Zycus recognized as a ‘Strong Performer’ in The Forrester Wave™: Contract Lifecycle Management, Q2 2023  Read More

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.



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:

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 many things:

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.



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.



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.



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:

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:

At Zycus, we have broadly classified AI-based CLM use-cases into three major segments:

2. Contract Search & Insights –
3. Compliance & Risk Management –


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:

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:



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:

Key Benefits of Artificial Intelligence in Contract Lifecycle Management

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.

Meet Merlin AI for contracts

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