As with any topic that sounds overwhelmingly complex, confusing or concerning, there is a lot of varying viewpoints on AI. However, there is no doubt that AI today has evolved and reached a stage where it has begun to create substantial impact across domains – Legal being no different.
The most significant impact AI can have on the legal front is arguably in Contract Lifecycle Management. With contract management issues contributing more than 8% in revenue losses, it is only logical that tools that help in mitigating such issues be leveraged, especially AI-powered solutions.
Before we get deep into the topic of how AI and tech-driven CLM solutions are disrupting the legal operations, let’s first try to understand what Artificial Intelligence actually means.
Artificial Intelligence is the branch of computer science that aims to create intelligent machines, the key governor being intelligence. Programming for intelligence means the ability to possibly comprehend a variety of traits, including reasoning, problem-solving, planning, learning, and perception – all integrated around knowledge.
AI has been extensively used across large enterprises, including decision support and smart search systems, and has broadened applications into advanced semantic / language processing, computer vision and image processing.
As with any buzzword, there has been misuse of artificial intelligence and people tend to attribute any form of automation to AI. It is those above-mentioned traits – their variety and complexity – that make AI stand out. Specifically, reasoning and ‘projection’ involved in AI are what makes it distinct from earlier ‘dumb’ systems commonly used in enterprises, pre-2000.
Unflatteringly, numerous vendors still advocate knowledge-based systems as being classified under Artificial Intelligence. The rule / constraint / logic-based systems do a great job for many tasks, but these are, at best, reactive machines, and fundamentally not based on Machine-learning or AI concepts.
Machine learning is the technology behind predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images.
When companies today deploy artificial intelligence programs, they are most likely using machine learning — so much so that the terms are often used interchangeably, and sometimes ambiguously. Machine learning is a subfield of artificial intelligence that gives computers the ability to learn without explicitly being programmed.
Deep Learning is a type, or a subset, of Machine Learning, but one where it involves larger data sets. But isn’t that true for all ML? The difference lies in the fact that Deep Learning algorithms don’t necessarily need structured or pre-labeled data.
Using algorithms like Artificial Neural Networks (ANN), Deep Learning sends the data through various ‘layers’ of algorithms, or ‘networks’, with each network, hierarchically defining specific attributes of the data – or in other words, it makes sense of the structure by itself.
This is, in many ways, how the human brain works – by passing it through various ‘concepts’ and hierarchical questions and then arriving at an answer.
Ultimately, of course, ML and DL is only so effective, if the quality of data it uses to learn is sufficiently accurate and consistent.
It would be easy to assume that Deep Learning is advanced AI. Simplistically speaking, Deep Learning can provide a better and ‘deeper’ understanding of data.
But DL involves a much larger set of good data, which many organizations may not have access to. Machine Learning, on the other hand, can be sufficient for many of the basic organizational use-cases, including aspects of Contract Lifecycle Management.
Simple answer – Yes!
At Zycus, we heavily use ML for processing the digitized contract text, and to make sense of the various terms and clauses. AI-based techniques including Natural Language Processing (NLP) and Semantic Analysis are used in this regard. ML is sufficient for many standard tasks including meta-data extraction, whereas Deep Learning and ANN can be very useful for advanced use-cases, including auto-segmentation of Contract Clauses, and Risk Identification and Management.
According to a recent Zycus survey, around one in four large enterprises still do not use a CLM, and of the 75% who do, almost half of them put their maturity of 4 or below, on a scale of 1-10.
It is imperative that organizations start using CLM solutions for basic contract requests, authoring, review, and negotiations – for which non-AI solutions like iContract would be apt.
The digitization of the data and repository management, along with the structured workflows available in standard CLMs would help in priming the organization for the next stage of Legal Operations evolution, driven by AI. Methodologies like RPA can also help organizations achieve a degree of scale in their Contract Management operations.
Contract lifecycle management today has become a key focus area within the corporate legal space – especially considering exponential litigation costs and other liabilities.
AI-based applications can help mitigate many of these risks, by providing an added safety-net for many of the complex and unstructured processes, and freeing up time for high-value resources to focus on more important aspects of Contract Management including compliance and risk management.
At Zycus, we have broadly classified AI-based CLM use-cases into three major segments:
The bedrock of any advanced contract management system is its ability to read contracts. With contract documents becoming increasingly unstructured and complex, the accuracy, as well as coverage of such processes can determine the success of adoption and eventual advocacy of the solution.
This is where AI can play an active role, ideally augmented with Machine Learning and ARR / Deep Learning algorithms.
There are many AI techniques and frameworks used today including:
Many, if not all techniques are used with varying degrees across CLM systems today. Arguably, the most important among these, with regards to impact and use-cases, would be on Artificial Neural Networks, and Natural Language Processing.
NLP technology has matured quite well today, with chatbot systems that can understand user intent, and propose solutions according to the context. In Contract Management, Natural Language Processing and its subset, Natural Language Understanding, has a vital role to play in meta-data extraction, clause identification, and more.
In fact, accurate, quick and comprehensive analysis of large contracts is the base on which further analysis and insights can be derived, both during pre and post-award stages.
Natural Language Processing has various components and steps that help in identification, classification and contextualization of each term, clause, and paragraph:
AI can be used for a plethora of use-cases across the contract lifecycle, from contract request, authoring & review, negotiations & signing, compliance & risk management. The benefits accruing from these can be broadly classified into: