The Evolution of AI in Legal Tech

What does AI actually mean, if anything? Artificial intelligence (“AI”) has become a buzzword that everyone seems to be talking about. As a legal professional, you may find yourself bombarded with information from vendors, leaving you wondering what exactly has changed in the realm of legal tech.

In this blog post, we explore the evolution of “AI” in contracting software. From the early days of rules-based systems to the rise of machine learning-based solutions, each stage has revolutionized how legal professionals approach critical tasks. Today, we stand at the forefront of a new era with generative AI promising to redefine contract-related workflows and enhance efficiency and precision.

Early 2000s: Rules-based systems

The journey began in the early 2000s with the advent of rules-based contract management systems like CobbleStone and Seal Software. These systems used predefined rules to identify specific contract terms and data points, significantly reducing manual review and enabling legal teams to focus on higher-value tasks. Rules-based systems were undoubtedly a game-changer, setting the foundation for further innovations in contract review technology.

Early 2010s: Machine Learning-Based Systems

In the 2010s, contract AI witnessed substantial progress, fueled by the integration of two essential technologies: Natural Language Processing (“NLP”) and Latent Semantic Indexing (“LSI”). NLP, a language comprehension tool, helped identify contract fields, streamlining search and data mining processes across vast collections of agreements. At the same time, LSI, a technique to uncover underlying themes in text, allowed for enhanced analysis and organization of contract data. These breakthroughs found favor among legal departments and law firms, with providers like Kira leading the charge with their comprehensive capabilities.

Companies like Ironclad, Kira, LinkSquares, and Evisort introduced cutting-edge solutions that harnessed machine learning algorithms. These systems could extract data from unstructured contract text and tag clauses, greatly enhancing contract management efficiency and minimizing human errors. To position themselves as end-to-end contracting solutions, these companies also introduced rules-based contract workflow solutions to help automate the approval and signature process, adding more transparency to contract workflows.

Early contract AI solutions offered significant benefits, but a strong return required substantial investment in implementation and professional services. As contract analysis needs evolved with changing regulations, market conditions, and disruptive events, the strategic value of contract data became evident. However, in dynamic landscapes, quick adaptability of AI models became crucial to unlock the full potential of contract data. Yet, these machine learning models were constrained by their training datasets, requiring extensive training and a representative dataset to be accurate. Consequently, these early ML-based companies faced challenges in building models with datasets that captured each client's language version effectively. As a result, clients who had initially embraced the promise of AI-enhanced capabilities found themselves disillusioned with the unmet expectations set by these companies.

Past eight months: Generative AI and LLMs

Today, we find ourselves in the age of generative AI, with large language models ("LLMs") at the forefront of this transformation. Equipped with advanced natural language processing and generation capabilities, these models excel at understanding and generating human-like text. What sets LLMs apart is their ability to learn from vast amounts of legal data, continuously improving their language comprehension and analysis skills.

LLMs belong to a distinct class of machine learning algorithms designed for language-related tasks. Within the broader machine learning umbrella, LLMs' functionalities encompass advanced NLP, enabling them to comprehend and process human language coherently and accurately. They also exemplify generative AI models, capable of generating new, contextually appropriate text like human language, making them valuable for tasks like contract drafting. Additionally, LLMs leverage transfer learning, undergoing pre-training on vast text datasets to grasp language patterns and semantics, allowing for fine-tuning on specific tasks.

In the context of contract review and management software, leveraging LLMs with unsupervised learning capabilities leads to a significant improvement in the user experience by eliminating the need for manual clause tagging and data analysis. Unlike traditional supervised machine learning approaches that require labeled data for training, LLMs can autonomously process and comprehend contracts without explicit input-output annotations. This automation streamlines the contract review process, reducing the burden on users to manually tag clauses for extraction and data analysis. By using unsupervised learning, LLMs can accurately identify and extract key clauses, critical information, and potential issues from contracts, providing users with a seamless and efficient contract review experience. Consequently, this enhanced user experience empowers legal professionals to focus more on strategic decision-making and higher-level legal tasks, while the software handles the labor-intensive aspects of contract review and analysis.

Modern LLMs have revolutionized contract review by autonomously assessing contracts, identifying potential issues, and even suggesting improvements. Their capabilities extend to handling contract due diligence, regulatory compliance, and legal research, all with unparalleled accuracy and speed.

With the power of generative AI, LLMs have become an indispensable tool for legal professionals seeking to streamline contract review processes and optimize outcomes. Their capacity to learn from the entirety of the internet allows them to read, write, and reason like a human, making them proficient in automating complex legal tasks, including contract analysis and drafting. As a result, legal professionals can harness the full potential of LLMs to enhance efficiency and precision in their work.

Uses of LLMs in Contract Review:

• Efficient Contract Analysis: LLMs can rapidly analyze large volumes of contracts, extracting crucial data points, and highlighting significant clauses, saving valuable time for legal teams.
• Risk Mitigation: By identifying potential risks and discrepancies within contracts, LLMs enable legal professionals to proactively address issues and ensure compliance with regulations.
• Contract Creation and Drafting: LLMs can generate contract templates and clauses, simplifying the drafting process and reducing the time and effort required to create agreements.
• Enhanced Due Diligence: With LLMs' comprehensive contract review capabilities, due diligence becomes faster and more reliable, helping legal professionals make well-informed decisions.
• Improved Accuracy: By minimizing human errors and inconsistencies, LLMs elevate the accuracy of contract review and reduce the likelihood of disputes and legal challenges.

Conclusion

As AI technology continues to advance, the potential of LLMs in contract review will only grow further. By harnessing the power of AI, legal professionals can unlock unprecedented insights, streamline processes, and stay ahead of the rapidly evolving legal tech industry. The future of contract review is undeniably promising with LLMs driving smarter and more effective legal practices.

If you’re curious to learn more about AI’s evolution and how Pincites uses LLMs to power contract negotiation, join the waitlist and we’ll schedule time to help you out.