Artificial Intelligence (AI) is reshaping how plaintiff law firms handle discovery, from efficiently reviewing massive document sets to identifying key evidence quickly. This article explores AI’s role in discovery, best practices for implementation and usage, and the ethical and compliance considerations.
AI’s Role in Discovery
AI has been playing a big role in the discovery process for some time, but it is important to differentiate between the “traditional” AI tools, and the newer “generative” technologies.
AI technologies like machine learning and natural language processing (NLP) help lawyers sift through vast volumes of electronically stored information (ESI) far more efficiently than manual review. Predictive coding – also known as Technology-Assisted Review (TAR) – uses AI algorithms to categorize and rank documents by relevance, drastically reducing the data set humans must read. By recognizing patterns and context, AI can highlight likely relevant documents, saving time and cost while improving consistency in review decisions. Beyond text documents, AI tools can search and analyze diverse data types (emails, chats, images, audio, video) to identify potential evidence. For example, AI-driven transcription can convert audio/video files into searchable text, making those contents accessible for discovery. AI’s contextual understanding can surface important information that simple keyword searches might miss. This leads to early case assessment advantages, where attorneys get a clearer picture of the evidence landscape sooner.
Generative AI’s Role in Discovery
The latest AI trend involves generative models (like large language models) that can aid discovery by summarizing documents, helping to brainstorm questions, and answering natural language queries about the document set. Unlike traditional TAR, generative AI can work “out of the box” without extensive training, accelerating certain discovery tasks - both for preparation and analysis. For instance, an AI assistant might rapidly draft a summary of key points in thousands of emails or perform conceptual searches to find documents related to a narrative or fact pattern. This broadens AI’s utility beyond classification into more analytical support, though these uses are still emerging.
Generative AI can work powerfully for plaintiff law firms, helping them through all parts of the discovery process. It can analyze, summarize, and even draft content—dramatically reducing the manual lift required for common litigation tasks. Here’s how generative AI can support each stage of discovery:
Propounding Discovery
Generative AI can help draft tailored, relevant discovery requests based on the facts of the case, without starting from scratch each time.
- Suggests initial interrogatories, requests for production (RFPs), requests for document production (RFDPs), and requests for admission (RFAs) based on the case narrative.
- Tailors discovery language to match jurisdictional rules and common case patterns (e.g., employment misclassification, slip-and-fall injury).
- Helps generate follow-up requests based on previous document productions or opposing party responses.
- Surfaces standard requests used in similar matters, making it easier to spot what’s missing or expand the scope of inquiry.
Responding to Discovery
Rather than relying solely on templates or reusing prior responses, generative AI can help teams quickly draft precise, case-specific discovery responses.
- Drafts preliminary responses to RFPs and RFAs based on case facts, documents, or notes provided.
- Raises objections grounded in relevance, privilege, or burden—reducing risk of boilerplate responses that don’t hold up under scrutiny.
- Summarizes responsive documents and suggests how to frame them in a response.
- Aids privilege review by helping spot potentially privileged content before production.
- Creates easy-to-understand requests for more information from clients and witnesses.
Preparing for Depositions
With hundreds or thousands of documents to review, generative AI can help attorneys focus on what matters most before taking or defending a deposition.
- Summarizes key evidence tied to a specific witness (e.g., emails, call logs, HR complaints, medical documents).
- Drafts custom deposition outlines based on themes or facts central to the case.
- Suggests potential lines of questioning tied to documents already produced or disclosed.
- Highlights inconsistencies or contradictions across previous statements and documents.
Analyzing Depositions
Once a deposition is taken, generative AI can speed up the review and help lawyers make the most of the testimony for motions or trial prep.
- Transcribes and summarizes deposition transcripts, flagging key admissions or credibility issues.
- Extracts timeline details, disputed facts, or names of key players mentioned.
- Suggests how a deposition might impact claims, defenses, or further discovery needs.
- Can generate snippets or key quotes to use in demand letters, mediation statements, or dispositive motions.
- Identify inconsistencies across testimony.
Best Practices for Implementing AI in Discovery
Successful use of AI in discovery requires more than just buying software – it demands thoughtful implementation. Here are key best practices to ensure accuracy, efficiency, and fairness:
- Educate and Train Your Team: Before rolling out AI tools, attorneys and litigation support staff should become familiar with how they work. You don’t need to be a data scientist, but understanding the basics (like what “predictive coding” means or how a model is trained) is crucial. Many AI failures are human failures in disguise – e.g. misusing the tool or misinterpreting its output – so invest in training sessions with the vendor or consultants. Maintain a human-in-the-loop approach, where attorneys guide the AI and review its suggestions rather than relying on autopilot.
- High-Quality Training (Garbage In, Garbage Out): When using predictive coding or any AI, the initial inputs largely determine the output quality. Making sure that you are using detailed, comprehensive, and relevant “prompts”, requests and messages to the AI will give you the best quality of response. Further, making sure that the underlying models are well-attuned to your practice area and case type will give a significantly higher output.
- Validate and Quality-Check Results: Verification is vital. Once the AI has done its job, perform quality control to ensure it hasn’t missed the mark. You should review all the answers and cross-refernce them back to your case documents (usually by referencing in-line sources proactively provided by the AI). Make sure that all output is reviewed to meet your ethical obligations as a legal professional (see more below).
By following these best practices, plaintiff firms can implement AI in discovery in a defensible, efficient manner. The overarching theme is augmented intelligence – leveraging AI to amplify human expertise, not replace it. As one expert noted, predictive coding and similar tools “augment the attorney’s own abilities”, catching patterns a human might overlook while leaving ultimate decisions in human hands.
Ethical Considerations and Compliance
Implementing AI in discovery comes with important ethical duties and compliance requirements. Lawyers must ensure that using AI aligns with their professional responsibilities under rules of competence, confidentiality, and fairness:
- Duty of Competence: Attorneys have an ethical obligation to be competent not just in law but also in the technology they use (ABA Model Rule 1.1). The ABA’s first formal guidance on AI (Formal Opinion 512 in 2024) emphasizes that lawyers using generative AI must understand the technology’s capabilities and limitations. This means adequate training or partnering with technical experts when deploying AI in discovery, so that errors or biases in the AI’s output are recognized and addressed. In short, a lawyer can’t blindly rely on AI; they must supervise and verify its work to meet their competence duty.
- Confidentiality and Privacy: When using cloud-based AI discovery tools, firms are entrusting sensitive client data to third-party platforms. Ethical obligations require lawyers to ensure client information remains confidential and secure. Best practices include vetting e-discovery vendors for robust security (encryption, access controls) and ensuring service agreements have enforceable confidentiality clauses. Some state bar opinions (e.g., New York, Pennsylvania) list steps like reviewing the provider’s security, getting breach notifications, and obtaining client consent when appropriate. Particularly with generative AI, which may involve sending data to external servers, lawyers should get informed consent from clients before using AI tools that expose confidential data to third parties.
- Avoiding Bias: AI systems can inadvertently perpetuate biases present in training data. In discovery, this could mean an AI tool might under-identify relevant documents if the seed set or training process is skewed. Lawyers must be alert to this possibility as part of their duty of competence and fairness. One safeguard is using diverse training inputs and performing quality checks on AI outputs to ensure nothing important is systematically being missed.
A lawyer may ethically utilize AI for discovery if making sure that all ethical obligations are met. By staying educated about AI, supervising its use, protecting client data, and being transparent with courts and parties, firms can harness AI’s benefits while upholding their professional duties.
Conclusion
AI’s integration into the discovery process offers plaintiff law firms significant benefits in efficiency, consistency, and the ability to unearth key evidence from mountains of data. Realizing these benefits requires adherence to best practices – from choosing the right tools, giving proper training, validation of AI output, and maintaining ethical standards of confidentiality and competence. In the end, AI is an augmentation, not a replacement. The most successful plaintiff teams are using AI to amplify their legal expertise – finding that sweet spot where machines do the heavy lifting of data processing, and attorneys apply wisdom and judgment to the refined results.
