, semantic analysis, and NLP can look out for and use contract language in addressing unanticipated risks, suggesting new ways to take advantage of previous customers or creating unintended compulsions.
7. Advanced Analytics for Mitigation and Risk Assessment
AI-powered solutions use NLP to evaluate documents, categorize clauses, and point out the dissimilarities between the versions of the document. This lets the system to go through the agreements and point out sub-optimal clauses or terms that may fall outside the organization’s interest. It can recommend alternative clauses that can help in reducing the risk and safeguarding the organization.
8. Actionable Insights on the Compulsions within the Contracts
AI-enabled contract management solutions let law firms gain access to valuable insights needed by them to derive the complete negotiations’ value. Using the different advanced capabilities, it can support law firms in analyzing huge contracts’ amounts and report on contractual data through the system across languages, geographies, and contract databases.
User-defined dashboards can be generated for reporting milestones, service level assurances (SLA), key performance indicators (KPI), and other parameters. For instance, it is now imperative to know business payment compulsions or recognize venture contracts bound by a particular regulation such as contract condition or requisite quality assurance.
Greater contract visibility enables tracking of the performance, enhances compliance, and optimizes compulsion management. Its user-friendly dashboards, reminders, and customizable alerts ensure greater visibility of terms mentioned in the contract as well as enhanced negotiated terms’ implementation.
9. Optimize Contract Review Timelines
As per the Forrester Research study, 3-4 weeks is the average contract approval cycle time. The reason being a lot of time is spent on multiple contract reviews, signing, and negotiations. However, with AI-powered CLM solutions, entire involved functions are integrated into a common platform, pointing out clauses’ changes, facilitating comparisons amongst versions, and speeding up turnaround and review times.
Apart from this, third-party contracts are also assimilated into the system. This is extremely helpful as it enables contract reviewing on a common platform, helping in the implementation of policies, and keeping compliance across the enterprise.
Conclusion
This clearly defines how proficient is AI in the contract management lifecycle. It not only guides the users towards the right templates and clauses, recognizes associated obligations, risks, and opportunities but also makes the approval process streamlined, saving time and efforts at large.
However, due to a lack of time and resources, lawyers are unable to reap the complete benefits of AI. This is where the providers of contract management services come to their rescue. With their AI-enabled processes, they help attorneys and law firms manage their contracts to perfection, providing them with unmatched benefits and saving time to help owners focus on core competencies.
