AI is Redefining Contract Analytics
Contracts are the engine of a business. About 90 percent of organizational spending and investments are governed by the terms and conditions embodied in them. Organizations continue to be challenged in identifying and extracting value from their contracts.
A Harvard Business Review article found that while “contracting is a common activity, it is one that few companies do efficiently or effectively. In fact, it has been estimated that inefficient contracting causes firms to lose between 5% to 40% of value.”
The process of efficient identification, extraction and analysis of contractual clauses and business terms is essential not only to mitigate compliance risks in an increasingly rigorous legal and regulatory environment but also to ensure optimal utilization of trading agreements.
Traditional approaches are insufficient to meet evolving demands
The conventional approach to contract formation is designed “as protection against the possibility that one party will abuse its power to extract benefits at the expense of the other…economists call this the hold-up problem: the fear that one party will be held up by the other.” One particularly salient example is the inclusion of “termination for convenience” clauses in agreements that allows a party to terminate the contract without cause which may “create perverse incentives for suppliers to not invest in buyer relationships.”
Is there a better way to contract formation? This topic is the subject of an analysis by McKinsey & Company in their article: “Contracting for performance: Unlocking additional value.” The foundation of their analysis is that:
“The majority of organizations invest relatively limited resources in contract development and vendor management. In fact, across industries, total procurement operating expenses are typically less than 1 percent of total spending. By underinvesting in this way, companies are overlooking a significant source of value: suboptimal contract terms and conditions combined with a lack of effective contract management can cause an erosion of value in sourcing equal to 9 percent of annual revenues.”
A new framework for generating greater value from contracts
To remedy such inefficiencies one may consider a fresh approach to contracting based on an “environment of trust—one in which they are transparent about their high-level aspirations, specific goals, and concerns.” Within such a new framework, contracts ought to incorporate mutually agreed upon key performance indicators and periodic reviews of supplier performance relative to negotiated business terms (e.g. pricing, discounts, incentives). Moreover, monitoring supplier performance against negotiated business terms and industry benchmarks requires periodic review of contract terms, including matching supplier invoices against negotiated terms, identifying potential discrepancies in earned discounts, assessing supplier performance, and determining negotiating tactics in order to secure more favorable business terms.
Optimizing contract analysis with artificial intelligence
Given the sheer number of trade and supplier agreements that organizations need to manage at any given time, it is important to consider how advances in AI technologies may streamline contract review and analysis. AI based contract review accelerates and streamlines document analysis in the following areas:
- Contract Discovery: AI streamlines contract discovery and the migration of contracts from multiple channels to a system of record, which typically includes Contract Life Cycle Management and/or Content Management systems, to: a) identify and validate key terms, b) normalize entities into a system of record, and c) streamline tedious and labor-intensive linear contract review processes.
- Obligations and Compliance Analysis, such as the Accounting Standards Board (ASC 606) revenue recognition analysis. Developed jointly by the Financial Accounting Standard’s Board (FASB) and International Accounting Standards Board (IASB), the ASC 606 standardizes revenue recognition when public or private companies enter into contracts for the provision of goods and services.
- Evergreen Contracts: Gartner estimates that 60% of all supplier contracts automatically renew without buyer knowledge simply because the buyer does not initiate proper notice of termination. Many of these evergreen contracts have very specific requirements for cancellation, often with 30, 60 or even 90-day limits before the date of the automatic renewal by which a party must cancel. AI technologies are able to extract the start date, renewal date, notice period and any other requirements for cancellation and then connect those dates to a system of record to give the organization a fair chance to analyze whether to renew before the cancellation time limit expires.
- Matching Purchase Agreements and Invoices, that evaluate, identify and extract negotiated performance obligations from supplier agreements such as volume discount, rebates missed and other incentives, and match them to invoices received to help minimize the impact of contract leakage. Monitoring supplier performance enables organizations to optimize their supply chain and negotiate more favorable terms with preferred suppliers thereby reducing cost of goods.
AI technologies for contract analysis are powered by Natural Language Processing (NLP) technologies and algorithms. NLP helps 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 understanding that transforms content into intelligence:
Understanding the meaning of business-critical documents substantially impacts business-critical processes – the ones that are key for driving revenue, reducing risk, or both.
Originally published at https://blog.abbyy.com/ai-is-redefining-contract-analytics/