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Generative AI Legal Use Cases & Examples in 2024

Updated on Jan 3
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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People are concerned about the integration of generative AI systems into human jobs. The legal implications of this technology are also being debated. The economists at Goldman Sachs predict that 44 percent of legal work could be automated by AI (Figure 1), highlighting a potential shift in legal services and the roles of legal industry professionals.1

Figure 1. AI automation in industries

Source: Goldman Sachs Global Investment Research

However, more than its potential damages, generative AI promises to be beneficial for the legal system and lawyers. In this article, we explain the potential generative AI legal use cases and demonstrate some real life examples for its legal use.

What is generative AI?

Generative AI refers to a branch of artificial intelligence (AI) that focuses on creating new content, such as text, images, or audio, by learning patterns and structures from existing data. These advanced AI systems have the potential to revolutionize the automation of human tasks, as they can mimic human-like creativity and decision-making processes. 

The human-like AI generated content has led to various applications, ranging from generating written content to AI generated images. Notably, the automation of legal tasks, such as drafting contracts or legal documents, which has become increasingly relevant for corporate legal departments and many law firms.

In fact, the latest large language model by OpenAI, namely GPT-4, has advanced in reasoning abilities. GPT-4 is able to pass the bar exam successfully, scoring around 10% of test takers, indicating its potential utility for in house counsel.2

Figure 2. Exam results of GPT-4

Source: OpenAI

1- Compliance and regulatory monitoring

AI-powered systems can monitor evolving regulations and ensure that businesses remain compliant with relevant laws and industry standards, alerting them to any changes that may impact their operations.

2- Contract analysis and negotiation

AI tools can review contracts to identify important clauses, flag potential issues, and suggest revisions based on best practices, simplifying the negotiation process and reducing the risk of disputes.

3- Document drafting and review

Generative AI can assist in drafting legal documents such as contracts, wills, and pleadings, by using pre-defined templates and input data. It can also help review existing documents for errors, inconsistencies, or potential legal issues.

4- Due diligence

In corporate transactions, generative AI can help automate the process of reviewing large volumes of documents, identifying potential legal risks and issues, and generating due diligence reports.

5- Intellectual property management

Generative AI can help in the process of patent analysis, trademark searches, and infringement detection, making it easier for attorneys to manage their clients’ intellectual property portfolios.

Generative AI tools can expedite legal research by quickly searching and analyzing relevant case law, legislation, and secondary sources, enabling legal professionals to access pertinent information with ease.

Generative AI can power chatbots that provide basic legal guidance, answering common questions or directing users to appropriate resources or services, making legal assistance more accessible to the public.

Law firms have started to use AI softwares that incorporate LLMs like ChatGPT, which are customized for the legal system.

1- CoCounsel

For example, Casetext released the first AI legal assistant CoCounsel, which is powered by GPT-4. CoCounsel mentions that none of the client data are sent to the AI companies for the purpose of using them as training data. The assistant can automate many of the manual legal work, such as:

  • Reviewing documents
  • Preparing for a deposition
  • Summarizing legal documents

2- Harvey

Harvey is one of the generative AI platforms, using OpenAI’s latest LLMs that are fine-tuned for legal issues. It is open to the use of 3,500 lawyers in 43 law offices.3 Harvey functions similarly to the CoCounsel, in automating the similar legal tasks. 

Concerning data privacy, Harvey diligently addresses clients’ compliance requirements by anonymizing user information and removing data after a specified duration.4 Users have the option to request data deletion at any point in time.

Figure 3. AI vs human being performance 

Source: Goldman Sachs Global Investment Research

According to many benchmarks, generative AI tools are outperforming human performance in many tasks (see Figure 3 for an example). This raises concerns whether AI will replace human jobs altogether. 

Research by OpenAI and University of Pennsylvania indicates that approximately 80% of the American workforce may experience a minimum of 10% of their job tasks being impacted by the implementation of large language models.5 

Although generative AI has started to automate a lot of legal work, legal professionals state that it doesn’t seem possible from this point that AI will replace all human involvement in the legal landscape.6 On the other hand, with generative AI automating most of the time consuming manual tasks, they believe that lawyers will be able to tackle more unique and creative tasks about the legal issues.7

Besides the use of generative AI in legal issues, there are legal and ethical problems raised by the very use of generative AI, such as the copyright law concerns and intellectual property rights for AI generated works. If you want to learn more about these, you can check our detailed articles:

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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