Understanding Legacy AI Contract Review Software
Before the advent of advanced language learning models (LLMs) like ChatGPT, the landscape of AI contract review tools was markedly different. Legacy systems, which many businesses adopted, used methodologies that, while innovative at the time, presented several limitations in helping legal professionals to review contracts as part of their day-to-day job. This section delves into the nature of these tools, their original intent, and why they fell short in meeting the evolving needs of modern contract management.
The Basis of Legacy AI Tools
Legacy AI contract review tools were primarily built on two foundational technologies:
- Local Self-Trained Language Models: Unlike the more sophisticated LLMs used today, these early models were trained on a limited scope of data, often proprietary to the specific company or legal department. This training approach restricted the model’s understanding to a narrower context, which could not easily adapt to the diverse nuances encountered in varied legal documents.
- Rule-Based Analysis: This method relied on a set of predefined rules or scripts to analyze text. While rule-based systems were good at identifying specific, anticipated issues, they lacked the flexibility to understand context or adapt to new, unscripted scenarios that are common in contract negotiations.
Originally, many of these legacy tools were developed for legal due diligence (DD) — a process that involves reviewing a large volume of documents to identify potential legal risks associated with business transactions, such as mergers and acquisitions.
The focused nature of legal DD, with its need for pinpointing specific risk factors, made it a suitable application for early AI tools. These legacy tools, built before the new LLM boom, could efficiently scan large datasets to find particular items dictated by their programming.
Shortcomings in Everyday Contract Review
However, the day-to-day realities of contract review present a different set of challenges:
- Variability and Nuance: Daily contract review involves a wide variety of documents and clauses, each with its unique context and subtleties. The less sophisticated AI of legacy tools often failed to grasp these nuances, leading to less reliable outputs.
- Adaptability: Contract terms and legal norms evolve, and the static nature of early AI systems meant they couldn’t easily adjust to new types of agreements or updated legal standards without significant reprogramming.
- Depth of Analysis: While adequate for identifying clear-cut issues, legacy tools struggled with the depth of analysis required for comprehensive contract review. They were not equipped to handle complex reasoning or to provide contextually appropriate suggestions for contract modifications.
How have legacy tools worked for in-house legal teams and legal professionals?
Despite being aggressively marketed for broad contract review purposes, legacy AI tools were not ideally suited for the task. Their limitations in adaptability, understanding of context, and depth of analysis often meant that they could not fully meet the demands of dynamic contract environments. With the development of more advanced LLMs, such as GPT4, the capabilities of AI in contract review have significantly expanded, offering more nuanced understanding, greater adaptability, and a deeper analysis that legacy tools could not achieve.
Looking Ahead – there is a bright future in automating contract analysis with Generative AI
As we move forward, the evolution from legacy tools to more sophisticated AI-powered systems highlights the importance of choosing the right technology that aligns with the specific needs of contract review. The continuous advancements in AI are set to further revolutionize this field, making contract lifecycle management more efficient, accurate, and aligned with modern legal practices.
