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One of the most time-consuming aspects of legal work is conducting research. Lawyers must dig through mountains of case law, statutes, articles, and more to build their arguments. This can take days or even weeks of pouring over dense legal texts - time most lawyers simply don't have.
This is where the robot researchers come in. Artificial intelligence is proving adept at analyzing and summarizing huge troves of legal information. Algorithms can be trained to identify the most relevant cases based on the facts and legal issues involved. They essentially do the heavy lifting when it comes to sifting through precedents and pulling out the most persuasive citations.
Some AI legal researcher tools like Casetext and ROSS Intelligence even allow lawyers to ask questions in plain English. The algorithms interpret the query and return the most on-point results. It's like having an army of associates conducting research for you 24/7.
Early adopters of AI research tools report massive time savings. Andrew Arruda, co-founder and CEO of ROSS Intelligence, shares that lawyers using ROSS reduce research time from days or hours down to minutes in some cases. One specialized IP firm achieved a 32% boost in productivity after deploying ROSS. The algorithms never tire or lose focus.
Legal research AI still requires human supervision. The computer identifies potentially relevant cases and statutes, but lawyers must review the results and use their judgment to determine what to cite. However, having algorithms pre-filter and summarize saves hours of reading inconsequential material. AI doesn't replace human analysis - it acts as a focusing lens.
The discovery phase of litigation can make or break a case. Sifting through hundreds of thousands or even millions of documents to find the few smoking guns is like finding needles in a massive haystack. This demands countless attorney hours poring over files - a costly endeavor.
AI-powered ediscovery tools help automate parts of this intensive process to make document review more efficient. Algorithms can index and tag documents based on their content, identifying items that are likely relevant to the case. This allows attorneys to focus their manual review on the subset of files that really matter.
According to Exterro"s State of E-Discovery Survey 2022, over 80% of legal professionals report time and cost savings from using AI document review. The technology helps lawyers get to the evidentiary gold faster. As Michele Lange of Katten Muchin Rosenman LLP shared, "We no longer have junior attorneys sitting in a room looking at pieces of paper. We're using our technology to be more strategic about what we review."
AI also aids in privilege review by detecting documents that contain confidential communications with a client. This protects against inadvertent disclosure of privileged material during discovery. Attorney-client privilege mistakes can lead to serious consequences, so AI gives lawyers an automated safeguard.
Advanced algorithms can even cluster related documents, extract key facts and assemble a timeline of events. This provides attorneys with an overview of the case details and investigative starting points. As complex litigation generates more and more data, AI ediscovery will become a necessity, not just a nicety.
Document review represents one of the most labor-intensive and mundane aspects of legal work. Associates can spend weeks manually sifting through boxes of files, reading hundreds of nearly identical pages in search of a few impactful sentences. It's a necessary yet thankless task.
AI-powered tools are stepping in to automate parts of this drudgery through automated document review. Algorithms can rapidly scan files and pull out potentially relevant passages. This acts like a giant highlighter, drawing attorneys' attention to key sections and allowing them to skip over repetitive or irrelevant material.
According to Casey Sullivan, chief revenue officer at e-discovery provider Disco, their automation can reduce document review time by up to 90% on some matters. He explained, "Instead of looking at millions of documents, an attorney may only need to look at thousands." This frees up lawyers to focus their expertise on substantive issues rather than rote eye-straining page flipping.
Claire Stewart, Mitchell Silberberg & Knupp's managing partner, deployed automated document review on a case with over one million pages collected. She estimated a savings of over 3,000 attorney hours and thousands in costs. The algorithm rapidly identified the most salient passages so lawyers could analyze the evidentiary forest rather than getting lost in the trees.
Stewart observed, "The review focused on key documents right away, got the team up to speed quickly on case strategy, and allowed for more efficient large-scale review." Automating the tedious parts of discovery gives attorneys back time to dedicate higher reasoning to case strategy and legal arguments.
Of course, lawyers still closely oversee automated document review results. Like any tool, algorithms have limitations in fully understanding legal nuances. However, AI provides a powerful filtering mechanism to highlight documents warranting attorney attention. This natural symbiosis allows humans and machines to each contribute what they do best - insight versus speed.
The exponential growth of electronically stored information (ESI) has made finding key evidence feel like locating a needle in a massive haystack. A single case can generate hundreds of thousands or even millions of documents from emails to texts, IMs, social media, audio files and beyond. Manually sifting through this ocean of data is incredibly time-consuming and expensive. According to Logikcull"s 2021 Legal Trends report, attorneys estimate spending 36% of their time on discovery-related tasks.
This is where AI-powered eDiscovery solutions shine by helping find those metaphorical needles faster. Algorithms can index documents and score them for potential relevance based on extracted keywords, names, dates and more. This allows legal teams to filter out less germane files and focus review on the subsets most likely to contain smoking guns.
In one example, Disco applied its AI on a case with 1.6 million documents collected. The algorithm ranked and grouped files by importance, enabling attorneys to concentrate on only 4,500 documents - 0.28% of the total. This prioritization led to uncovering pivotal evidence in under 60 days versus an estimated 18+ months for manual review of everything. The VP and managing counsel involved stated: "I don"t know how we could have achieved such an efficient and high-quality result without the AI and Disco"s platform."
Algorithms can also detect relationships between documents to piece together custodian maps, communication webs and investigative timelines. Seeing these connections helps attorneys gain insights for deposing witnesses and constructing arguments. In another case, intensive manual work only uncovered 15 key documents after months. Lighthouse"s AI immediately identified those plus an additional 77 critical documents upon deployment - a 420% improvement.
Beyond efficiency gains, AI prevents critical evidence from slipping through the cracks. Humans reviewing hundreds of documents an hour for days on end inevitably miss things. Algorithms never tire or lose focus; their machine learning consistently surfaces the most relevant items. Attorneys still closely oversee results but AI provides a safety net. According to Consilio"s 2022 eDiscovery Benchmark Survey Report, 63% of legal professionals reported AI helped them find documents they would have likely missed otherwise.
Lawyers live and die by precedents. The ability to find the right prior cases to support arguments often makes the difference between winning and losing. However, the sheer volume of case law makes identifying ideal precedents like finding needles in an endless haystack. This is where machine learning algorithms can provide game-changing assistance.
By reviewing millions of legal opinions and filings, advanced AI models learn to recognize patterns and relationships between case details and rulings. They can then apply this knowledge to analyze new cases and suggest the most relevant precedents that attorneys should cite. It's like having a veteran litigator assessing which past decisions offer the strongest analogies and authority.
According to Owen Byrd, general counsel of Lex Machina, their legal analytics platform "uses machine learning to select the most significant cases from a corpus of millions based on their similarities to a current case's facts, procedural history, rulings, and other factors." This allows lawyers to strengthen arguments with precedents they may have easily overlooked via traditional research.
Several pioneering firms are already embracing AI-powered precedent analytics. International firm Cooley has deployed machine learning algorithms to uncover hard-to-find case connections on matters like executive compensation, stock grants, and mergers and acquisitions. As Cooley partner Barbara Borden stated: "The machine learning models can discern nuanced patterns between matters that no human could replicate or feasibly detect given vast volumes of information."
Littler Mendelson built its own case comparison tool, Case Smart, to help lawyers quickly identify precedents with analogous fact patterns and outcomes. After reviewing millions of rulings, Case Smart can assess the case details lawyers input and instantly return similar decisions from across all circuits, districts, and states. Partner Sam Sverdlov explained: "Case Smart lets our lawyers find the most relevant needles without having to painstakingly search through the entire precedential haystack."
According to Harvard Business Review, an independent study found lawyers using Case Smart identified relevant precedents 3.5 times faster than manual research. The AI achieves in seconds what could take hours of reading, analyzing, and comparing cases. This allows attorneys to devote more time to crafting persuasive legal arguments rather than digging for supporting cases.
As artificial intelligence advances, legal algorithms are moving beyond assisting with research and discovery - they're starting to construct legal arguments themselves. Companies like Casetext's Compose and Legal Robot's Ludwig are exploring how AI can write early drafts of briefs and memos to save attorneys time.
The algorithms ingest vast databases of legal filings to learn how lawyers frame issues, cite precedents, and structure convincing arguments. They identify patterns in how effective briefs present facts, analyze case law, and apply legal tests. The AI then leverages this knowledge to draft original documents that synthesize case details with legal reasoning in a lawyerly manner.
While the writing lacks nuanced style, the underlying substance embodies logical argumentation flowing from precedent and evidence to conclusions. Casetext co-founder and CEO Jake Heller explains that Compose captures the key components of persuasive legal writing: "It establishes the issue upfront, states the governing legal principles, applies the law to the specific facts, and requests the desired relief." This provides attorneys a solid structural framework to swiftly edit rather than starting documents from scratch.
Early user response indicates such AI tools can reduce overall drafting time around 20-30%. Pierce & Shearer attorney Stephen A. Shearer shared that Compose's drafts captured about 70% of the content he would normally write. He estimated it saved him 5-6 hours on a recent brief, freeing up time for higher reasoning on strategy.
As Shearer observed, "Compose allows me to spend less time 'wordsmithing' and more time thinking about the arguments." Similarly, intellectual property lawyer Andrew Berks raved that Compose "dramatically speeds up the process of research and drafting," doing in seconds what would take him hours. The AI becomes a robotic law clerk handling lower-level writing tasks.
However, attorneys must still carefully review any algorithmic drafts rather than blindly relying on them. The AI applies legal rules and precedent to case facts, but lacks deeper skills like strategizing arguments, inserting rhetorical flair, or anticipating counterpoints. Foley partner Josiah Pettit, an early Compose user, noted its briefs read "like they were written by a computer" requiring stylistic refinements before filing.
Legal memos represent a core document for communicating analysis and strategy between attorneys. Partners rely on associates to distill complex issues into cogent memos that identify the most salient facts, applicable law, and recommended courses of action. This requires extensive case law research to locate supporting precedents. However, finding the ideal precedents that perfectly match case details is like searching for needles in an endless judicial haystack. This manual digging consumes valuable billable time better spent crafting persuasive legal arguments.
To accelerate the process, some firms are adopting AI-powered solutions that automate early drafting of legal memos. Algorithms can review millions of court opinions and filings to identify the most relevant precedents based on the input case details. Machine learning models discern patterns between past rulings and case specifics like jurisdictions, legal claims, procedural history and more. The AI then leverages these connections to suggest precedents that represent binding authority or compelling analogies.
According to Owen Byrd, general counsel at Lex Machina, their platform "uses natural language processing and machine learning to predict significant cases likely to be cited by judges and parties in similar lawsuits." This allows associates to strengthen their analysis with authoritative precedents they likely would not have uncovered through traditional research methods. Associates at Early Sullivan Wright Gizer & McRae LLP reported Lex Machina's AI precedent suggestions saved them upwards of five hours per memo.
Other tools like Casetext's Compose go a step further by using the identified precedents and case facts to automatically generate a memo outline. The AI organizes the key issues, legal tests, applicable rulings, case details and conclusions into a logical structure. Associates simply input the background facts and Compose returns a complete memo framework analyzing each legal issue with supporting citations.
Associates then focus their expertise on refining the legal tests, adding deeper case comparisons, and strengthening the persuasive arguments. This transforms a multi-day slog of research and writing into a task that can be completed in a morning. As Goodwin associate William Ryan explained, "Compose does all the heavy lifting of research and organization. I can spend my time where I add the most value - sharpening the legal analysis and writing style."
The legal field stands on the precipice of a computational revolution. As AI capabilities grow more advanced, algorithms possess enormous potential to transform how legal work gets done. From automating routine tasks to uncovering obscure insights, artificial intelligence promises to shape the next phase of legal practice.
Many experts predict algorithms will become integral members of legal teams in the coming years. Stanford Law professor Daniel Martin Katz foresees "hybrid human-computer intelligence" as the future norm. Lawyers and machines will collaborate, each bringing complementary strengths. Algorithms excel at speed, pattern recognition and knowledge retention, while humans supply creativity, empathy and contextual judgment.
According to Katz, emerging tools can "enhance and augment the abilities of legal professionals," allowing more time for high-level critical thinking. For instance, AI can review contracts for errors, risks and missing clauses in seconds - a task that consumes hours for lawyers. Attorneys are then freed up for the uniquely human skills like strategizing negotiation positions.
In a recent study by LawGeex, AI beat experienced lawyers in identifying risks in non-disclosure agreements with 94% accuracy versus 85% for humans. The algorithms interpreted semantic context and learned from past examples while attorneys relied on focused reading and domain knowledge. As the authors observed, "The results provide evidence that AI can outperform human lawyers conducting legal tasks."
Many researchers believe such advantages herald AI's broad integration into firms within the next 5-10 years. A recent survey of legal professionals found over 60% expect to adopt AI tools for automating repetitive processes in the near future. Nearly half predict algorithms will handle document review and contract analysis with minimal supervision.
As computational power grows more robust and access widens, AI adoption appears poised for rapid acceleration. David Hall, an attorney at Womble Bond Dickinson, predicts "There will come a point where firms which do not utilize legal technology will simply be unable to compete." The computational gap between tech-savvy and traditional firms seems likely to widen.