Contract analytics and AI are the future of corporate procurement
“You are at the forefront, the center of a revolution of how we do business.”
So said Arianna Huffington, renowned author, businesswoman, and former President and Editor-in-Chief of The Huffington Post, to an audience of supply management leaders, procurement practitioners, and solution providers, at the Institute for Supply Management’s 103rd annual conference, recently.
There is no doubt that procurement is changing. The function used to be about acquiring products and services for the best price. Now, it’s defined by information management and the ability to give executives the analytics and guidance they need to make better business decisions.
The industry is experiencing a wave of digital disruption, picking up where process automation began two decades ago and continuing with end-to-end digitization and transformation. Procurement professionals now play a strategic role in their organization. Beyond simply controlling expenses, they are now also expected to manage legal and regulatory exposure, while providing valuable intelligence to key stakeholders.
This, in turn, has led to new challenges, such as assessing risk with a clear understanding of complex documents, eg. service level agreements (SLAs), and driving efficiency by reducing the time and money it takes to search for specific contracts and terms. It has become mission-critical that CPOs and other executives have a clear view into contracts to increase savings by rationalizing suppliers, negotiating better deals and taking advantage of incentives.
Moreover, reducing company spend by centralizing and controlling an organization’s contracts is an essential component of expenses management, and requires reducing rogue spend and the number of vendors a company works with.
Applied analytics, driven by advanced methods in artificial intelligence (AI) is an increasingly popular route to the information held in contracts. Intelligent content analytics integrated with e-procurement, sourcing and contract lifecycle management helps to reduce cost leakage, increase overall savings on purchases, monitor risk exposure and enhance supplier visibility. Achieving any of these objectives can be difficult, and all the more so in organizations where contracts are hard to find, and the data they contain is even harder to extract, understand and manage.
Information management leaders, procurement practitioners and other executives with influence over, or participation in, an organization’s contracting process and transactions, are increasingly tackling complexity and deeper, multi-tier relationships. On both, the debit and credit sides of the general ledger. Not to mention, third-party non-customers and non-suppliers. This has made contract management a core competency, critical to, not only driving performance, but also to contain risk.
Industry use cases indicate that even sophisticated organizations, have 20 to 50 percent more open contracts than they assume they have. Yet only a minority of those firms have the bargaining power to force all contracts onto their own paper. In the case of procurement functions, contracts are managed on factors including commodity and contract value, which results in a distributed approach where contract fragmentation ensues. Organizational fluidity, with business units coming and going, accelerates the scattering of contracts, and multi-year or evergreen contracts contribute even further to the challenge.
Outdated Technology Still in Use
While there is now clear recognition of technology as a tool for gaining visibility into contracts, the traditional approach to on-boarding contracts into a software solution relies on scanning hard copy content (often tagging scans with metadata entries along the way). That process is followed by extensive manual work to extract further metadata from the images.
As a result, the process has become focused on capturing only the bare essential data points, and from only immediately available contracts. Follow-up enhancement work is neglected, carried out in an ad hoc manner by a few individuals, or omitted as a budget-saving measure. The process’ inherently extended time frames not only jeopardize both short and long-term opportunities, but also the quantification of this risk.
Select contract discovery and analytics platforms employ advances in artificial intelligence (AI), including machine learning and natural language processing methods, to provide visibility into all contracts with required levels of metadata, in a structured, commonly accessible format, and integrates with related software infrastructure.
For example, an enterprise company with a large number of vendor contracts must keep track of variations in damage and termination provisions in their own contracts and in vendor contracts. Organization and proactive management are required for the large undertaking of managing variations.
The best AI contracting software can record and help standardize these provisions in the company’s contracts, making it far easier to identify instances of noncompliance and ensure that unfavorable provisions are dealt with promptly.
The right technology solution can open up a cost-effective way to rapidly bring all contracts in the purview of procurement officers, legal teams and information managers, under effective management, without requiring the resources traditionally involved in manually rectifying the situation.
The Right Direction Moving Forward
To guide users in the right direction as they make daily procurement decisions, a comprehensive AI platform can be utilized for procure-to-pay and source-to-settle to avoid the negotiated-but-unimplemented loss of savings. It can also provide visibility to all relevant contractual fields (e.g. discount levels, delivery times, commitment levels in both quantities and dollars), allowing sourcing teams, in search of new opportunities, to leverage rich contractual metadata to identify contractually lagging or missing areas, in addition to chasing traditional under-sourced, fragmented or otherwise undermanaged spend.
An AI software solution that employs natural language processing also facilitates multi-tier contractual analysis using highly automated tools to collect and organize contract data, especially in direct-side procurement with strategic vendors. Procurement can significantly increase, not only a CPOs ability to approach risk management on an entirely new, contract-driven level, but can also begin to contribute to corporate initiatives, such as M&A, in new, value-creating ways which were not previously possible.
BPO/GPO and other procurement outsourcing firms also benefit from the ability of the chosen platform to facilitate rapid contract content acquisition, as their SLAs are closely tied to strict timelines. For legal process outsourcing (LPO) firms, automating the initial review process, with proven AI technology, allows them to stay relevant by focusing their value-add and investments on the inevitable second round of expert analysis and the intellectual value-add only humans can deliver. In fact, LPOs increasingly indicate that clients will no longer accept paying high billing rates for something technology alone can accomplish.
Flipping the traditional LPO on its head, the benefit to the business is a reduction in the reliance on external providers. Or if they are using them, a reduction in the amount of time an LPO requires to deliver the contract metadata back to their client. Either way, time can be reduced, as well as cost.
Procurement professionals deploying a CLM, CRM, or SIM tool need to give serious consideration to how contract data will be discovered and deployed into these tools. An automated approach can improve time to value, lower costs and increase the overall ROI of the broader deployment.
However, it’s important to understand what AI contract tools can and can’t do. There are many choices and a successful implementation hinges on applying the right tool to do the right job. The highest value will be found by corporations that have a high value of contracts and a procurement process that has become more complex as it grows.