Chess, Not Checkers: Navigating Complex Litigation with AI and Tech Solutions

In our last post, we discussed considerations for data collection strategies in complex litigation and how they differ from strategies in typical litigation cases.

Historically, managing complex litigation has been complicated by overwhelming data volumes, inefficient workflows, and high costs associated with manual review and analysis. Legal teams have faced challenges in identifying relevant information within massive document collections, often requiring armies of reviewers to sift through material – a process prone to human error, inconsistency, and delays.

These challenges made tasks such as summarizing, prioritizing, and analyzing data extremely time-consuming, leaving less bandwidth for strategic planning and case development. These inefficiencies have not only driven up costs but have also limited flexibility, as teams struggled to adapt to evolving case needs or respond quickly to new findings.

Fortunately, technology is evolving rapidly to help legal teams address these challenges. Generative AI and other advanced technologies are transforming complex litigation by automating and streamlining workflows, enabling legal teams to categorize documents with remarkable speed and accuracy as well as identify patterns, connections, and key entities within datasets earlier in the litigation life cycle than ever before. In this post, we will discuss how the application of generative AI can help keep costs manageable and increase flexibility in your strategic approaches to complex litigation.

Leveraging Generative AI to Manage Complex Cases

Generative AI can revolutionize discovery by automating labor-intensive tasks such as summarizing, categorizing, and analyzing documents, which can significantly reduce costs and offer increased flexibility in handling even the most complex litigations. Here’s how generative AI can support various discovery tasks in complex litigations:

Categorizing and Organizing Data

You’re probably already aware that generative AI can be used to classify documents. But it goes way beyond a simple classification of responsive vs. non-responsive providing significant benefits in complex litigations.

• Automated Classification: GenAI models can categorize documents based on content, metadata, or language patterns, efficiently grouping key information categories. This is particularly useful to identify privileged documents and those subject to regulatory protection, as well as those with specific relevance to case issues.
• Flexible Re-Categorization: Case priorities can frequently shift in complex litigation. When they do, AI models can rapidly adjust to new categorization rules without the need for significant reprogramming. This flexibility supports changing strategic focuses, such as targeting different custodians or themes.

One caveat to consider regarding document classification and categorization is the demand for transparency in the process from opposing counsel, especially in complex cases where there can be multiple counsel representing different parties or a class of claimants. We continue to see this in cases involving predictive coding including cases here and here. Expect to see similar demands regarding the use of genAI, dictating a transparent and defensible classification process.

Summarizing Documents

You also probably know that genAI models can efficiently produce summaries of lengthy documents, highlighting key points, extracting dates, names, and other critical data points, saving considerable time compared to manual review. For large-scale document collections, this functionality enables legal teams to quickly assess the relevance of documents without diving into full content. AI-generated summaries can even be customized for different needs – such as creating an executive summary for senior counsel while providing a detailed analysis for your document review team.

However, the ability to summarize large documents to streamline decision making can (ironically) begin to degrade as documents get larger, as the summary may miss key facts or fail to include important information from the later pages. It’s important to apply a rigorous QC process to document summarization to ensure the summaries are consistently accurate.

Analyzing and Extracting Insights

GenAI can identify and track entities (people, companies, places) across documents, surfacing connections that may be pivotal to understanding the underlying story in a case. This helps uncover relationships and patterns that may not be immediately apparent in isolated document reviews. GenAI can also assess sentiment, detect anomalies, or surface tones of documents, which can be critical in understanding the context and intent behind communications.

Those insights can be captured earlier in the litigation, during Early Case Assessment (ECA), facilitating refined case strategies and better positioning for negotiations or alternative dispute resolution. Earlier insights lead to efficient and effective development of a successful strategy for your litigation.

Managing Costs and Scaling Flexibly

Litigation is expensive. Complex litigation can be exponentially more expensive. Here’s how genAI can help keep costs managed for complex litigation:

• Reduced Need for Manual Review: By taking over repetitive and low-value tasks, genAI reduces the need for extensive manual labor, keeping discovery costs manageable even when dealing with massive datasets. Here’s an example where Cimplifi used genAI to eliminate 250 review hours in a recent case.
• Scalable Resources: GenAI’s capacity to analyze large datasets in parallel allows legal teams to handle sudden increases in document volumes or complexity without needing to dramatically scale human resources.
• Improved Predictive Coding: GenAI can supplement traditional predictive coding techniques, making it easier to predict and prioritize relevant documents with higher accuracy.

Expanding Strategic Flexibility

Success in complex litigation is enhanced by maximizing your strategic options. Here’s how GenAI and other advanced technologies can expand your strategic flexibility:

• Continuous Learning and Improvement: As discovery progresses, genAI and predictive coding models can learn from reviewers’ actions and adapt accordingly, refining its categorization and analysis algorithms to better align with the case’s evolving needs.
• Enhanced Collaboration: GenAI-generated insights, summaries, and categorizations facilitate seamless collaboration among different team members, whether they are focused on case strategy, deposition prep, or trial.

Conclusion

In applying genAI and other advanced technologies, legal teams gain access to resources that support them in staying ahead of the data deluge and more effectively leverages document insights – empowering them to respond dynamically to the unique challenges posed by complex litigation. This enables teams to keep costs in check while maintaining strategic flexibility in how the litigation will be managed. That’s a win-win!

In our next post in the series, we will discuss the human element of balancing innovation with ethics and best practices to ensure human oversight in AI-driven processes for a defensible approach to complex litigation that also maximizes protection of sensitive data!

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