Contracts are the engine of a business. They contain mission and business critical obligations and opportunities for organizations, yet, firms continue to struggle to do something that seems simple: connect contracts to business value.

Consider the opportunities lost in a typical Fortune 1000 company that manages between 20,000 to 40,000 active contracts at any given time. At least 10 percent of contracts are misplaced, difficult to find, still in paper form, on a file share somewhere, or buried in an email attachment not managed.

Adding to the complexity, contracts are entering an organization through multiple channels—email with PDF attachments, fax and electronic files. Compounding the challenges managing contracts is the content, which is mostly unstructured, making it difficult to digitize and pull relevant information.

Contract inefficiencies can cause significant revenue loss for firms. According to research conducted through the University of Southern California Marshall School of Business, inefficient contracting causes firms to lose between 5 percent to 40 percent of value on any given deal. To overcome these challenges, legal professionals are turning to artificial intelligence (AI)-enabling technologies.

AI for Contracts

AI has become an umbrella term to describe various technologies that encompasses machine learning, intelligent automation and natural language processing. An area of AI that will productively augment legal professionals' day-to-day activity is content, or contract, analytics.

Content analytics technologies are designed to help organizations uncover business critical information contained in unstructured or semi-structured content, common in contracts. It does this by identifying and classifying metadata found in contracts—a capability only 20 percent of organizations effectively employ.

Document understanding of incoming information based on content and metadata is key to automating the document capture and classification process, and the foundation for enabling AI in contracts.

Fortunately, contracts provide a number of inherent properties to assist with organization of data. Most contracts are divided into segments, with headings, often major and minor (the X.0 and X.X levels) to better organize them. Contract clauses can be compared against other contracts, or against standards and best practices. And, content classification using extracted entities and metadata helps to organize the universe of contracts into a coherent overall structure.

However, the first step in harnessing the value of contracts is digitizing contracts, converting them into machine encoded text, extracting key contract metadata and entities, and classifying contracts based on their subject matter.

The foundational technologies enabling content analytics include:

Document capture technologies: Full text optical character recognition (OCR) technology digitizes documents, extracts relevant contract metadata and maps them into transactional applications such as contract life cycle management (CLM) systems and to enterprise content management repositories. Organizations can reduce error-prone and labor-intensive tasks associated with the capture, extraction and classification of large volumes of information. They can also accelerate the process of compliance with data classification, retention and compliance policies and regulations. Additionally, it delivers efficiency gains and enables organizations to improve the management of contracts.

Machine learning technologies: Intelligent capture is more powerful than OCR. It enables organizations to identify patterns in data collected, organize, preserve and protect data in a highly efficient and accurate manner. As a result, organizations can better mitigate risk, and leverage information as a strategic corporate asset.

There have been significant advancements in machine learning technologies, commonly referred to as robotics process automation (RPA). RPA consists of software robots that automate repetitive tasks between processes. It is designed to reduce costs of labor-intensive and error-prone business processes.

These AI-enabling technologies easily extract data and clarify the content of contracts. Companies can review contracts more rapidly, organize and locate large amounts of contract data more easily, decrease the potential for contract disputes and increase the volume of contracts it is able to negotiate and execute.

But firms cannot be satisfied with just organizing information. They need to take the next step and action that information to gain the final, highest level of content: meaning.

Gain Meaning from Contracts

Meaning in content can come from the subject matter within sentences and paragraphs, from the objects being acted upon, and of course from the overall context. Being able to derive at least some semblance of meaning—especially since even the best AI is still a long way off from matching human beings—opens up a number of potential use cases.

An emerging technology that delivers meaning from contracts is natural language processing (NLP). NLP helps to read and analyze textual information, infer meaning in context and determine which parts of the document are important by analyzing the co-occurrence of text and their relationships within and between documents. It's this document understanding that transforms content into intelligence.

It's time for firms to leverage AI with the use of contract analytics. There's no need to worry that it will replace lawyers. Alternatively, it will remove highly mundane, routine and labor-intensive tasks, enabling lawyers to focus on higher value work. The benefits are obvious. It will reduce transaction costs for automating highly repetitive processes such as client billing, and support analysis of fixed price versus hourly billing practices.

Finally, let's be honest, when it comes to identifying and extracting key entities and terms from agreements, who wouldn't want to use contract analytics to streamline the tedious contract review process?

 

Bruce Orcutt is Senior Vice President of Product Marketing at ABBYY, a global provider of content intelligence solutions and services. Bruce has a deep understanding of image capture, document imaging, mobile platforms, text analytics, and content extraction, and how to integrate those technologies to deliver a world-class experience for customers and users in Legal, Financial Services, Insurance, Transportation, Retail, and Manufacturing markets.