Supporting Legal Use Cases with AI Equals Proven Benefits for Legal Teams

As AI becomes more commonplace in the legal world, it is important to examine its application to legal use cases and to distinguish between the use of analytics, automation, and AI, according to an article by Cimplifi. 

Not every use case involving analytics and automation incorporates AI, while AI uses machine learning and natural language processing (NLP). Machine learning, a subset of AI, enables computers to improve task performance by learning from data without explicit programming. Natural Language Processing (NLP) focuses on enabling machines to understand and generate human language. 

The article highlights these 10 use cases for AI that have exhibited proven benefits for legal teams. The first three are traditional legal use cases and the remainder involve data analysis to support discovery needs. 

1. Concept clustering: Uses AI through machine learning or NLP to automatically group similar concepts or documents based on characteristics or language similarities
2. Predictive coding: Applies machine learning to prioritize documents rapidly and is crucial for discovery.
3. Continuous multi-modal learning (CMML): Integrates various data models to enhance understanding, prediction, and decision-making.
4. Key entity analysis: Extracts important entities from data, such as people or organizations, using NLP and machine learning.
5. Timeline analysis: Identifies sequences of events and inconsistencies, employing NLP and machine learning.
6. Communication analysis: Detects patterns in communications using NLP and machine learning.
7. Cognitive analytics: Understands behavioral signals within data, employing AI techniques like machine learning and NLP.
8. Privilege analytics: Analyzes potentially privileged communications using NLP and machine learning.
9. PII analytics: Identifies personally identifiable information using NLP and machine learning.

There are unique requirements for contracts, resulting in contract analytics becoming a key legal use case. NLP, machine learning, and deep learning are used for the extraction, interpretation, and analysis of contract data. In addition, contract analytics provides analysis of the trends, patterns, and benchmarks, along with strategic insights that can guide decision-making.