Legal departments face operational challenges and expectations and are also tasked with delivering great service in a timely manner, all while reducing legal risk to the business. A core responsibility of the legal department is to manage clients’ contractual obligations, and if that process is not performed optimally, it can lead to lagging sales cycles, unmitigated risks, and many more problems for the business.
Recent advances in artificial intelligence showcase powerful applications of deep learning and similar technologies in a host of sectors, yet do not show any real promise toward “General AI” or technology that thinks and learns as humans do. Rather, we are, as the formidable Chinese thought leader Kai Fu Lee notes, in the “age of implementation.” Companies in industries ranging from insurance to health care to fashion are joining in. Contract Management has also gone all-in.
Businesses that continue to use outdated and analog processing are only limiting the full potential of the organization’s long-term goals. Fortunately, the market appears to understand this point: According to a report from McKinsey, by 2030, 70% of companies will have adopted at least one type of AI technology to improve efficiencies, in comparison to 33% today.
The proliferation of unstructured text and changing legal and regulatory landscapes are putting ever more pressure on already-strapped legal departments to balance business-critical needs with time-consuming—yet essentia—tedious routines. Automating rote processes guarantees legal teams handle documents securely and efficiently.
Issues With Inefficient Contracting
Corporate counsel can never find enough time in the day. While contracting is a common activity, it is one that few companies manage efficiently or effectively, which often triggers a chain of deleterious events beginning with slower sales cycles, moving into “contract leakage” and at times culminating in litigation. In fact, 80% of U.S. civil litigation is related to contracts and one-third of corporate profits are spent on litigation.
In fact, the IACCM (the Global Contract Management Association) has estimated that inefficient contracting causes organizations to lose more than 9.2% of their annual revenue. Adding to the challenge is that when contracts are mismanaged as a result of poor governance, it can cost a company 5% to 15% of the contract’s overall value.
The main challenge firms face in contracting arises from the sheer number of contracts they must track, which in turn often lack uniformity and are difficult to organize, manage, and update
How AI Can Help
Contract lifecycle management (CLM) solutions grew out of the realization that contracts have long, often-changing lives. Static contract management systems forget the ebbs and flows—increases in obligations, opportunities to increasing pricing, important deadlines—and that leads to costly contract leakage, or the failure of a party to obtain the full benefit of its contractual bargain.
CLM forces standardization throughout the process, including the way in which contract language and metadata are stored and managed. Clause and contract template libraries provide standard boilerplate language for users to initiate new contracts. Users can even leverage wizards to dynamically build a contract based on their own paper with minimal risk.
But the promise of CLM historically failed to address two critical gaps in contracting: (i) when an organization decides to use a contract proposed by the other party—a “third-party paper” contract, and (ii) legacy contracts, which are the “tale of the tape” so to speak for the organization’s historical operations. Without addressing these two problems, no CLM will provide a holistic view.
The problem from the outset is that each organization has thousands, if not hundreds of thousands, of active legacy contracts that existed prior to implementing a new CLM solution. Rather than spending weeks or months analyzing existing contracts to glean insights that may be skewed at best (and inaccurate at worst), contract management AI can quickly extract relevant data, letting legal teams spend more of their valuable time on analysis, and less on investigation.
For example, Workspace Property Trust, a commercial real estate company that specializes in the ownership, management, leasing, and development of office space, recently harnessed AI to obtain comparable lease abstracts for an asset acquisition (a classic third-party paper problem) at considerable time and cost savings.
Prior to implementing AI, Workspace was faced with challenges associated with abstracting leases as it’s a laborious process and is most commonly done by attorneys. In the past, the Workspace team had always employed a third-party agency to do the lease abstracting. Even with a third party, however, abstracting the leases remained time-intensive and expensive.
By implementing AI, the Workspace Property Trust team can now identify the fields needed to abstract key information from leases. The technology can perform the lease abstracting significantly faster and less expensively than a third-party company.
It’s not uncommon for businesses to undergo unanticipated and unavoidable situations. Whether it’s changes to the financial department, internal policy, or even changes in the law, it’s important for a business to adapt quickly and manage these changes securely.
We’ve seen a veritable “perfect storm” of unanticipated change recently with events such as Brexit, the new lease accounting rules, CCPA, and GDPR. Technology solutions, especially AI, help businesses navigate these turbulent times, providing quick insights to assess contractual impact across the organization.
Legal departments are often slow to implement the adoption of AI solutions, because the process of transitioning any business to a new technology ecosystem can be daunting. But it is vital to identify the tools best suited for the company needs and make sure internal teams are informed on the value this brings to the company.
Automation is changing how organizations do business, and legal teams in particular have much to gain by introducing machine learning and AI into the process of contract management.
This column does not necessarily reflect the opinion of The Bureau of National Affairs, Inc. or its owners.
Jason Gabbard is the head of AI strategy at Conga. He utilizes his experience as an experienced lawyer and technologist to spearhead Conga’s AI strategy.