AI

Navigating the World of AI and ML in Contracts: Pitfalls and Best Practices

The world of legal is slowly embracing digital transformation with more adaptation of new technologies like artificial intelligence (AI) and machine learning (ML). For contract management, this is good news; 9.2% of revenue is lost by organizations resulting from poor management of contracts. However, there are several misconceptions around AI and ML. Let’s explore a few myths and highlight best practices for in-house counsels and legal teams looking to improve efficiency around their current processes.

Technology Is No Silver Bullet

A major pitfall that legal departments pursuing digital transformation often face is setting unrealistic expectations for technology, often overestimating its capabilities. First, when AI is introduced in a workplace, there’s often a stigma stemming from a fear that AI will replace employees’ jobs. On the contrary, for in-house counsels, the purpose of AI is to support lawyers and legal teams, increasing productivity and allowing professionals to focus on more strategic projects by eliminating repetitive and routine tasks. Additionally, many stakeholders are hesitant to adopt new because they don’t how it will affect their personal work environment, which leads to slower adoption rates.

Ultimately, driving change in an organization often depends on executives and technology vendors, who can help change the company’s or users’ perspectives. Their emphasis should be on informing how AI supplements, instead of hinders, the day-to-day needs of an enterprise.

Another common myth associated with AI and ML is what’s referred to as a “silver bullet.” Legal teams often fall victim to the belief that as soon as a new technology is introduced, the results will be instantaneous. Even though there is an aspect of standard use out of the box, some degree of training is required – it’s referred to as machine learning for a reason. The output received from AI depends on the inputs offered during the learning process.

AI and ML will not take over all aspects of legal jobs, nor will it be an instant fix to contract inefficiencies. But putting in effort in the beginning will ensure resulting success.

Set the Foundation for Success

It’s wise for legal departments to build an internal protocol or playbook for contracts. Organizations should assign one point of contact to run this process, and then work to identify the contract types that are process-driven. Starting an AI implementation by separating contract types into buckets, such as standard templates or third-party contracts, is beneficial as it allows for the identification of patterns during the training process.

Once patterns are established, teams should process the contracts to identify if the output meets their requirement. If not, then additional training should be provided to the machine. Many companies cut costs at this stage and forgo identifying the scan quality of legacy contracts. However, if the document scan does not meet a certain standard of clarity, AI output will be affected. As a result, receiving hands-on training around AI processes is useful to understand the system and set the company up for success.

New and Improved Contract Management

In-house legal teams are incredibly intelligent, so it is essential to utilize contract management technology that’s just as savvy. When trained correctly, AI and ML are able to recognize and extract certain clauses across a variety of contracts based on just a small sample set. This helps organizations quickly identify relevant contracts and expedite the review processes. Using AI does not eliminate jobs, but instead allows legal teams to cut down on daily minutiae and focus on more important aspects of business.

About Sarvarth Misra

Sarvarth Misra
Sarvarth Misra is a leading legal innovator with extensive experience in the legal technology and legal services sector. As co-founder and CEO, Sarvarth is responsible for ContractPodAi’s development, strategy, and growth. Sarvarth has led the conceptualizing, designing, and development of ContractPodAi and continues to lead all new innovation initiatives, new routes to market and its commercialization strategy.

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