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Contract Lifecycle Management and Artificial Intelligence: The Future is Now

M ost Americans use artificial intelligence (AI) in some form or another on a daily basis, though they may not even realize they’re doing so. After all, it’s AI that’s the secret sauce behind iPhone’s Siri, Amazon’s Alexa, and Google’s natural language search engine. Those who don’t use those tools might still have a sense of AI’s power.

More than 10 years ago, IBM’s AI creation, Watson, defeated the best human Jeopardy players. Twenty years ago an earlier IBM AI creation, Deep Blue, defeated the world’s top chess player. As if those demonstrations weren’t impressive enough, the technology has significantly advanced since then. Among many professions, including education, energy, and environmental work, AI has opened up new markets and opportunities. It appears to be on the precipice of saving lives in the medical field.

Yet, this remarkable field of technology has, so far, made few inroads in tools for legal professionals beyond at the edges. Online legal research companies have made some progress in using AI to aid their search functions, but beyond that, it’s largely crickets. In terms of a substantive tool, the introduction of AI into law is just dawning, but as implementation accelerates, the changes that accompany it are potentially dramatic.

Perhaps most significantly, it promises to vastly improve the Contract Lifecycle Management (CLM) tools now available to law departments and firms. CLM is ready for change too. For those who manage contracts, the CLM tools they’re using often look like the digital versions of the metal filing cabinets used in our grandparents’ era: a place to store contracts at various stages of their lifecycle, to sit dormant unless something goes wrong, at which time the contract is retrieved and pored over.

That needn’t be the future, though. New developments will bring life to CLM tools, and allow users to discover new relationships and build intelligence around the data that can be extracted from the old versions’ electronic filing cabinet.

This article summarizes some of those opportunities. To do so, it explains the uses for AI in the main functions of CLM solutions, which can be broadly framed into three categories: authoring, storing, and analyzing. Each of these areas allows for the use of cutting-edge automation and AI features.  

Authoring

Today many CLM tools can store a series of clauses and templates to be used when constructing a contract. This makes for efficient creation of new agreements, by allowing users to see what they’ve used in the past. AI, however, will take the ability of software to help in authoring projects to the next level. With an AI engine powering the CLM, the level of awareness of the tool moves from content to context.

This means that the CLM can help not only in building the contract, but also in identifying the pertinent clauses that should be used for a particular type of contracts. For example, an AI-boosted CLM can recognize and inform users that a procurement contract should contain a currency fluctuation clause if the vendor is not based in the United States. This sort of context-based analysis is the hallmark of AI: the computer program is capable of learning with each experience.

CLM can help not only in building the contract, but also in identifying the pertinent clauses that should be used for a particular type of contracts.

It can learn too. AI is best powered when it’s a sort of technological minotaur (instead of being half human, half beast; it’s half human, half computer). Here, that means that if a misapprehension occurs, humans show the machine the error, and the machine learns from it and doesn’t repeat the mistake.

By way of example, imagine the AI-powered CLM reads the word “Lebanon” in a contract, and, as a result, inserts a currency fluctuation clause. However, in this hypothetical situation, imagine the location, Lebanon, referred to the town in New Hampshire, not the country in the Middle East. Here, the user would correct the CLM’s misapprehension, and the next time the default would recognize it correctly.

Storing

Searching for a record in a repository with hundreds of thousands of contracts is cumbersome. A CLM with an AI engine that learns on the job can perform a Google like natural language search rather than search based on exact “boolean” text matches. Because the software learns, it is able to identify patterns within searches, and helps make better connections, quicker.

Think of it as solving what has been called the “Paris Hilton” problem: When someone does a Google query using those words, will it return information about a 3-star hotel in the City of Lights — or the socialite from California?

The answer: well, the prediction depends who’s asking (a world traveling business person or a teenage pop culture fanatic). However, an AI-based tool will know enough about the users, and the context in which they use it, to return the right result. That’s where we’re headed with CLMs that use AI, and having the right results the first time will save users time and energy.

Analyzing

AI really shines when it comes to analyzing information within a CLM.

It’s as simple as this: with the right CLM that uses AI, every contract in the drawer becomes not just a thing taking up hard drive space, but a source of intelligence. Each agreement contains data about how an organization deals with each part of the contracting process: how it deals with rights, remedies, insertion, or deletion of a certain clause, or the time it takes them to execute a deal, the new generation of CLM can provide intelligence based on looking at vast collections of information and drawing conclusions based on it.

The power to use the information contained in contracts will change the way that businesses develop intelligence. Consider a company that inks thousands of contracts a year. With a modern, AI-based CLM system, it can scan each agreement into the system. From there, behind the scenes, the modern CLM tool can parse and chunk the content into clauses.

The power to use the information contained in contracts will change the way that businesses develop intelligence.

After loading a number of contracts in, the user can generate a report to see the way a specific issue is dealt with, as well as to identify outliers. Along the way, the system can continually get more accurate and efficient. Ultimately, the system can learn about trends related to specific customers or specific domains and provide reports, parsed to provide intelligence on the specific customer, clause, or issue that the CLM user wants to consider.  

Managing — the forgotten contract lifecycle

The modern CLM tool can also create unseen value during the term of the agreement. Consider the possibility of a system that can handle obligation and compliance. As events during existing contracts occur (or fail to occur), it triggers actions. For example, imagine a contract where one party agrees to purchase 100 t-shirts before a given date and, if it fails to do so, a 10 percent price penalty goes into effect.

The new generation of CLM can send a notification warning that a condition isn’t met a few days before the deadline, trigger the penalty if no sale is made by the deadline, or create a debit memo if the sale occurs before the deadline. Automating this process is made simple, with a complete paper trail created without the need for human interaction.

Consider as well the possibility of a CLM that uses the inputs from a supplier performance system to update its own risk rating algorithms, effectively learning from both positive and negative scenarios to adjust risk profiling based on real world variables or using social media listening agents to adjust workflows and approval gates based on current trends.

Automating this process is made simple, with a complete paper trail created without the need for human interaction.

The growth of AI in CLM — and law, generally — has the potential to make lawyers more efficient, and to open doors previously inaccessible. The new horizon, combining AI and CLM, promises to give legal workers more answers than ever before, in areas, perhaps, they never thought to consider. This savings in time, energy, and effort also allows attorneys to spend less time on drudgery, and more on work that adds value.

About the Authors

Gabe Teninbaum is the director of the Institute on Law Practice Technology & Innovation at Suffolk University Law School, as well as a visiting fellow at the Information Society Project at Yale Law School. [email protected]
Arthur Raguette is executive vice president for business development at Ultria, which offers a leading CLM solution providing end-to-end contract management solution for the enterprise. [email protected]


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