How Gen AI Is Actually Being Used By Lawyers
Whenever generative AI is being discussed the focus tends to be on theoretical use cases and or how one day AI will be able to do certain tasks performed by lawyers. But which tasks can AI actually do today? and which tasks does it hope to do. A lot of firms that are developing their own software also can't share this kind of information and then the general statement of 'AI is going to completely change the industry' is thrown around and we are all left asking ourselves how that is exactly. In this article I intend to demystify gen AI's actual use cases in the legal industry. I will focus on generative AI's use in top legal departments, law firms generally, and its use cases by legal services providers.
AI application by In-House teams
For in-house legal teams gen-AI has the potential to significantly impact the effort-to-impact ratio in the following ways:
- Summarizing legal documents.
- Asking questions about the document, aka querying information.
- Document review and analysis.
- Drafting documents.
- Comparing documents to reference materials.
- Comparing jury instructions to deposition summaries to determine if testimony meets all the elements of the law.
Challenges and Drawbacks
Custom UI – Customer retention due to complicated UIs is a major challenge. Companies can focus on offering supplemental training to their clients, or design user interfaces that are easy to use.
Prompt engineering – Most users don’t understand prompt engineering, or the importance of wording prompts in ways to get their desired results.
Increasing adoption – Many users aren't implementing the software even when it's readily available. Change must therefore be encouraged and the benefits of using the software must constantly be reinforced.
AI application by Law Firms
The use cases are geared to helping law firms internally and helping law firm’s clients separately. Law firm early adopters are focusing on use cases that can create the biggest impact out of the gate. For some firms, a test kitchen or sandbox approach, where people can experiment with as many different models as possible, has been the best way to get the most from lawtech and to think not just about what they can do right this moment, but about how they might change the way they operate in the future.
Use Cases:
- Summarization of legal documents.
- Legal research.
- Internal chatbots.
- Generative AI tools that argue opposing sides of potential issues to narrow down the issues in a case (generally, not involving specific client information).
- Data extraction and complex analysis.
- Analyzing deal documents.
- Creating first drafts of legal documents, for example contracts.
- Document review – at least as a first round of review.
Challenges and Drawbacks
Hunting for talent – law firms have to compete with FANG in order to retain talent.
Scaling major use cases across large firms is still a problem.
Cost – high price tags are still an issue, and particularly a barrier to building tools and models internally rather than buying. This may have some association with the talent hunting problem mentioned above. The tech talent that the legal field is able to attract charges based on what they would make at big tech firms – and the lack of tech skills in the legal field doesn't make this situation easier.
Overall, gen AI has the potential to revolutionize the legal industry by streamlining tasks, improving efficiency, and enhancing decision-making processes. While challenges exist, addressing them can unlock the full benefits of gen AI and shape the future of the legal profession.