Of 650 Contract Professionals attending a Contract Management Conference; how many raised their hands when asked if they were currently using AI/ML?
The answer: 6 people in total raised their hands at an IACCM (now World CC) Global conference. If the same question was to be asked today, the numbers would unquestionably be higher; albeit, the “interest” in using AI/ML for enterprise contract management software has always been multiples of that practical usage number, often approaching 50% or higher. What does one make of these data points? Two years ago, the practicality of using AI/ML within a contracting process was not as achievable nor was it the priority that it is today. Furthermore, AI/ML technology was still in a nascent state, despite it being magnitudes easier, more intelligent, and easier to “train” since it first started to surface decades ago.Why Does AI/ML Matter In Today’s Contract Management Process?
In short, the infusion of AI/ML functionality into the contracting process does what most other automation initiatives introduce at a macro level; efficiency improvements and risk avoidance/reduction. In the context of enterprise contract management software, this allows for 3rd party and executed contracts to more easily get loaded into the centralized repository with the accurate capturing of key data elements such as the counterparty name, contract expiration date and contract value. In this regard, efficiencies are introduced as otherwise, this would be a manual, time-consuming and error-prone endeavor. In terms of risk avoidance/reduction, the capturing and interpretation of risky language within a contract may be the catalyst for moving forward with or canceling a relationship or transaction that otherwise would be too risky.
These two factors are compounded further through M&A activity which requires that due diligence efforts mandate clarity and assessments of contractual commitments. In the absence of such risk scoring mechanisms achieved through AI/ML, organizations are left to laborious and error prone contract risk rating. This frequently is performed in a superficial capacity which exposes the organization(s) participating in such contractual relationships.
AI/ML Deconstructed For The Enterprise Contract Management Process
While the high level, value propositions of AI/ML in contracting is framed out above as efficiency improvements and risk mitigation, the practicality of it within the realm of contract management can be segmented as follows:Ad-Hoc Contract Ingestion
As a Contract Manager or business user, I need to be able to easily drag a document(s) into the system and have it automatically create the contract record, upload the documents, associate them with the contract record and extract and store relevant metadata. I also need to easily know if something went wrong or if the process succeeded.High-Volume Contract Migration
As a Contract Manager (and not an IT person), I need to take a large volume of contract documents and have the contract management software create counterparty records (or associate with existing counterparties), create contract records, load up the documents into the system, associate the document(s) with the contract record. As part of this process, key metadata will be extracted and stored into the system; which may include obligation data.Contract Language Risk Analysis & Rating
When a document, typically, third party paper, is loaded into the CLM software, it should be able to use AI/ML to digest the document, interpret the clauses and compare them against standard clauses within a Standard Clauses library. An interface should be available to review the risk rating, suggested standard clause language and allow for redlined insertion of standard clauses in place of those that are provided in the document.AI/ML Based Contract Search
As a Contract Manager, Risk Analyst or any other role that requires visibility into contracts (3rd party paper or your internal paper), there is an on-going need to have the CLM software allow for the searching and identification of contracts that meet certain search criteria. The value add with AI/ML is that the searching and identification of content is not necessarily dependent on the precise criteria defined in a contract query; rather, the AI/ML engine should be able to look for language that is similar and appropriately relevant to deliver more meaningful search results. For example, a new regulation may be introduced which requires that all contracts are identified and available in a report when arbitration is the preferred vehicle for disputes as compared to litigation. In this scenario, standard criteria for such a query may include the term “arbitration”. With an AI/ML powered contract management software the AI/ML engine may extrapolate further to include such contracts where adjudication or mediation is referenced; however, this happens without having to issue such specific variances on the searching.It’s Not Just Contracts Dummy
While this may not be as surprising as the 6 out of 650 people reference in the beginning of this post, it does raise an important point that so many AI/ML contracting enthusiasts miss. Specifically, contracting systems and of course, AI/ML enabled CLM software are employed to generate and manage many other documents that extend beyond the initial and primary agreement. These may consist of SoWs, Change Orders, sub-contracts, Amendments, etc. It is in this regard that the “It’s Not Just” references come into play. Many people forget about how important these supporting documents are within the context of their respective contracts. In this regard, AI/ML should be fully exploited for all contract documents and not just the primary agreements.
