Documate is now Gavel! Read more about why we’re excited about this rebrand.

AI Explained: How Law Firms Are Using AI Today

AI Concepts Explained
How Are Law Firms Using AI Today?
What Risks and Safeguards are Necessary?

AI Explained: How Law Firms Are Using AI Today

AI Concepts Explained
How Are Law Firms Using AI Today?
What Risks and Safeguards are Necessary?

Table of Contents

AI can be used in law firms to automate and augment some of the lawyer's tasks, including document generation, discovery, citations, reviewing documents, and more. Read our guide to understand what AI is, and how law firms can use it.

Artificial Intelligence (AI) is increasingly becoming a vital tool in the legal industry, changing how lawyers and law firms handle their work. AI assists in automating routine tasks, improving eDiscovery, and aiding in the creation of legal documents. These advancements contribute to greater efficiency in legal practices, allowing lawyers to focus on more complex aspects of their cases. With AI, law firms are not only saving time and money but also enhancing the quality of their work product.

In this article, we will delve into what AI is and how law firms can leverage it to streamline their operations and improve their services.

What is Artificial Intelligence?

Artificial Intelligence (AI) is a branch of computer science focused on creating machines capable of performing tasks that typically require human intelligence. These tasks include learning, problem-solving, perception, and language understanding. AI systems are designed to handle complex operations, often mimicking or even surpassing human capabilities in specific domains. The development of AI has led to significant advancements in various fields, including the legal sector, where it offers innovative tools for automating and enhancing work processes.

Rules-Based Workflows in AI

One of the earliest forms of AI in legal practice is rules-based workflows. This approach involves creating a set of predefined rules that the AI system follows to make decisions or carry out tasks. In a legal context, this might include sorting documents, organizing case files, or identifying relevant laws and precedents. Rules-based AI is particularly useful for tasks that are consistent and have a clear set of guidelines or criteria. However, its reliance on predefined rules can limit its flexibility and adaptability to new or complex situations.

Machine Learning

Machine Learning (ML), a subset of AI, moves beyond rigid rules-based systems. It involves training AI systems to learn from data, identify patterns, and make decisions with minimal human intervention. In law firms, ML algorithms can analyze past case outcomes, legal precedents, and other relevant data to offer predictive insights, assist in legal research, or even forecast case results. Unlike rules-based AI, ML adapts and improves over time as it processes more data, making it more dynamic and capable of handling nuanced and evolving legal scenarios.

Natural Language Processing

Natural Language Processing (NLP) is another critical aspect of AI, particularly relevant to the legal industry. NLP enables AI to understand, interpret, and generate human language in a meaningful way. In the legal field, NLP is used for tasks such as analyzing legal documents, processing contracts, and even assisting in drafting legal briefs. By processing large volumes of text quickly and accurately, NLP saves time and reduces the likelihood of human error, making it an invaluable tool for lawyers and legal researchers.

The Role of Large Language Models (LLMs)

Large Language Models (LLMs), like OpenAI's GPT series, represent a significant leap in AI capabilities. LLMs combine aspects of machine learning and natural language processing to understand and generate human-like text. In the legal field, LLMs can be used for a variety of tasks, including drafting legal documents, conducting legal research, and providing preliminary legal advice. Their ability to understand context and generate coherent, relevant text makes them highly effective for tasks that require a deep understanding of language and legal concepts. LLMs can process vast amounts of legal data, learning from each interaction to provide more accurate and relevant assistance over time.

How Are Lawyers Using AI in Law Firms?

Document Automation

Document automation involves the creation of legal documents based on pre-set templates and rules. AI significantly streamlines this process. By inputting specific client information, AI systems can automatically generate customized legal documents such as contracts, wills, and legal notices. This not only saves time but also reduces the likelihood of human error. Additionally, AI can keep templates updated with the latest legal requirements and standards, ensuring that the documents produced are compliant and up-to-date. This is yet another area where AI is augmenting lawyers, rather than replacing them. Gavel Blueprint is an example of AI that is in the full control of the lawyer.

AI has transformed document automation in the legal field by making the drafting of legal documents faster, more accurate, and personalized to individual case needs. Using AI, legal professionals can automate the creation of complex legal documents such as contracts, wills, and legal briefs. This process begins with AI systems analyzing existing documents to learn the structure, language, and clauses typically used in specific types of legal documents. With this knowledge, AI-powered document automation tools can then generate new documents that adhere to legal standards and are customized based on the specifics of a new case or client requirement. This reduces the time lawyers spend on drafting documents and minimizes the risk of errors, allowing them to focus on more strategic tasks.

AI-driven document automation tools can adapt to the preferences and requirements of individual lawyers or firms over time, further refining the quality and relevance of the documents they produce. These tools often feature intuitive interfaces that allow users to input specific details or choose from a set of options to ensure the final document meets their exact needs. By automating routine drafting tasks, AI not only increases efficiency but also enhances the consistency and quality of legal documents across the board.

Examples of tools in this space include:

  • Gavel: Gavel's Blueprint allows lawyers to combine both traditional AI and generative AI to create powerful document automation. With Blueprint, legal professionals can leverage generative AI to create automated workflows from their existing document templates in minutes—enabling them to achieve immediate ROI from legal practice automation.
  • CLM Matrix: This tool helps legal departments automate the drafting and management of contracts, improving compliance and reducing the risk associated with contract management.

Legal Research

AI technology is increasingly used in legal research to streamline the process and make it more efficient. At its core, AI tools in legal research can sift through vast amounts of legal documents, cases, and precedents much faster than a human could. This means lawyers can quickly find relevant case law, statutes, and legal writings that are pertinent to their current cases. These tools often use natural language processing (NLP), enabling them to understand and interpret the context of legal texts, not just search for keywords. This can help in uncovering more accurate and relevant results.

For lawyers and law firms, AI in legal research also offers the ability to predict outcomes based on historical data. By analyzing patterns in past legal decisions and outcomes, AI can provide insights into how similar cases have been resolved. This predictive capability can be a powerful aid in strategy planning, helping lawyers assess the strengths and weaknesses of their cases with greater precision. Additionally, AI can assist in identifying legal trends and shifts in jurisprudence, enabling lawyers to stay ahead in their field.

Several tools have become integral to legal research and practice, thanks to advances in AI. Here are some notable examples:

  • LexisNexis and Westlaw: These are two of the most established and widely used legal research platforms. They've incorporated AI to improve search functionalities, making it easier for lawyers to find relevant case law, statutes, and legal commentary. Their AI algorithms offer predictive insights and help in legal analytics, providing an edge in legal strategy and decision-making.
  • ROSS Intelligence: This tool uses natural language processing to understand and answer legal questions. Lawyers can ask ROSS questions in plain English, and the system will retrieve relevant legal documents, case law, and secondary resources. ROSS Intelligence enhances legal research efficiency and accuracy.
  • Casetext: Casetext’s CARA AI uses machine learning to analyze legal documents submitted by users and then finds relevant legal texts, cases, and articles. This contextual search capability allows lawyers to conduct more thorough research in less time.
  • Blue J Legal: This platform applies machine learning to predict legal outcomes. It analyzes past case law and outcomes to forecast how a court might rule on specific issues or cases. This predictive ability helps lawyers in assessing case strategies and advising clients with a higher degree of confidence.
  • Ravel Law: Acquired by LexisNexis, Ravel Law offers advanced analytics, visualization, and AI-driven research tools that uncover valuable insights and trends in case law. Its technology enables lawyers to see how cases are connected, understand the precedent landscape, and identify patterns in judicial decisions.


AI plays a crucial role in the field of electronic discovery (e-discovery), where it helps lawyers and law firms manage and analyze large datasets during the legal discovery process. In e-discovery, AI technologies, particularly machine learning and natural language processing, are used to automate the review of documents and communications. This automation significantly reduces the time and manpower needed to sift through potentially millions of documents to find relevant information. AI tools can identify patterns, classify documents, and even predict which documents are likely to be relevant to a case, based on previous inputs and decisions made by human reviewers. This not only speeds up the discovery process but also enhances its accuracy by minimizing human error.

AI's ability to perform sentiment analysis and recognize specific legal concepts within documents further refines the e-discovery process. For instance, AI can differentiate between documents that are relevant to a particular legal matter and those that are not, even when the volume of data is enormous. This level of analysis helps lawyers focus on the substance of the case rather than getting bogged down in document review.

Several tools embody these advancements in AI for e-discovery:

  • Relativity: Known for its user-friendly interface and powerful analytics, Relativity uses AI to improve the efficiency of data sorting, searching, and categorization in the discovery process.
  • Logikcull: This tool simplifies the e-discovery process with its cloud-based platform, offering features like automated document categorization and an AI-driven review process that helps in quickly identifying relevant case materials.
  • Everlaw: Leveraging AI, Everlaw offers features such as predictive coding and advanced analytics, enabling legal teams to streamline the review process and uncover insights from their data more effectively.
  • Reveal: With a focus on machine learning and natural language processing, Reveal provides an array of AI tools for data categorization, anomaly detection, and relevance ranking in e-discovery.

Due Diligence

In the context of due diligence, AI is revolutionizing the way lawyers and law firms conduct their investigations into corporate transactions, mergers, and acquisitions. AI technologies, particularly those employing machine learning and natural language processing, are instrumental in analyzing large volumes of documents to identify risks, obligations, and opportunities in potential business deals. These tools can quickly sift through contracts, financial records, and other pertinent documents to flag clauses, terms, or patterns that may warrant closer examination. This capability not only accelerates the due diligence process but also enhances its thoroughness by ensuring that critical details are not overlooked.

AI-driven due diligence tools are designed to automate and streamline the identification of potential legal and financial liabilities. For example, they can detect non-compliance issues, intellectual property concerns, and inconsistencies in financial statements with greater accuracy than manual reviews. This level of analysis allows lawyers to provide more informed advice to their clients, reducing the risks associated with business transactions.

Some notable examples of AI tools in this space include:

  • Kira Systems: Kira is tailored for contract analysis, helping firms to extract and analyze information from contracts and other documents quickly.
  • DiligenceVault: This platform offers a digital due diligence solution that caters to asset managers and investors, streamlining the collection and analysis of due diligence questionnaires.
  • Luminance: Luminance employs machine learning to improve the efficiency of document analysis for due diligence, mergers and acquisitions, and compliance reviews, adapting to users' interactions to provide tailored insights.

These tools exemplify how AI is being utilized to make due diligence faster, more accurate, and more efficient, fundamentally changing how legal professionals approach and manage the complexities of corporate transactions.

Document Management

AI significantly enhances document management within law firms and legal departments by streamlining the organization, categorization, and retrieval of documents. By leveraging machine learning and natural language processing, AI systems can automatically classify documents based on their content, context, and relevance to specific cases or legal matters. This automation not only saves time but also reduces the likelihood of human error in filing and retrieving critical legal documents. Furthermore, AI-driven document management systems can recognize patterns and suggest relevant documents for current cases, improving the efficiency of legal research and case preparation.

Beyond basic organization, AI tools in document management also feature advanced search capabilities that allow lawyers to query vast databases using natural language. This makes it easier to find specific documents or information without needing exact keywords or phrases. Additionally, some AI systems offer predictive analytics, suggesting documents that might be relevant to ongoing cases based on previous interactions and searches.

Some key examples of AI-powered document management tools include:

  • iManage: Leveraging AI, iManage improves document and email management by offering intelligent and secure ways to find, share, and govern corporate documents and communications.
  • NetDocuments: This is a cloud-based document and email management service that uses AI to help law firms and legal departments organize, manage, and collaborate on documents securely.
  • DocuSign: Known for its electronic signature technology, DocuSign also incorporates AI to streamline document management processes, making it easier to prepare, sign, act on, and manage agreements.

Litigation Analysis

Litigation analysis is crucial for developing legal strategies and advising clients on the likely outcomes of their cases. AI can analyze past case law, court decisions, and judge rulings to predict potential outcomes of current cases. This predictive analysis helps lawyers in making informed decisions about whether to settle or proceed with litigation. AI can also assist in identifying litigation trends and patterns, which can be invaluable in preparing for court proceedings.

Considerations, Challenges, and Risks to Be Aware When Using AI For Your Law Firm

Lawyers integrating AI into law firms brings not only opportunities but also a range of challenges, considerations, and risks that must be carefully navigated. From ethical dilemmas to data privacy concerns, understanding these complexities is vital for law firms adopting AI technologies.

Ethical Considerations

The use of AI in legal practice raises several ethical questions, which can be broadly categorized as follows:

  • Bias and Fairness: AI systems, particularly those involving machine learning, can inadvertently perpetuate and amplify biases present in their training data. This can lead to unfair or discriminatory outcomes in legal advice, decision-making, or e-discovery processes.
  • Transparency and Explainability: Many AI systems, especially those using complex algorithms, are often seen as "black boxes" due to their lack of transparency. Understanding how an AI system arrives at a conclusion is crucial for maintaining accountability and trust in legal processes.
  • Dependency and Diligence: There is a risk of over-reliance on AI tools, which can potentially erode the professional judgment of lawyers. Maintaining a balance between leveraging AI and exercising independent legal judgment is essential.
  • Client Confidentiality: Ensuring that AI systems do not compromise client confidentiality is paramount. Lawyers must ensure that the use of AI adheres to professional standards regarding the protection of sensitive client information.

Data Privacy Issues

  • Handling Sensitive Data: Law firms deal with highly sensitive data, and AI systems require access to vast datasets for training and operation. Ensuring that this data is handled securely and in compliance with privacy laws and regulations is a significant challenge.
  • Data Breach Risks: The use of AI increases the volume of data stored electronically, which can elevate the risk of data breaches. Implementing robust security measures to protect against unauthorized access or cyberattacks is crucial.
  • Compliance with Data Protection Laws: Law firms must navigate complex data protection laws, such as GDPR in Europe or various state laws in the USA. Ensuring that AI systems comply with these regulations is essential to avoid legal and reputational risks.

Education and Training

  • Understanding AI Capabilities and Limitations: Lawyers and staff need to be educated about the capabilities and limitations of AI technologies. Understanding what AI can and cannot do is critical for effective and ethical use.
  • Training in AI Use: Proper training in using AI tools is necessary to maximize their benefits while minimizing risks. This includes understanding how to interpret AI-generated insights and when to rely on human judgment.
  • Staying Updated with AI Developments: The field of AI is rapidly evolving. Continuous learning and staying abreast of the latest developments in AI technologies and their legal implications is necessary for law firms.

Liability and Accountability

  • AI Decision-Making: Determining liability for decisions made with the aid of or by AI systems can be complex. Establishing clear protocols for AI use and decision-making is necessary to manage liability risks.
  • Accountability for AI Errors: If an AI system makes an error (e.g., overlooking a crucial legal precedent), determining who is accountable—the AI developer, the law firm, or the individual lawyer—can be challenging.

Client Perception and Trust

  • Building Client Trust: Clients may have reservations about the use of AI in handling their legal matters. Transparent communication about how AI is used and its benefits can help in building trust.
  • Managing Expectations: It’s important to manage client expectations regarding what AI can achieve. Overpromising the capabilities of AI can lead to dissatisfaction and trust issues.

Further Resources on AI for Lawyers

While the journey of integrating Artificial Intelligence into legal practices presents various challenges and considerations, it is a path worth traversing for forward-thinking law firms. Addressing ethical issues, safeguarding data privacy, investing in education and training, and understanding liability aspects are fundamental to harnessing AI's potential responsibly and effectively.

As we've explored, AI in the legal sector is not just about adopting new technologies; it's about adapting to a new paradigm in legal services. The benefits – from streamlined legal research and document management to enhanced e-discovery and due diligence – are transformative. However, the journey is not without its hurdles. Ethical considerations, such as ensuring fairness and transparency, are paramount. Protecting client data and adhering to privacy laws is critical, and fostering an environment of continuous learning and adaptation is essential for both lawyers and their staff.

For law firms willing to embrace these challenges, the rewards are significant. AI can lead to more efficient processes, cost savings, and improved accuracy in legal work. It enables lawyers to focus on the more nuanced aspects of their practice, such as strategy and client relationships, while AI handles the heavy lifting of data analysis and routine tasks.

We understand that navigating the world of AI can be daunting. That's why we encourage lawyers and law firms to refer to our comprehensive article on AI tools in the legal industry. This article provides an in-depth look at specific AI applications and tools, offering valuable insights into how these technologies can be effectively integrated into your practice.


No items found.

Gavel Newsletter

Sign up for our newsletter to get product updates, exclusive client interviews, and more.

By clicking “Accept”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.