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When we think about artificial intelligence, what comes to mind might be HAL 9000 from 2001: A Space Odyssey, Skynet from Terminator, and even Ultron from Marvel (a.k.a. Tony Starks mad AI experiment). You know, robots are going to take over the world. Humanity is doomed. Computers for life. The matrix is real

Thankfully, not all AIs are so destructive and dangerous. For every Terminator, theres also R2-D2, WALL-E, and Data from Star Trek. Suffice to say, Earth is safe for now, and chances are low that well find ourselves in a world where machines take over and force humans to fight as an underground resistance.

Most of the AI we see in films and series are still works of fiction. Whether we get to that level, only time can tell. But be that as it may, there are many advanced applications of AI in everyday life, including AI in contract management.

Before we dig deeper into how AI plays a role in CLM, here are a couple of key definitions.

What is Artificial Intelligence?

AI attempts to replicate humans intelligence or behavioral patterns. Theoretically, an AI can be created based on some other living entity, so long as its intelligence or behavior can be mapped, but for simplicity, lets assume all AI in legal technology tries to replicate human behavior.

Creating an AI requires the application of techniques that enable computers to solve problems as if it were a human. This usually takes the form of a set of pre-defined rules.

What is Machine Learning?

Within the larger sphere of AI, ML is a field comprised of four subcategories: supervised learning, unsupervised learning, reinforced learning, and deep learning. Its a technique that allows computers to learn from data. In other words, its a type of AI that learns by itself.

Often, ML is conducted by training a computational model (also known as an algorithm) using a dataset. The more time and data provided, the better the computers performance.

A subset of machine learning is deep learning. This attempts to use artificial neural networks, which are computer systems that imitate the human brains neural network. These ANNs are designed so that an increasing number of characteristics are extracted from the input data.

In simpler terms, deep learning is simply machine learning, with the key difference being that the model is based on human neural networks.

Machine Learning vs Artificial Intelligence

Some people use AI and ML interchangeably, as if they were perfect synonyms. Others might refer to them as completely separate, parallel examples of advanced technology. Sometimes this is because both words are overhyped, and sometimes its because people dont quite get the difference.

The short answer is that AI and ML are not the same.

As we mentioned earlier, machine learning is a field of AI. Theyre closely related, yet still different. All machine learning counts as artificial intelligence, but not all AI counts as machine learning. AI, in fact, can be divided into four primary parts: Reasoning, Natural Language Processing (NLP), Planning, and Machine Learning.

AI can be divided into four primary parts: Reasoning, Natural Language Processing (NLP), Planning, and Machine Learning.

An example of an AI that doesnt use ML is a chatbot. Certainly, there are chatbots that use ML, but a basic chatbot doesnt require it. Thats because its a rule-based system whose rules (the questions it asks and answers it receives) were defined by people. These sorts of expert systems are essentially a sequence of if this, then that statements, otherwise known as a decision tree. Although no learning occurs, such rule-based chatbots can still prove highly useful.

But lets imagine we decide to create an ML-based chatbot. Whereas all questions and answers were previously scripted for a rule-based bot, an ML-based bot is given a massive corpus containing hundreds of thousands of conversations to clean up and analyze. Then based on the information provided, the ML-based bot is trained to handle various situations.

AI and ML in everyday life

Most likely, you interact with some tool or program that uses AI or ML, such as any Google product. Major services like Gmail, Google Search, and Google Maps all have ML. For example, how emails are filtered into the primary, social, and promotional tabs, or how Search and Maps anticipate what youre looking for based on past searches, trends, and even your location.

Then there are assistants like Siri and Alexa, social media newsfeeds, Netflix, Amazon, and even smart home devices. As the world becomes increasingly digital and smart functionalities become more ubiquitous, its almost certain that youll be interacting with ML-based products and services on an hourly basis.

AI and ML techniques in contract management solutions

To quickly recap AI and ML, think of artificial intelligence as an attempt to give machines the intelligence of people. In contract management, this would enable an AI to read and interpret agreements while extracting key information.

Machine Learning is the process of teaching or training the AI by exposing it to massive quantities of documents. The larger the dataset, the better the AIs accuracy and the more stable its performance.

But one critical technique that hasnt been highlighted is NLP, which is the ability for software to recognize and mimic a persons speech. This approach helps identify and resolve potentially trick spots in the language. Its also used when contracts are being drafted, when spotting missing contract aspects, and categorizing contract attributes with regards to the context.

Role of AI in CLM

Currently, there are three core roles that artificial intelligence plays in contract lifecycle management:

Data extraction from active contracts

With the help of ML and Natural Language Processing, legacy contracts and other documents you had before implementing a CLM software system can be quickly imported to the new CLM platform. This is of critical importance if any of the contracts and agreements are still active. To avoid complacency and missed deadlines, its a good idea to upload legacy contracts so that the new CLM software can do its job of managing obligations and generating notifications.

Since this can be done using ML and NLP, the importing process takes considerably less time. Teams no longer need to open PDFs, read terms, save them in Excel, and manually track them. Thats because both ML and NLP help identify a contracts metadata, such as the due dates, deliverables, and signing parties. Better yet, technology is good enough that even if a contract is a scanned image (this might be the case if your signed contract is only in paper form), information can still be extracted.

This kills two birds with one stone because at the same time that youre importing legacy contracts to the new CLM platform, youre training the ML algorithm on your contracts. That means the ML will be more capable of handling your future contracts.

Assistance during contract authoring

During the stone age of CLM software, processes were simply made available in a digital format. For example, the system would simply provide fields that could be filled in with data.

CLM software has come a long way since that, in part due to significant advancements in AI. Trained on thousands of contracts from several years, a quality AI-based CLM software can act like a senior advisor by reviewing contracts and suggesting opportunities for negotiation and risk management. Assuming your organization has a clause library, it can even provide recommendations on which clauses to use according to geography, vendor type, and contract value.

Some CLM platforms even offer a chatbot for the contract authoring process. Not all are powered by ML, but they add a great deal of value for users by helping fill out contracts with the answers to select questions.

Management of contract obligations

Arguably AIs biggest role in CLM, an AI can extract obligations automatically from contracts (including uploaded legacy documents) to manage them. At present, AI still has some problems when extracting non-quantifiable obligations like IP protection and employee welfare. But when the language features quantifiable terms like discounts and due dates, AI works like a charm.

Major applications of AI in contract management

As mentioned previously, AI has come a long way since its inception. The meaning and techniques used have consistently evolved over the years until weve become dependent on AI to simplify common problems. Google and Netflix are major examples of the strides that AI has made in solving peoples problems, but other industries like medicine and finance have also been affected. Legal is no different.

How an AI-based CLM can solve contract management issues

Here are just a few of the ways that AI can address common problems of contract management:

Batch review

Instead of checking one document at a time like a person would, an AI can review multiple agreements, update terms, and import legacy contracts in batches. For example, if your government regulator changes certain legal requirements, you can input the fix across multiple documents at the click of a button.

Risk

The more contracts your organization handles, the more likely there will be missed renewals, fee increases, and compliance problems. The failure to abide by contract terms is one of the leading causes of commercial legal disputes. An AI-based CLM is capable of generating the reminders you need and monitoring obligations so that your organization stays compliant.

Time

Its been said before, and itll be said later, but the consequences of saving time are massive. Not only do you save money as a direct result, but your employees will have greater freedom to dedicate their efforts to more productive and professionally rewarding tasks.

AI can be used to help overcome challenges that frequently appear as contracts make their way through the workflow. But thats not all. With the right approach, AI can unlock innovative, new possibilities.

For instance, GPT-3, which is a neural network model that uses deep learning to generate any type of text, opens up countless opportunities. Its ability to perform reading comprehension and writing at near-human levels stems in part from having consumed more text than any human can ever read in their lifetime.

Reasons to use AI in CLM

There are a lot of excellent reasons to use artificial intelligence in contract lifecycle management. But first and foremost, the top three reasons are that it saves a lot of time, a lot of money, and a lot of effort. Processes can be completed 80% quicker (which in turn saves money), and a lot of repetitive tasks can be carried out automatically.

Those are just some big picture reasons though. Here are some specific benefits that AI can provide legal companies and legal teams:

Automated contract compliance

An AI CLM system can be configured to take into account regulatory and contractual compliance terms that are industry or jurisdiction specific. Also, in addition to setting notifications for specific team members regarding dates and obligations, an entire audit trail can be created.

Maximized employee expertise

AI is unable to operate with complete independence. There must always be somebody overseeing its performance in order to guide and fine-tune it, or to step in if theres an anomaly or problem. As a result, companies can leverage and maximize team members expertise while freeing them from repetitive, less interesting tasks.

Standardized contractual processes

AI can ensure that theres consistency across the entire CLM process by putting every contract through the same sequence of stages. Not only is this the most efficient approach to managing contract lifecycles, but it enhances visibility from start to finish while simplifying the identification of any contracts with issues to be resolved.

Improved connectivity

Robust, organized systems can unlock countless opportunities for companies thanks to the increase in efficiency. AI contract software can clean up and standardize data so that it can be easily integrated with other powerful tech. Otherwise, if data remains inconsistent, and if storage and retrieval systems are outdated, then teams will miss out on the advantages that automated CLM solutions can provide.

Efficient scaling

If an AI-based CLM solution has made data standardized, compliance automated, and visibility ensured, teams can scale and add layers of management without becoming overly complex and inefficient. Thats because an AI CLM software system doesnt need an increase in personnel to keep a growing organization running smoothly.

Conclusion

One of the most important things to remember about AI in contract management is that its not about to steal anyones job. Like we mentioned before, AI is best deployed in a supporting role where it can provide critical assistance in speeding up contract processes. Although modern AI has made great strides, it cant function 100% independently. But if deployed correctly, it can speed up the contract workflow by up to 80%.

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Reap the Benefits of AI in CLM | AXDRAFT (an Onit company) - JDSupra - JD Supra