Summary - Law firms that embrace AI in 2025 will gain a strategic edge in eDiscovery, from early case assessment to data-driven negotiations. Cutting-edge firms are using AI to analyze modern data, optimize document review,...more
2/6/2025
/ Algorithms ,
Analytics ,
Artificial Intelligence ,
Data Management ,
Document Review ,
e-Discovery ,
Information Governance ,
Innovative Technology ,
Legal Technology ,
Machine Learning ,
Technology-Assisted Review
Most legal teams would say that reviewing the same documents multiple times across multiple matters—aka “repeated review”—is a costly but necessary part of eDiscovery. With legacy tools and methods, that may be the case. You...more
Advancements in artificial intelligence (AI) are raising questions and opportunities in every industry. AI capabilities like natural language processing, prediction, and content generation have taken massive leaps forward in...more
Similar matters often pull in the same documents for review during eDiscovery. Many legal teams default to manually reviewing these documents for each matter, but this is quickly becoming untenable....more
By now, legal teams facing discovery are aware of many of the common technology and technology-enabled workflows used to increase the efficiency of document review on a single matter. But as data volumes grow and legal...more
The March sessions of Legalweek took place recently, and as with the February sessions, the virtual event struck a chord that reverberated deep from within the heart of a (hopefully) receding pandemic. However, the...more
3/29/2021
/ American Bar Association (ABA) ,
Artificial Intelligence ,
Best Practices ,
Cloud Storage ,
Data Collection ,
Data Preservation ,
Data Privacy ,
Data Protection ,
Discovery ,
Document Productions ,
Document Review ,
Duty to Preserve ,
e-Discovery Professionals ,
Electronically Stored Information ,
Evidence ,
Federal Rules of Civil Procedure ,
Information Technology ,
Innovation ,
Law Firm Associates ,
Law Firm Partners ,
Legal Ethics ,
Legal Technology ,
Machine Learning ,
Metadata ,
Model Rules ,
Rules of Evidence