AI revolution!?
AI is taking the world by storm and transforming the way we do business in every niche. The legal sector is no exception. AI has become a valuable asset to law firms worldwide in their day-to-day practices, from helping lawyers process a large amount of information to creating powerful first-response AI attorney assistants available 24/7 to the general public.
Since popular legal platforms like Westlaw have been using AI for a while now, almost all law firms have used some form of artificial intelligence (AI) at some point. However, the adoption of Generative AI in the legal sector is relatively new.
The main advantage of AI in law firms is its ability to save time. Tasks that once took hours or even days like research, drafting, reviewing documents, and entering data can now be done in seconds. With the help of AI the routine tasks can be automated and lawyers can focus more on higher-level, strategic work that directly benefits their clients.
To fully grasp the benefits AI can bring to the legal field let's step back a little and dive into what AI is all about.
What is artificial intelligence?
Artificial Intelligence (AI) is a branch of computer science that is focused on developing machines that can perform tasks that only humans previously could do. These tasks involve learning, problem-solving, perception, and understanding language. One of the first uses of AI in the legal field was through rules-based workflows. This method involves setting up specific rules that the AI system has to follow to make decisions or complete tasks. In a legal setting, this could mean sorting documents, organizing case files, or finding relevant laws and precedents. Rules-based AI is especially helpful for straightforward tasks that follow clear guidelines. However, its dependence on preset rules can make it less flexible and adaptable to new or complicated situations.
Machine Learning (ML) is a layer of AI, that goes beyond fixed rules-based systems. It involves teaching AI systems to learn from provided data, recognize patterns within, and make decisions with minimal human input. In law firms, ML algorithms can review outcomes of past cases, legal precedents, and any other relevant data to provide predictive insights, help with legal research, or even estimate case results. Unlike rules-based AI, ML adapts and improves as it processes more data, making it more flexible and better suited to handle complex and changing legal situations.
Natural Language Processing (NLP) is an important part of AI, especially for the legal sector. NLP allows AI to understand, interpret, and produce human language in a meaningful way. In law, NLP is used for tasks like analyzing legal documents, handling contracts, and helping to draft legal briefs.
Large Language Models (LLMs) represent a significant advancement in AI technology. These are models like GPT that use machine learning and natural language processing to comprehend and create text that closely resembles human writing. In the legal industry, LLMs can assist with tasks such as drafting legal documents, performing legal research, and offering initial legal advice. Their ability to grasp context and produce clear, relevant text makes them effective for tasks that require a strong understanding of language and legal principles. LLMs can analyze large amounts of legal information, improving their accuracy and relevance with continued use.
If you'd like to know more about LLMs, you can read our article that explores areas of excellence and limitations of large language models.
What can AI in LAW help with?
Legal research
The main uses of AI in legal research are predictive analytics, document review, and analysis. By quickly analyzing large volumes of documents, AI can identify patterns and extract important information. AI-driven tools help legal professionals:
- Process large amounts of data in minutes, find patterns in previous cases, minimize error, make better decisions about handling a case, identify potential risks, and anticipate obstacles before they arise.
- Help review contracts by identifying non-standard clauses, inconsistencies in language, and potential risks. AI algorithms can scan the text and highlight areas that need further review, helping legal professionals save time and spot potential legal issues that might be missed in a manual review.
Compliance and risk management
Identifying and preventing fraud, assisting with due diligence and compliance monitoring, as well as assessing and managing risks are all doable tasks for AI in the legal field. More specifically AI-powered tools can:
- Detect and prevent fraud and spot suspicious financial activities, such as money laundering or fraudulent transactions, while analyzing large data sets.
- Assist with due diligence and compliance monitoring, identify potential issues, and flag them for further review, saving time and helping to identify risks before they escalate.
- Assess and manage risks by analyzing past legal cases, spotting patterns in cases, and assessing the likelihood of risks.
Client services
In the sphere of client services, AI chatbots and virtual assistants offer personalized client recommendations and even predictive analytics in billing and pricing. AI can help with client retention by:
- Interacting with clients via AI-powered chatbots and virtual assistants that can answer common questions, provide updates on legal cases, and offer basic legal advice. These chatbots can be integrated with messaging platforms and websites to provide 24/7 client support, improving accessibility and responsiveness.
- Analyzing client data to provide personalized recommendations. By examining a client’s legal history and case details, AI can offer personalized legal advice and suggestions, including potential risks, legal options, and likely outcomes, allowing clients to make informed decisions.
- AI can analyze billing and pricing data to predict the cost of legal services, helping law firms to estimate costs more accurately. Predictive analytics can also highlight areas where firms can adjust their pricing structures and reduce costs.
Case management and workflow optimization
When it comes to work processes, AI can help with case prioritization, legal project management, and workflow automation. AI is making lawyers' daily workload lighter by:
- Helping legal professionals prioritize their workload by analyzing factors like case complexity, deadlines, and client priorities.
- Assisting in managing legal projects by tracking tasks, deadlines, and resources so law firms can monitor progress to keep projects on schedule and within budget.
- Taking on such tasks as data entry, document generation, and scheduling. AI can extract information from legal documents, fill databases, create standard legal documents, and schedule appointments saving time and allowing legal professionals to focus on more complex work.
Predictive maintenance for legal technology
Predictive maintenance is another field where AI-powered software excels. From predictive maintenance of legal databases and hardware to software updates and user support, you can rely on AI to get the job done. Minimzie the possibility of data loss or system downtime with the help of AI:
- Continuously monitor the status and performance of legal databases, predicting when maintenance or upgrades are needed before problems arise.
- Automate software updates and security patches, tracking release schedules and applying updates at optimal times to minimize disruptions.
- Identify critical security vulnerabilities that need immediate attention.
- Monitor hardware performance metrics, such as temperature and usage patterns, to predict when maintenance or replacement is needed.
- Provide immediate technical support with AI-driven chatbots and virtual assistants that analyze error messages and offer solutions to common issues.
Legal marketing and client acquisition
AI can help with marketing by automating targeted advertising, content generation, customer support, lead generation, and market trend analysis. With AI focused on marketing you can:
- Analyze data such as client demographics and online behaviors to create targeted advertising campaigns that reach the right audience.
- Generate written content on legal topics, such as blog posts, articles, and social media updates, and optimize content for search engines, improving visibility.
- Engage visitors, answer questions, and collect contact information for future personalized follow-ups to help generate leads for law firms.
- Analyze data from legal publications, social media, and client feedback to provide insights into market trends and competition.
Real-world EXAMPLES of AI in Law
Kira Systems uses AI to analyze and extract relevant information from legal documents, improving the efficiency of due diligence processes. Kira Systems team used Natural Language Processing, and Machine Learning to save time, reduce human error, and enhance the accuracy of document review.
Lex Machina offers legal analytics that predict litigation outcomes by analyzing past case law and judge rulings. Machine Learning and Predictive Modeling was employed to help lawyers devise better case strategies and manage client expectations by understanding trends and precedents.
LawGeex is a legal tech company that automated contract review and approval with the help of AI. LawGeex's platform analyzes legal documents, compares them to the company’s predefined legal policies, and highlights any deviations or issues that require attention. Now legal teams can review contracts faster and handle higher volumes of work while reducing the risk of missing important legal details.
DoNotPay was originally designed as a chatbot to help users contest parking tickets, and now has evolved into a broader legal service platform. The platform assists users in a wide spectre of legal matters, including filing small claims, managing subscriptions, and even helping with immigration processes. The chatbot interacts with users to gather necessary information and generate the appropriate legal documents or provide guidance on their legal issues.
ELTEMATE – A Hogan Lovells technology company, adopted AI-powered e-discovery tools to manage large-scale litigation cases. By using AI for technology-assisted review (TAR) and predictive coding, the firm can automatically sift through vast amounts of electronically stored information (ESI), such as emails, documents, and other data. The AI tools help identify relevant information more efficiently and accurately than manual review, significantly reducing the time and cost associated with e-discovery in complex legal cases.
Clio is a widely-used legal practice management software that employs AI for case prioritization and management. The platform uses AI to automate tasks like case scheduling, document management, and client communication. Clio’s AI features help law firms prioritize cases by analyzing deadlines, complexity, and client needs, ensuring that the most urgent cases receive attention first.
Seal Software is an AI-powered contract analysis and fraud detection platform that helps legal teams identify risks and potential fraud within contracts and other legal documents. NLP and ML is used by the software to analyze contracts, flag clauses or terms that may indicate fraudulent intent or non-compliance with regulations.
Derwent Innovation is an AI-driven platform that provides comprehensive patent research and analysis tools. The platform analyzes global patent data, identifies trends, assess the patentability of inventions and provides analysis of patent documents, helping with strategic IP decisions.
JuryScope is a legal consulting firm that uses AI to assist law firms with jury selection. The company combines AI tools with psychological analysis to evaluate potential jurors. By analyzing social media activity, online presence, and demographic data, JuryScope helps legal teams identify biases or predispositions that may affect the outcome of a trial.
DeepL, an AI-based translation service, used by legal professionals for translating contracts, court documents, and other legal texts. DeepL uses its neural network technology to provide highly accurate translations while maintaining the original legal terminology and tone.
Ravel Law (acquired by LexisNexis and now a part of their Lexis+AI products) used AI for sentiment analysis to assist lawyers in understanding judicial opinions and trends. By analyzing the language used in court opinions, Ravel Law's platform provided insights into judges' sentiments, helping legal professionals predict how a judge might rule on a particular case.
Verbit is an AI-driven transcription service that uses speech recognition and NLP technologies to provide real-time transcription services. Law firms use Verbit to transcribe depositions, court hearings, client interviews, and other legal proceedings. Verbit’s ability to learn from legal-specific language and terminology ensures that transcriptions are not only accurate but also contextually relevant.
Clio Grow is a legal-specific CRM and client intake management system that uses AI to streamline client relationship management for law firms. It helps law firms manage the entire client lifecycle, from lead generation and intake to ongoing client communication and relationship building. The AI-driven system can analyze incoming inquiries, automatically categorize and prioritize leads, and even suggest personalized follow-up actions based on client interactions. Clio Grow’s AI features can also analyze communication patterns to identify clients who may require more attention or follow-up, helping the firm maintain strong client relationships and improve retention rates.
Risks to be aware of when using AI
Ethical Considerations
- Bias and fairness
AI systems, especially those using ML, can unintentionally copy the biases found in their training data, leading to unfair outcomes in legal decisions or advice.
- Transparency
Many AI systems operate as "black boxes," making it hard to understand how they reach certain conclusions, this lack of transparency can affect accountability and trust.
- Dependency
There is always a risk that over-reliance on AI could undermine lawyers' professional judgment.
- Client confidentiality
Lawyers must ensure that AI tools meet professional standards for handling sensitive information to guarantee that client information is kept confidential when using AI.
Data privacy issues
- Data breach risks and sensitive data
AI use increases the amount of highly sensitive data stored electronically by Law firms, which can heighten the risk of data breaches. Strong security measures are needed to keep this data secure and prevent unauthorized access.
- Compliance with data protection laws
Law firms must ensure that AI systems comply with laws like GDPR or other local regulations to avoid legal and reputational risks.
Education and training
- Understanding AI
Lawyers and staff must learn about AI's capabilities and limitations to use it effectively and ethically.
- Training
Proper training in AI tools is necessary to maximize benefits and minimize risks, including knowing when to rely on human judgment.
- Staying updated
AI is constantly evolving, so continuous learning about new developments and their legal implications is a must.
Liability and accountability
- AI decision-making
Determining who is responsible for decisions made with AI can be complex. Clear guidelines for AI use are needed to manage liability.
- Accountability for AI errors
If an AI system makes a mistake, it can be challenging to determine who is accountable—the AI developer, the law firm, or the lawyer.
Client perception and trust
- Building trust
Clients might need to be more open about AI in their legal matters. Clear communication about how AI is used and its benefits can help build trust.
- Managing expectations
Setting realistic expectations about what AI can do is also essential to avoid client dissatisfaction.
While integrating Artificial Intelligence into legal practices presents various challenges, it is a path worth taking. By taking on routine tasks, AI does the research and analytics heavy lifting while allowing lawyers to focus on the more complicated aspects of their practice, such as strategy and client relationships.
