The Ultimate Guide to Artificial Intelligence in Contract Lifecycle Management


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.

Contract Authoring Software
Contract Authoring Software


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 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.



What is Machine Learning (ML) - and how is it different from AI?

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.

Contract Authoring Software
Contract Authoring Software


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 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.



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.

Contract Authoring Software
Contract Authoring Software


Does AI Matter in Contract 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.



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 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:

  • Identification of contract types and mapping to 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)
Contract Authoring Software
Contract Authoring Software


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:

  1. Heuristics
  2. Support Vector Machines
  3. Artificial Neural Networks (ANN)
  4. Markov Decision Processes (MDP)
  5. Natural Language Processing (NLP)

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.



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.
Contract Authoring Software
What are the main benefits of using AI-powered CLM?

What are the main benefits of using AI-powered CLM?

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:

Managing at Scale:
The true power of AI is best appreciated when the number of contracts handled/reviewed increases. As an example, Zycus' AI-powered solutions have been tested on more than 100,000 contracts with quick search capabilities, meta-data advanced search, versioning, and more. This will be a hugely cumbersome and slow process if it is not AI-enabled.
Maintaining Consistency:
Human errors, combined with inconsistent clause usage across similar contracts, is one of the main reasons for increased complexity, especially when it comes to re-negotiations, regulatory change management, and renewals. By leveraging the enhanced meta-data extraction and clause comparison capabilities with AI-powered CLMs, Legal Counsels can have their team members focus on ‘high-touch’ activities, thus, enhancing overall department morale and costs.
Risk Mitigation:
Advanced AI-powered CLM solutions can identify Contract obligations terms as well as compliance/regulatory aspects, which are the first steps in Risk identification and management. With sufficient data sets, and learning algorithms, AI systems can proactively highlight risk, so that mitigation strategies can be implemented before it impacts a broader scale.
Enhanced User Experience:
An intuitive and de-cluttered user interface combined with seamless integration with 3rd party systems can help in the author/reviewer to process contracts quicker and more accurately. The collaborative features during the review cycle, where multiple stakeholders and teams would come into play, can also be enhanced with AI and suggestion algorithms. Yet another example would be in the contract request process, where say, the Sales team, can capture and request for contracts from the comfort of her CRM solution, or even Email client, without the need to go to a separate solution.

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