Artificial intelligence has quickly become one of the most talked-about technologies in the legal industry. From reviewing documents to identifying privileged communications and summarizing thousands of emails, AI is helping legal teams work more efficiently than ever before.
But with so many new terms – generative AI, machine learning, large language models (LLMs), predictive coding, natural language processing – it can be difficult to understand what AI actually is and how it fits into litigation support.
But you certainly don’t need to be a data scientist to understand the basics. Once you know a few key concepts, it becomes much easier to see how AI is changing eDiscovery, digital forensics, and managed document review.
What is Artificial Intelligence?
Artificial intelligence (AI) refers to computer systems designed to perform tasks that normally require human intelligence. Rather than simply following a fixed set of instructions, AI can recognize patterns, interpret language, identify relationships within data, and make predictions based on what it has learned.
It’s important to remember that AI doesn't “think” the way people do. It analyzes enormous amounts of information, identifies patterns, and generates responses based on probability – not reasoning or personal judgment.
In the legal industry, AI is best viewed as a tool that helps professionals process and organize information more efficiently, allowing attorneys and litigation support teams to spend more time on analysis and strategy.
What Does Machine Learning Mean?
One of the most common forms of AI used in legal technology is machine learning. Machine learning allows software to improve its performance by learning from examples rather than relying solely on rules programmed by a developer.
Imagine reviewing thousands of documents to determine which ones are relevant to a lawsuit. Instead of manually reviewing every file, reviewers can identify examples of relevant and non-relevant documents. The system learns from those examples and predicts which remaining documents are likely to belong in each category. This approach has been used in eDiscovery for years through technologies like predictive coding and Technology Assisted Review (TAR), long before generative AI became part of the conversation.
What is Generative AI?
Generative AI is a newer type of artificial intelligence that creates new content based on prompts provided by a user (for example, ChatGPT). Unlike earlier AI systems that primarily sorted or categorized information, generative AI can write summaries, answer questions, draft text, identify themes, compare documents, and explain complex information in plain language.
In litigation support, generative AI doesn’t create legal arguments or decide cases. Instead, it can help legal teams quickly understand large collections of documents by summarizing communications, identifying key issues, extracting facts, or highlighting important conversations that might otherwise require hours of manual review.
What is a Large Language Model (LLM)?
At the heart of many generative AI tools is a large language model, or LLM. An LLM is trained on massive amounts of text so it can understand how language is structured and how words relate to one another. This allows it to recognize context instead of simply matching keywords.
For example, a keyword search for “Apple” cannot tell whether a document refers to the technology company or the fruit. An LLM can often distinguish between the two by understanding the surrounding language. This ability to interpret context is one of the reasons AI is proving valuable during document review.
What is Agentic AI?
One of the newest developments in artificial intelligence is agentic AI (for example, Relativity aiR). Unlike traditional AI systems that respond to a single prompt or perform one task at a time, agentic AI is designed to carry out a series of connected tasks to help achieve a broader goal.
Think of it this way: If you ask a generative AI tool to summarize a document, it completes that one task. An agentic AI system, however, can be instructed to complete an entire workflow. It can gather information, analyze documents, identify relevant themes, flag items that require human attention, and organize its findings into a structured output, while still allowing a legal professional to review and guide the process.
In litigation support, agentic AI has the potential to streamline complex workflows across eDiscovery and investigations. For example, it can help legal teams organize large collections of electronically stored information (ESI), identify potentially relevant or privileged documents, summarize key communications, track progress across multiple review tasks, and surface issues that may warrant closer examination. By coordinating these interconnected activities, agentic AI can reduce repetitive administrative work and allow attorneys, litigation support professionals, and reviewers to focus on legal analysis and strategic decision-making.
While agentic AI represents an exciting advancement, it’s still intended to work alongside legal professionals, not independently of them. Human oversight remains essential to validate findings, apply legal judgment, and ensure that discovery obligations and ethical responsibilities are met.
What is Natural Language Processing (NLP)?
Natural language processing (NLP) is the branch of AI that allows computers to understand written and spoken language. Rather than treating documents as isolated words, NLP examines grammar, sentence structure, context, and relationships between ideas. This helps AI identify concepts that traditional keyword searches might miss, making searches more comprehensive while reducing false positives.
How is AI Different from Traditional Search?
For decades, eDiscovery relied heavily on keyword searches. Keywords remain valuable, of course, but they have limitations. People often use different words to describe the same concept, misspell names, or communicate using abbreviations and slang.
AI adds another layer of intelligence. Instead of looking only for exact matches, AI can recognize similar ideas, identify related concepts, summarize conversations, and group documents discussing the same topic, even when they don’t contain identical words. This allows legal teams to uncover relevant information that might otherwise remain hidden.
How is AI Used in eDiscovery?
eDiscovery is often where legal professionals encounter AI first. The purpose of eDiscovery is to identify, collect, review, and produce electronically stored information (ESI) that may be relevant to a legal matter. AI helps make this process faster and more manageable by reducing the amount of manual effort required to analyze large document collections.
Depending on the technology being used, AI can:
- Summarize lengthy documents or email threads
- Suggest whether documents are relevant to a matter
- Identify communications that may contain privileged information
- Recognize personally identifiable information (PII) or sensitive data
- Group similar documents together
- Detect duplicate or near-duplicate documents
- Highlight unusual patterns or conversations that deserve closer review
Instead of replacing legal review, these capabilities help reviewers prioritize where to spend their time.
How Does AI Support Digital Forensics?
Digital forensics focuses on collecting, preserving, and analyzing electronic evidence in a legally defensible manner. AI is not typically responsible for collecting evidence – that remains the job of trained forensic professionals – but it can assist investigators after data has been collected.
For example, AI can help identify unusual user activity, organize evidence by topic, recognize communication patterns, and surface potentially important artifacts from large datasets. This allows investigators to analyze digital evidence more efficiently while maintaining the integrity of the forensic process.
AI and Managed Document Review
Document review has traditionally been one of the most time-intensive phases of litigation. AI helps reviewers work more efficiently by organizing documents, generating summaries, suggesting coding decisions, and identifying relationships between documents before human reviewers begin their analysis.
Rather than reading every document from beginning to end, reviewers can focus their attention on validating AI-generated insights, resolving difficult legal questions, and making judgment calls that require legal expertise. The result is often a review process that’s faster, yet maintains quality and defensibility.
What AI Cannot Do
Although AI is becoming increasingly capable, it’s definitely not a substitute for legal judgment. AI cannot determine legal strategy, understand every nuance of a case, interpret evolving case law, or make ethical decisions. Like any technology, it can also make mistakes if information is incomplete or ambiguous. For that reason, successful AI-assisted litigation support combines advanced technology with experienced legal professionals who validate results and ensure discovery obligations are met.
Understanding AI in the Legal Industry
AI is transforming litigation support by helping legal professionals manage growing volumes of digital information more efficiently. Understanding a few core concepts, such as machine learning, generative AI, large language models, and natural language processing, makes it easier to understand how these technologies fit into everyday legal workflows.
Whether it’s organizing evidence for a forensic investigation, accelerating eDiscovery, or streamlining managed document review, AI is proving to be a valuable tool for reducing repetitive work and helping legal teams find important information faster.
As AI continues to evolve, organizations that understand both the technology and the legal process will be best positioned to take advantage of its capabilities while maintaining the accuracy, defensibility, and professional judgment that effective litigation support requires.
If you have questions about how Avalon uses AI, contact our experts today.
