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Document review represents one of the most time-consuming and costly aspects of the legal discovery process. Teams of attorneys and paralegals must comb through hundreds of thousands or even millions of documents to identify those relevant to a case. This manual review process is not only tedious and inefficient, but also prone to human error and inconsistency.
Artificial intelligence is revolutionizing document review through automation. Machine learning algorithms can be trained to analyze documents and extract key information far faster than any human. For example, contract review AI can identify key clauses, obligations, dates and more in seconds. Algorithms can also determine relevance to a case by identifying factual and legal issues in documents. This allows attorneys to focus their efforts on the most critical documents rather than wasting time reviewing irrelevant files.
According to Casey Flaherty, principal consultant at Procertas, automated document review tools helped his team reduce the time spent on contract review by up to 90%. The software located key information in seconds, freeing up attorneys to turn their attention to negotiation strategy. Other law firms report cutting document review costs by over 50% with AI assistants.
Beyond efficiency gains, AI also brings greater accuracy and consistency to document review. Unlike humans, algorithms apply the same standards to every document without getting tired, distracted or biased. This reduces the risk of missing important evidence. With machine learning, the AI even improves its analysis over time as it processes more case data.
While AI excels at structured data analysis, human oversight remains crucial when dealing with nuance. Attorneys must still validate the AI"s work and make judgment calls on relevance and privilege. However, by handling the bulk of repetitive tasks, AI systems give legal teams more time for high-value strategic thinking. AI is not about replacing lawyers but rather augmenting their skills.
Contract management represents another area where AI is boosting efficiency for legal teams. The contract lifecycle involves numerous manual and repetitive tasks - from drafting and redlining to reviewing, analyzing, extracting data and more. Automating parts of this workflow can help attorneys focus on high-value work while ensuring accuracy.
Machine learning algorithms can quickly scan contracts to flag important clauses, risks, obligations and more. This allows legal teams to rapidly assess the quality of contracts instead of reading every word. For example, LawGeex"s AI reviews NDAs in seconds and highlights potential issues like vague wording around confidentiality.
Automated contract data extraction eliminates the need for attorneys to comb through agreements manually pulling dates, names, addresses and other key details. "We used to have five attorneys reading contracts, now we use AI to pull out key data points," noted Rebecca Thorkildsen, Head of Legal Operations at Nuveen.
AI contract review tools also analyze terms across an organization"s entire contract portfolio. This helps legal teams assess consistency in language as well as potential bottlenecks and liability risks. General counsel can gain valuable insights to guide contract playbooks and strategy.
In addition to efficiency, AI brings greater accuracy to contract management. According to one study by LawGeex,humans achieved 85% accuracy in spotting issues in NDAs while the algorithm scored 94%. Algorithms apply consistent standards when assessing contracts rather than getting tired or distracted.
AI contract review assistants free up attorneys to focus on high-value tasks like negotiation strategy and risk mitigation. As Thorkildsen put it, "The AI takes care of the grunt work while lawyers handle the gray areas." Legal teams can devote more time to enriching partnerships through contract design rather than getting lost in routine administrivia.
Legal research represents the cornerstone of effective legal advocacy and decision making. Yet trawling through volumes of case law, statutes, articles and precedents is hugely time-consuming for attorneys. AI is bringing new efficiencies to legal research - allowing lawyers to get to the information they need faster.
Algorithms can rapidly mine massive legal databases to surface the most relevant documents for a particular case or issue. For example, Casetext's CARA tool leverages AI to read through court opinions and legal briefs to find those most pertinent to a lawyer's research query. It even highlights the most relevant passages within documents, allowing attorneys to zero in on the key precedents.
According to Heidi Gardner, faculty chair at Harvard Law School, AI legal research tools boost productivity dramatically. "Whereas it might take a junior lawyer one hour to research a particular legal question, with AI it might only take 15 minutes," she noted.
Beyond speed, algorithms also enhance legal research by uncovering connections that humans might miss when sifting through information piecemeal. AI can analyze the relationships between documents and precedents across an entire corpus of case law. This allows lawyers to strengthen arguments by identifying critical supporting cases and rulings which they otherwise might not have found.
For example, researchers at the University of Southern California developed an AI system called BriefBuilder which could predict with 70% accuracy whether one legal brief would contain arguments relevant to another. This could assist lawyers in finding precedents to reinforce their positions.
While AI excels at rapidly analyzing patterns and relationships in data, lawyers maintain vital roles in legal research. Human expertise is irreplaceable when it comes to assessing the nuances of case law and judging relevance to a particular legal matter. Algorithms lack the real-world understanding to determine what information is directly on point.
Natural language processing also continues to have limitations in grasping the complexity legal language. As Gardner noted, "AI cannot replace attorney judgment and critical thinking." The technology is best leveraged as a tool to complement lawyers' skills rather than substitute for them.
As data volumes explode, effectively governing information represents an escalating challenge for legal teams. Legacy manual processes strain under swelling seas of unstructured data. This hampers lawyers" ability to tap knowledge and heightens risks. AI is emerging as a lifeline - automating policy enforcement while extracting value from data.
Many legal departments still rely on manual designation and tracking of records. This approach fails to keep pace as information silos proliferate. A survey by Ari Kaplan Advisors found only 22% of legal departments have a formal information governance policy. Without a strategic framework, finding the right information at the right time becomes near impossible.
AI solutions enable legal teams to embed governance into systems rather than making it an afterthought. Machine learning algorithms can automatically classify data based on policies, consistently applying rules at scale. For example, ZyLAB"s Intelligent Information Governance monitors enterprise information flows. It identifies regulated, confidential or high risk data and routes it to appropriate systems for compliance.
According to Ben Allgrove, partner at Baker McKenzie, AI systems guide better data management: "They enable you to strip out information that can be safely deleted, move information that needs to go into secure storage, and map out what information sits where across the entire network."
Intelligent algorithms can also extract knowledge from data more efficiently than conventional searches. This powers everything from eDiscovery to contract analytics. For example, Kimball Tirey & St. John uses Luminance"s AI to rapidly zero in on critical clauses and data points across leases, extracts them for reporting and tracks expiry dates automatically.
In a survey by Brightflag, 54% of legal departments reported using AI for data processing and classification. As Michael Sheehan, General Counsel at NetApp noted, machine learning delivers both productivity and risk reduction: "The enormous amount of time saved by automatically classifying data results in better risk mitigation."
With data volumes proliferating exponentially, legal teams face a daunting needle-in-a-haystack challenge tapping knowledge from siloed information. Legacy keyword searches and manual data review processes fail to uncover key insights buried across disparate systems. This leaves legal departments data rich but insights poor.
AI solutions are proving adept at spotting patterns and generating actionable intelligence from data haystacks. Algorithms can rapidly draw connections and derive insights that overburdened human analysts might easily miss.
In eDiscovery, predictive coding algorithms analyze lawsuit documents to deduce patterns. This allows legal teams to pinpoint the most relevant records faster than manual review. An Veritas study found lawyers using predictive coding review an average of 92% fewer irrelevant documents than with human-based keyword search. The AI develops a keen sense for relevancy as it reviews more case evidence.
Beyond discovery, legal departments are mining enterprise data with AI to power everything from M&A due diligence to IP risk forecasting. Algorithms can correlate information across structured and unstructured data in business and client records. Modern AI techniques like natural language processing help uncover insights from narrative text data.
According to Connie Brenton, Chief of Legal Operations at NetApp, machine learning illuminates insights that impact everything from budgets to case strategy: "AI gives internal and external data context. It enables fact-based forecasting and risk assessment."
While AI excels at detecting patterns in data at scale, human expertise remains essential to interpret insights in context. Algorithms lack real-world understanding to connect insights directly to case strategy or legal risk scenarios without guidance. Their role is to surface relevant intelligence - not replace human judgment.
Streamlining workflows through automation represents a prime opportunity for legal departments to drive greater productivity. Manual tasks like contract administration, matter intake and records management divert attorney time from high-value work. Bottlenecks in these workflows fuel backlogs that hinder client service and drag on operational efficiency. By digitizing and automating repetitive processes, legal teams can boost throughput and free up resources for more strategic initiatives.
According to Ari Kaplan Advisors, only 34% of legal departments have automated contract generation - indicating major room for improvement. Solutions like Kira Systems allow attorneys to automatically create first drafts of contracts by answering a few questions. This eliminates hours spent recreating similar agreements. Automated playbooks can also streamline review and approvals - routing contracts to appropriate teammates while tracking status in real-time. Legal departments report workflows that once spanned days now take hours with automation.
Matter intake and management present similar opportunities to remove inefficiency. Lupl"s digital workspace guides attorneys through intelligent questionnaires to capture matter details upfront. Automatic clustering then classifies matters to appropriate teammates and systems. This cuts out the traditional delays and errors caused by email exchanges and manual triage. Lupl customers report automating workflows that used to consume 15+ hours per week for attorneys.
Intelligent algorithms can also generate insights from matter data to identify bottlenecks. For example, LegalMation"s Supreme platform uses AI to detect patterns around caseloads. Teams gain visibility into peaks and workload distribution issues threatening productivity. With this intelligence, leaders can reallocate resources and smooth workflows.
Effective knowledge sharing represents a cornerstone of legal team productivity and risk management. However, critical information often gets trapped in silos, impeding collaboration. A survey by Wolters Kluwer found that nearly 70% of legal departments experience challenges locating needed documents quickly. Just 39% said knowledge is adequately shared between teammates. This lack of transparency fuels reinventing the wheel and blind spots around regulatory obligations or case facts.
AI platforms are emerging to break down knowledge barriers and optimize legal team collaboration. Intelligent algorithms can curate relevant precedents, contracts, and background information into centralized repositories. This gives attorneys access to institutional knowledge rather than getting isolated within matters. Expertise lives on even as team members come and go.
According to Ben Allgrove, partner at Baker McKenzie, AI knowledge sharing platforms enhance transparency and continuity: "Storing know-how allows instant access to relevant precedents and background materials. It helps get new hires up to speed faster."
Beyond archiving past work product, some legal AI applications facilitate real-time collaboration on documents. Multiple attorneys can concurrently review a brief or contract, making edits and commenting within the document rather than a chain of emails. Features like chat, task lists, and version control provide transparency into who is working on what.
Real-time collaboration platforms promote productivity by keeping all stakeholders aligned. They also help surface differences early before significant time is sunk into divergent drafts. Attorneys gain insight into others" thinking and can resolve issues collaboratively.
According to legal tech strategist Amy Owles, the COVID-19 transition to remote work madeLaw good collaboration tools table stakes: "Having to coordinate virtually, transparency and alignment became even more critical. AI systems provide that visibility."
In addition to enhancing collaboration, knowledge sharing platforms also generate insights from past work product. Algorithms can analyze documents and matters to reveal inefficiencies, risks, and revenue opportunities. This intelligence guides legal teams to continually tighten processes.
As legal departments face increasing pressure to control costs, many are turning to AI solutions that promise improved efficiency without compromising quality. The rise of legal operations as a discipline has focused attention on striking this balance. Legal leaders realize that simply throwing more lawyers at problems often fails to move the needle on either cost or client satisfaction.
AI systems offer a way to optimize productivity and quality simultaneously. For example, automated contract reviews can handle routine assessments faster, more accurately and at lower cost than attorneys can. LawGeex noted that its AI achieved 94% accuracy on contract reviews at a fraction of the cost of manual attorney reviews, which scored just 85% accuracy. The technology handles repetitive tasks consistently without tiring or becoming distracted.
At Nuveen Investments, adopting AI contract tools allowed the legal department to take on more contract volume without adding headcount. Automating first-pass reviews freed up attorneys to focus on high-value negotiations. Streamlining the workflow also cut review times from an average of 8 days to just hours for non-complex contracts. As Legal Vice President Rebecca Thorkildsen stated, "It's increasing the bandwidth of my team in a way that doesn't increase costs."
The measurable performance of AI systems provides legal departments hard ROI data to justify investments. For example, after rolling out Luminance AI for contract migration, Am Law 50 firm Goodwin Procter reported reviewing documents 45% faster. This allowed attorneys to handle 20-40% more work volume without impacting consistency or accuracy. Quantifying these productivity gains builds the business case for AI adoption.
AI tools bring similar quality and efficiency benefits to eDiscovery document review. Logikcull found that algorithmic document prioritization and batching decreased review times by over 50% compared to manual processes. Their clients also report finding more "hot docs" critical to cases by using AI. Algorithms consistently flag important evidence and relationships that human reviewers might miss reviewing documents linearly.
While AI delivers efficiency through automation, its analytical capabilities also guide improved quality by revealing patterns and insights. These data-driven insights help legal teams continuously strengthen compliance, mitigate risk exposure, and refine processes. AI spotlighting of potential bottlenecks in caseloads enables better workload balancing, for example.
According to Connie Brenton, Chief of Legal Operations at NetApp, "AI enables continuous improvement. The analytics provide data to help improve processes, refine guidelines and assess the effectiveness ofä¸åtion."