The Ultimate Guide to Artificial Intelligence in Contract Lifecycle Management
Introduction to Artificial Intelligence in Contract Lifecycle Management
As with any topic that sounds overwhelmingly complex, confusing or concerning, there are 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 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.
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What is AI - and what it is not
Before we get deep into the topic of how AI and tech driven CLM solutions are disrupting legal operations, let’s first try to understand what Artificial Intelligence means.
Artificial Intelligence is the branch of computer science that aims to create intelligent machines. 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:
- In decision support and smart search systems,
- It has broadened applications into advanced semantic / language processing
- For 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.
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What is Machine Learning (ML) - and how is it different from AI?
Machine learning is many things:
- It is the technology behind the predictive text, and language translation apps
- It is behind the recommendation engine for the shows Netflix suggests to you, and how your social 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.
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What is the difference between Machine Learning and Deep Learning (DL)?
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 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 are only so effective, if the quality of data it uses to learn is sufficiently accurate and consistent.
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What is Deep Learning advanced AI?
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.
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Does AI Matter in Contract Management?
Simple answer – Yes!
At Zycus, we heavily use ML, and AI-based techniques like Natural Language Processing (NLP) and Semantic Analysis, for:
- Processing the digitized contract text
- Large meta-data extraction
- For computer vision and image processing
- Making sense of the various terms and clauses in contracts
- 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.
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What are the major AI-based application areas in Contract Management?
1. Contract Clause & Meta-data analysis –
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 the complex and unstructured processes
- Freeing up the time of 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:
- Identification of contract types and mapping existing organizational contract templates
- Extraction of major contract terms and metadata and classifying them into standard meta-information, or even the ability to train the system to search for a custom meta-data
- Identifying the major clauses from a contract and mapping these into clause categories, parsing each clause and matching with the existing clause templates, while understanding the degree of similarity between these two clauses. This is especially useful in 3rd party contract review.
- Normalization of terms and clauses, in-line with organizational templates and processes
- Clause suggestion including fallback and alternative clauses
- Assisted workflow management during Contract Request, Review and Negotiation processes
2. Contract Search & Insights –
- Sift through contracts at scale, and clustering based on various parameters including standard and custom metrics
- A quick search within contracts, and ability to find other documents where similar clauses/terms were used
- Ability to make bulk modifications across documents, after evaluating the impact on individual contracts
- Identify key metrics and KPIs of contract groups, and ability to come up with insights and anomalies automatically
3. Compliance & Risk Management –
- Extraction of obligations across different contract types and classification into different categories including financial, operational, etc.
- Identification and evaluation of various compliance and regulatory terms, with the ability to perform impact and sensitivity analysis based on different regulatory changes and scenarios.
- Risk assessment and risk mitigation strategies
- Integration to other CLM as well as up / down-stream solutions, including Enterprise Content Management (ECM) and Document Management Systems (DMS)
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What are the different AI / ML techniques used in Contract Management Solutions?
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:
- Heuristics
- Artificial Neural Networks (ANN)
- Natural Language Processing (NLP)
- Support Vector Machines
- Markov Decision Processes (MDP)
- 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.
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What is Natural Language Processing and what are the major components of NLP?
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:
- Morphological / Lexical: Structure of words and expressions
- Syntactic: Parsing, ordering of words to extract entities and to understand the syntax
- Semantic: Understand the meaning of entities, and to evaluate the inferred meaning with the real meaning
- Sentiment: Understand the opinions, emotions, and attitudes of the sentence, in terms of magnitude as well as polarity (positive/negative)
- Pragmatic: Understand the context of usage – when, where the parties in the discussion, etc.
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What are the main benefits of using Artificial Intelligence in Contract Lifecycle Management?
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: