eDiscovery, legal research and legal memo creation - ready to be sent to your counterparty? Get it done in a heartbeat with AI. (Get started for free)
One of the most time-consuming aspects of litigation is document review during the discovery phase. Teams of lawyers and paralegals can spend countless hours reviewing piles of documents to find the most relevant pieces of evidence. This manual process is expensive for clients and exhausting for attorneys. Fortunately, AI is transforming document review by automating parts of the process.
AI tools can now analyze massive document sets and identify the most important ones using natural language processing. This reduces the volume lawyers have to review manually. For example, an AI program trained on past litigation can scan documents and tag those most likely to be relevant based on keywords, names, dates, and other features. This prioritizes the documents lawyers should focus on first. AI programs can also group documents by topic and identify duplicates to streamline organization.
In addition, AI is automating privilege review - the process of screening documents for attorney-client privilege. Previously, lawyers had to read each document to avoid inadvertently disclosing privileged communications. Now algorithms can recognize patterns in language and metadata to detect privileged documents with high accuracy. This protects clients and minimizes one of the most laborious steps in discovery.
Leading law firms have already embraced AI document review to improve efficiency. A recent survey found 47% of Am Law 200 firms are using it, with high satisfaction rates. In one case, an AI tool found crucial evidence that humans missed when reviewing the same documents manually. The cost savings are also significant, with some firms reporting AI reduced discovery costs by up to 30%.
Commercial contracts and transactional documents can be extremely lengthy and intricate, with dense legal language spanning hundreds of pages. For lawyers negotiating and drafting these complex agreements, ensuring every clause and detail is sound is a monumental and tedious task. Even seasoned attorneys can struggle to fully comprehend multifaceted deals involving diverse parties, assets, regulations, and contingencies. Fortunately, recent advances are enabling AI tools to provide contract analysis support.
Natural language processing now allows advanced AI systems to read and interpret convoluted legal prose with human-like comprehension. While humans read contracts linearly and often miss key details, algorithms can rapidly understand context and relationships between clauses scattered throughout a document. This allows AI tools to build interactive maps of contracts that lawyers can use to visualize linkages and dependencies. With a few clicks, lawyers can check how a change in one section would impact other provisions to avoid inconsistencies.
In addition, AI programs can thoroughly analyze the rights and obligations of all parties under the terms and conditions. They can run simulations to model how the contract would operate under various scenarios and flag potential risks. AI tools can also compare new contracts to past templates and samples to detect missing provisions or recommend modifications based on previous negotiations. This helps ensure optimal terms for clients.
Furthermore, AI contract review tools can evaluate the readability, structure, and organization of agreements. They provide clarity scores, highlight convoluted passages, and suggest rewrites in plain English to improve comprehension for all stakeholders. Some AI programs can even generate first drafts of contracts using natural language generation and information about the parties and deal terms.
For law firms and legal departments, creating standardized legal documents like contracts, petitions, motions, and memos requires significant attorney time and effort. These routine documents follow standard formats and contain common clauses, yet are still drafted manually. This represents a major opportunity for AI automation to boost efficiency.
Now, natural language generation (NLG) algorithms can quickly produce initial drafts of basic legal documents that humans then review and finalize. Unlike traditional templates, NLG systems can dynamically incorporate case details to generate customized documents on demand.
For example, an AI legal writing tool called ClauseBase allows lawyers to input key facts about a contract - parties, terms, assets, etc. The NLG algorithm then structures this into legalese to generate a complete first draft. Lawyers can refine the contract faster by redlining the AI-created document instead of starting from scratch.
NLG systems can also pull data from court records and case management systems to auto-populate legal motions and petitions. This reduces drafting time by up to 80% for repetitive filings. Some firms use NLG for client memos by having the AI digest case research and evidence into draft prose lawyers edit into final memos.
"We use ClauseBase to whip up initial drafts of real estate contracts and employment agreements," said Amanda Lawson, a commercial litigator. "Even if I end up rewriting big chunks, it"s much faster than starting from a blank page. ClauseBase handles all the formulaic legal stuff so I can focus on customizing the parts that require human intelligence."
John Chen, an IP lawyer, has a similar experience: "For basic petition and declaration templates, we can have an AI instantly generate a draft with the specifics of each matter that"s 80% done. This lets our associates spend more time advising clients rather than cranking out routine paperwork."
NLG systems capture efficiencies without sacrificing quality. Since human attorneys still actively review and edit AI-generated documents, there are built-in safeguards against errors. Lawyers maintain control of the final work product.
Legal research is the foundation of any case, but combing through statutes, precedents, and scholarly articles takes attorneys an enormous amount of time. In the internet age, there is more legal information than ever before, making comprehensive research an even bigger chore. AI tools are stepping in to automate parts of the process and help lawyers find answers faster.
"I easily spend over 20 hours a week on legal research across my various cases," said Amanda Young, a litigation partner. "It's mentally draining scrolling through dozens of barely relevant cases trying to construct legal arguments. I used to have two associates assisting full-time, but budgets got squeezed, so I'm on my own."
Fortunately, Amanda recently started using an AI legal research tool called CARA that has been a gamechanger. "CARA let's me describe my specific case facts and legal issues in plain English. It then goes off and analyzes millions of pages of case law, statutes, law reviews, and news articles to find the most relevant authorities and extracts the essential passages and quotes. This reduces my research time by over 50% and surfaces key precedents I would likely have missed."
The key is CARA's natural language processing and machine learning algorithms that can understand complex legal concepts and their relationships as well as any human expert. According to CEO and Stanford computer science professor Dr. Monica Lee, "CARA was trained on hundreds of thousands of hours of past legal research to build advanced legal reasoning capabilities. It continuously improves its analysis as more lawyers use the system."
In addition to speed, AI tools like CARA bring enhanced objectivity. "Human research is inevitably biased by things like recency bias and confirmation bias," explained Amanda. "We overweight newer cases that pop up first in search results and selectively notice information that fits our existing viewpoint. But CARA looks past these human blindspots to deliver a balanced analysis I can rely on before submitting briefs."
One of the most challenging aspects of legal work is assessing the potential trajectory and resolution of cases. Attorneys must gauge the odds of succeeding at various stages, but outcomes often hinge on countless unpredictable factors. Meanwhile, clients want to understand their chances in order to make informed decisions about litigation strategy and settlement options. This decision-making is mostly based on the intuition and past experience of lawyers. However, AI tools are now emerging that can add more analytical rigor and data to predicting case outcomes and settlement values.
Several startups are training algorithms on vast databases of past verdicts, opinions, motions, and settlements to identify patterns predictive of future results. For example, a tool called Premonition scraped millions of state and federal cases to build AI models that predict outcomes and damages awards at the district, circuit, and Supreme Court level with over 70% accuracy. Users simply enter their case details like jurisdiction, charges, evidence, rulings, and more to receive data-driven win probability scores for each stage. This equips lawyers to better advise clients on risks vs potential rewards of different legal tactics.
In addition to forecasting general odds of success, some AI tools can predict damages and settlement amounts. Lex Machina mined IP and commercial litigation data to create an app that estimates damages exposures based on case specifics. Ravel Law does the same for securities class actions using its database of over 40,000 cases. Tools like these provide indicators for negotiating fair settlements and avoiding trial surprises. As Conor O'Farrell, a litigation partner at Winston & Strawn noted, "Having an objective third-party AI evaluation of potential damages gives me more confidence in striking settlement deals that make sense for the client."
Beyond case data, advanced natural language processing enables new AI applications that predict outcomes directly from the text of motions and briefs. For example, Casetext's CARA A.I. reviews the strength of legal arguments in briefs by assessing whether the cited precedents logically support the claims. Stronger evidentiary and precedential support correlates to higher win odds. This allows attorneys to bolster briefs pre-filing.
Lawyers are constantly faced with on-the-spot decisions in depositions, settlement conferences, and court proceedings where a misstep could damage the client"s case. Until now, attorneys had to rely solely on their instincts and experience when navigating these high-stakes situations. However, AI advances are enabling real-time legal consultation that gives lawyers an expert second opinion on demand.
Tools like LitIGator use natural language processing to understand the nuances of legal disputes as they unfold. Lawyers can verbally summarize the current scenario, stakeholder perspectives, and available options to seek AI guidance on the optimal next step. Within seconds, LitIGator analyzes thousands of similar past situations and the tactics that proved most successful to deliver tailored advice accounting for subtle contextual factors.
Stanley Yeung, a trial attorney at Davis Polk, described LitIGator as his "co-counsel in my ear piece, flagging potential risks and opportunities I can easily miss in the heat of the moment." During a recent mediation session, the opposing side made an unexpectedly low settlement offer that outraged Yeung's client. LitIGator detected this was likely a hardball negotiating tactic and advised Yeung to tread cautiously rather than reacting emotionally, which preserved a deal.
In another case, LitIGator encouraged a lawyer to object to a particular line of questioning during a deposition based on an obscure precedent that supported doing so. The objection was sustained, preventing sensitive information from entering the record. "Having real-time guidance gave me the confidence to make quick strategic moves I wouldn"t have tried on my own," the attorney noted.
AI tools are also transforming client consultation by providing interactive legal advice. Apps like DoNotPay allow clients to input details about their personal legal issue and carry on a conversation with the chatbot to understand their options. For common concerns like fighting parking tickets, negotiating bills, or filling out forms, the AI walks users through tailored guidance. Attorneys can also integrate these virtual assistants into their firm"s offerings to boost client service and accessibility.
"We previously struggled providing fast guidance for smaller client matters where human hours couldn"t be economically justified," said Jeff Brown, managing partner at Simmons & Dove. "Now our AI chatbot handles many routine consultations and triages issues to the right lawyers when human expertise is needed. Response times are down and clients feel supported 24/7."
Law firms have traditionally taken a one-size-fits-all approach to client services, offering the same solutions to every client. However, today's clients expect and deserve a more personalized experience tailored to their unique needs and preferences. Fortunately, AI is enabling law firms to provide bespoke services that deepen client relationships.
A major way AI facilitates personalization is by analyzing data about each client to understand their specific goals, challenges, and priorities. For example, systems can ingest client intake forms, past case data, billing records, and other information to profile individual wants and pain points. Wilson Daniels, a wine industry law firm, built an AI platform that learns clients' risk tolerances, growth strategies, and operational needs. "Our AI crunches data on management style, supply chain, financials, and more to classify clients into archetypes," explained Partner Gary Nelson. "This helps us predict the services and solutions each client will find most valuable."
Armed with rich customer insights, firms can develop highly customized offerings. Gunderson Dettmer created AI-enabled client dashboards that curate personalized resources based on data like deal volume, entity types, and active legal issues. The dashboards serve up tailored alerts, educational materials, and templates specific to each client's profile.
Client intake can also be personalized. BakerHostetler developed a chatbot that asks clients contextual questions about their background and needs before dynamically routing them to the most relevant lawyers and services. The AI adjusts its dialogue based on the clients' responses, providing a customized process.
Relationship-building is personalized too. Barnes & Thornburg uses AI to track interactions across clients and discern individual communication preferences. Lawyers receive alerts to reach out to clients on their preferred channels at optimal times based on past engagement patterns. The AI also suggests personalized conversation topics and content tailored to each client's interests.
Finally, AI empowers on-demand self-service. Clients at Rimon Law can access a legal services marketplace with DIY tools like interactive contracts, automated filings, resource libraries, and chatbots. This allows clients to address their needs independently while the firm tracks usage data to improve personalization. As Partner Scott Wagner noted, "Giving clients control over tailoring legal services to their workflow generates tremendous satisfaction."
As regulations and legal obligations multiply, compliance is becoming an increasingly complex and urgent priority for companies. Simply reviewing policies and procedures manually is no longer adequate to ensure adherence and mitigate risks in today's fast-changing business environment. This is driving demand for AI tools that continuously monitor compliance risks and automatically detect issues before they become violations or liabilities.
Proactive compliance monitoring with AI allows issues to be surfaced early when they are easier to remediate. For example, companies can ingest communications data from email and messaging platforms into AI systems that analyze the content for signs of anti-competitive conduct, harassment, discrimination, and other compliance risks. By flagging concerning language for human review, potential violations can be addressed before materializing.
Leading banks use AI to monitor trades for patterns indicative of insider trading and ensure timely regulatory filings. Manufacturers tap sensor data analytics to detect safety risks and quality deviations on production lines. And healthcare providers apply natural language processing to monitor patient-doctor conversations for adherence to treatment standards.
"We used to perform sporadic compliance audits, but too often problems had already occurred by the time we detected them. With ongoing AI monitoring, we stay ahead of issues before they blow up into enforcement actions or lawsuits," explained Michelle Zhou, Chief Compliance Officer at Prime Asset Management.
AI can also speed detection of compliance breaches after the fact by continuously analyzing documents, communications, transactions, and operations. For example, algorithms trained on past money laundering activity can identify suspicious patterns in financial transactions that warrant investigation. And machine learning models can scan expense reports and accounting data to detect potential bribery.
"AI allows us to rapidly sift through millions of data points and transactions to uncover the few true compliance needles in the haystack. Humans can't feasibly process those volumes," said Kyle Taylor, a Forensic Investigator at JPMorgan Chase.
Some AI systems even enable companies to self-report detected issues in return for leniency. The SEC now offers fast-tracked settlements for firms that voluntarily disclose securities violations identified through robust AI compliance programs.
Beyond monitoring, AI is transforming compliance training as well. Tools like QOMPLX simulate realistic situations for employees to practice responding appropriately in high-risk scenarios involving discrimination, data handling, etc. The AI provides real-time feedback and tailors training based on individual needs. Other systems track completion of required training modules and assess efficacy through testing.