Why Contract Data Is an Ideal Place to Start Using AI Tech

From the proliferation of legal AI tech innovation over the past two years, it’s clear that there is no shortage of what AI tech can and will do for legal work. But where do those capabilities actually live? How do early adopters access them today? The answer for many is a category of tools that has become a must-have for both private firms and in-house teams: contract lifecycle management software, or CLM.

CLM systems have been around for a long time, but, as with tech across virtually every sector today, they’ve all recently been turbocharged by AI. Many could already streamline stages of the contract process, from creation to negotiation, execution, and post-execution. But now, because AI uses ML and NLP to mimic human processes, a truly robust AI-powered CLM will do all that, plus help with:

• Contract ingesting and tagging. CLMs use AI to analyze, tag, extract, and report on contract data.
• Building standardized templates and assisting in negotiating, redlining, and reviewing contracts. AI-powered CLM tools can automatically locate problematic clauses within contracts before you send them out.
• Providing contract analytics insights to help organizations make data-driven decisions about their contractual relationships.
• Boosting operations and productivity by streamlining the contract creation, review, and renewal process.
  Why contract data is an ideal place to start using AI tech

There’s another reason why CLMs are ideal spaces to start with AI: they house contract data. And working with contract data presents an opportunity for legal departments to drive real, data-driven business impact through data locked in contracts—and serves as a perfect experimentation environment for five main reasons:

1. Universality

Contracts are one of the most ubiquitous business tools on the planet. Every department, within every company in the world, uses contracts.

2. Accuracy

By nature, contracts are highly accurate. Having been reviewed by teams of lawyers and stakeholders, for the most part, your contracts will be complete, accurate, and, more or less, final. \

3. Volume and repetitive structure

Organizations typically handle a large number of contracts, many of which share similar structures and clauses. This repetition creates a perfect learning environment for AI systems, and the structured format of most contracts also provides a consistent framework for AI algorithms to analyze.

4. Risk mitigation

Manual contract review is time-consuming and prone to human error, and unfavorable or risky terms can often hide in contract data. AI excels at uncovering problematic language, and at creating guardrails so employees don’t inadvertently introduce risk while drafting or editing contracts.

5. Customization opportunities

Contract data is a good place to get your feet wet with customization capabilities as most organizations usually have custom clauses, fields, and metadata properties that are pre-approved for use.

Building on sturdy foundations

The beauty of starting with contracts lies in their predictability. Their standardized language, consistent format, and inherent accuracy create an ideal environment for AI to prove its worth. As legal departments build experience with contract analysis, they establish a foundation for tackling more complex AI applications, all while delivering measurable results to their organizations.

The contents of this blog post come from Ironclad’s Legal AI Handbook. For more tips, tricks, and best practices, download your copy today.