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Introduction
Contract lifecycle management (CLM) technology has become a critical piece of infrastructure for the modern organization, with many now understanding the efficiencies that can be gained by automating each part of the contract lifecycle. However, CLMs come in many forms and if you already have one in place, are there ways to realize even more efficiencies in your business? Read on to find out more.
Contract Lifecycle Management vs Contract Intelligence
Contract intelligence is the next evolution of CLM technology that uses a specific type of artificial intelligence known as natural language processing (NLP). The aim of NLP is to allow machines to process human language. As a result, they can perform tasks such as classifying contracts and extracting key information from them. Imagine you could automatically extract contract metadata such as limitation of liability clauses and deal values from your existing repository of customer contracts. If you then wanted to understand whether there is a correlation between what’s in your limitation of liability clauses and deal sizes, surfacing that information would take seconds, instead of days or weeks. The possibilities are endless.
One question we’re often asked is whether NLP-powered contract analytics is a separate category of product from CLM. Traditionally, this has been the case. CLM vendors have primarily been focused on the procedural, workflow aspects of contract management. Contract analytics vendors have primarily been focused on the substantive, NLP aspects. Both are difficult problems to solve which is why many vendors have chosen to specialize in one or the other.
This, however, is not the best result for the customer. Firstly, they require the budget for two separate pieces of software. Their users have to go through two onboarding and training processes using two platforms that may bear no resemblance to each other. And while both systems may require data from each other, there may be no easy way for them to exchange that data. This is why the most ambitious companies are incorporating best-in-breed NLP technology into their CLM products. The result? A win for the customer due to lower costs, less friction in onboarding, and less integration headaches.
Getting NLP up to Speed
NLP technology typically doesn’t work optimally “out of the box” and the NLP onboarding process can often be long, tedious, and painful. This is because legacy NLP technology requires an enormous amount of training data. Thousands of data points are often required to train the algorithms, and even then there’s no guarantee it will work with future contracts. We’ve heard of companies having “only” several hundred contracts being told that they don’t have enough data to train the AI!
The next generation of AI technology changes everything. This technology has been pre-trained on billions of documents, which means it is capable of generating accurate results using as few as two or three training examples. Those numbers are not typos.
Suppose, for example, that you’re interested in extracting the notification period in your data breach notification clauses. Traditionally, you’d need to upload several thousand contracts, manually train the data such as by manually tagging contracts individually (at considerable time and expense), and then hope that the algorithm will work for your next few thousand contracts. That’s a process that could take weeks or even months. With the latest technology, you should now be able to perform that test in minutes.
Developing the Business Case
There are numerous benefits to adopting an AI-powered CLM platform. Firstly, it will enable the business to achieve richer insights. While housing contracts in a traditional CLM offers many benefits over storage on a shared drive (or a filing cabinet), those contracts are still static. With AI-powered CLM, the business will be able to leverage the data in those previously-static documents and visualize trends such as the number of NDAs signed in a given quarter, or changes in limitation of liability caps over time. Secondly, AI-powered CLM facilitates improved obligation management. Tracking of renewal dates will help ensure that on the buy-side, contracts aren’t inadvertently renewed, and that on the sell side, cross-sell and upsell opportunities are not missed. Additionally, the company will have full visibility over commercial events such as CPI increases and performance obligations such as contract deliverable milestones.
Finally, AI-powered CLM allows in-house lawyers to better perform one of the core functions of practicing law: risk management. Suppose, for example, that a company needs a snapshot view of all force majeure clauses in its contract repository. As a baseline, the company would at least want to know which contracts have force majeure clauses. Ideally, the company would also want to more granular information, like how long a force majeure event needs to continue before a party has a right to terminate. This information may not have previously been practicable to obtain if you have hundreds or thousands of contracts to review. However, with AI-powered CLM, that capability is unlocked, allowing legal teams to provide that information to facilitate informed decision-making by company leadership.
AI-powered CLM is not a panacea. Contracts can be complicated legal documents. Interpretation and judgment from trained human professionals who can also consider the business context in which a contract exists is not something that will be replaced anytime soon. But there are also a lot of useful analytical tasks where AI excels that are simply infeasible for humans to perform given resource constraints.
So, What Should You Do?
We recommend a discussion with an AI-powered CLM vendor to explore how they might be able to help your business. The ideal vendor will:
- Be open to just a conversation – if now is not the right time for a purchase, the vendor should be happy educate and answer questions, without pushing for a sale
- Be able to demonstrate how their AI works on your contracts – any “sample” set of contracts can facilitate a great-looking demo. But the only question that matters is whether their AI works on your contracts.
- Offer a guarantee – the ideal buying process involves no risk to the customer. The vendor should be able to facilitate this by setting up low friction proofs of concept, along with a guarantee that there will be no charge for the AI if it does not meet acceptable benchmarks. (Our customers tell us that 80% is the minimum tolerable accuracy threshold.)
- Provide a solution that delivers rapid time to value – some vendors in this space charge hundreds of thousands or even millions of dollars annually. For very complex cases, this is a justifiable fee. But for many companies, the fair market price of AI-powered CLM is considerably less than this. A reasonably priced solution delivers strong ROI and rapid time to value.
We hope that you have found this article interesting and informative. If you still have any questions, please feel free to get in touch!
