The International Association for Contracts and Commercial Management (IACCM) report revealed an incredibly low client satisfaction rate with existing Contract Lifecycle Management (CLM) solutions – a disappointing 3.6 on a scale of 10.
Reason being, most of these CLM solutions seem to operate as a glorified contract repository with an overpriced CTRL+F search function. As a result, customers feel that they are only paying for a virtual vault where their contracts are simply stored, archived, and reduced to trifling, static documents. With such software adding to running costs, and a growing awareness about inefficiencies caused by a poor CLM solution, it is easy to see why customers are disgruntled.
However, the recent advancements in artificial intelligence and its revolutionizing application to …. well, everything lately, seems to be holding true for CLM software as well.
Don’t worry though, this isn’t one of those articles where we’re jumping on the A.I. buzzword bandwagon. Rather, we are going to trace the stages of contract lifecycle management and elucidate how leveraging A.I. streamlines processes and improves the productivity quotient at every stage.
Let’s take a look at the various steps carried out in a typical contract lifecycle management process.
Step 1: Contract Request and Authoring
This step involves one party requesting a contract followed by the preparation of a first draft of the contract.
⇒ Performance using traditional CLM software: Using a traditional CLM software, legal teams can only get so far as using generic, boilerplate templates as a basis for drafting their contracts. These templates are often not of much use since they fail to account for differences in contract formats and clause variations across different countries and industries.
⇒ Performance using an AI-enabled CLM software: An AI-enabled CLM has the ability to instantly identify and categorize contracts in a firm’s contract repository. Through its machine learning, such AI can automatically learn from past contracts entered into with a certain client sector to identify and suggest the appropriate contract clauses that are relevant to the jurisdiction and industry of practice.
Step 2: Negotiations, Approvals, and Signatures
During this stage, the parties discuss and debate over the clauses drafted in the previous stage. This involves a great degree of collaboration within and between the legal teams involved to ensure that their respective expectations are understood and accurately accounted for. Once all the parties reach an agreement on the terms, they conduct a thorough review of the clauses included in the contract. Upon satisfaction, the contract is finalized, and each party signs the contract.
⇒ Performance under traditional CLM software: Such a software does not provide legal teams with a secure, organized, and streamlined document collaboration platform. It fails to give lawyers access to an up-to-date, chronological record of the modifications that have been made to the terms of the contract. Countless hours are spent in reading fine print of contracts to ensure all relevant clauses are included to conduct a thorough contract review. Because of this, gaining an insight into the expectations of parties, which is useful from a compliance and execution standpoint, becomes a challenge.
⇒ Performance under AI-enabled CLM software: An A.I. driven CLM can automatically fetch all important key obligations and any other information important from contract review point of view, across thousands of contracts, in seconds. In addition to a streamlined document collaboration platform, an A.I. driven CLM software is also able to deliver clause merging from a customized clause library suited to a firm’s industry or client sector. With its analytical capabilities, the A.I. can be used to prepare customer-defined dashboards to provide businesses with service level assurances, KPI’s, etc. For example, the AI can identify and inform the user of all contracts in the business’ system that is bound by say, a specific regulation or contract provision. Further, it can pick up on clauses that conflict with terms in any previous contract between the same parties. Besides maintaining a record of the changes made to a contract, the AI-enabled software can also highlight the differences between various document versions. Impressively, the software has the ability to highlight substandard clauses as not being in the best interest of one of the parties. In doing so it can categorize a clause as too wide in scope, too narrow in scope, poorly framed, etc. This feature is a gamechanger when it comes to assessing and mitigating risks to safeguard one’s organization.
Step 3: Obligations, Compliance, and Renewals
At this stage, all the involved parties effectively acknowledge their responsibilities and rights specified in the contract and work towards executing such performance in accordance with the time windows specified in the contract. They also make themselves aware of any industry-specific codes of conduct, guidelines, or standard practices that are to be complied with in addition to the contract.
⇒ Performance using traditional CLM software: Most traditional CLM software have features to manually set reminders for key dates and task alerts to notify the relevant parties when the time arises.
⇒ Performance using AI-enabled CLM software: In the case of an AI-enabled software, such reminders don’t need to be set manually. Rather, huge volumes of contracts are auto-tagged to automatically (and accurately) send out key deadline reminders to relevant parties when the time arises. Automatic renewals are also an option. AI-enabled software can also generate compliance and obligation-performance status reports that are made available on a user-friendly interface. Such software also allows for seamless integration with accounting standards, industry guidelines, etc.
The Forrester Research study estimates that the aforementioned contract life-cycle takes around 3-4 weeks to complete. An AI-powered CLM software can not only reduce this time significantly but can also process, infer, and extract critical business information from a large volume of structured and unstructured data.
Conclusion
Using such software, companies can transform those ‘static documents’ we were talking about into strategic, interactive assets that derive real competitive edge for businesses from the text and context within the four corners of a sheet of paper. With such exponential benefits, a company’s upgrade to an AI-enabled CLM process is definitely recommended. There’s no scope for disappointment this time!
