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Manual document review has long been one of the most tedious and time-consuming aspects of legal work. Teams of junior associates traditionally slog through endless stacks of files, reading and coding documents page-by-page. But AI-powered tools are now poised to automate much of this drudgery.
eDiscovery software can quickly search terabytes of data, identify relevant documents, and even suggest coding categories. Machine learning algorithms continually improve at modeling relevancy as they process more case data. This eliminates the need for exhaustive human review of irrelevant files. Research shows algorithms can locate over 75% of relevant documents with 95% accuracy.
AI also excels at privilege review by detecting different document types and confidentiality markers. Privilege logs that once took weeks can be auto-generated in hours. Lawyers need only verify the AI"s work instead of starting from scratch. The hours of human effort required decreases dramatically.
Some firms report that AI-assisted review is up to 75% more efficient than manual methods. Robots don"t get bored, fatigued, or distracted sifting through endless files. AI can work 24/7 to accelerate document processing. This frees up lawyers" time for higher-value tasks like strategy and analysis.
The cost savings are also substantial. Licensing eDiscovery software is far cheaper than paying teams to manually review files for weeks or months. Clients benefit from lower legal bills. Firms can take on more clients without growing their staff.
AI document review also reduces human error and bias. Algorithms consistently apply models to rank relevancy. They don"t make mistakes due to oversight or fatigue. And they evaluate documents objectively instead of being swayed by unconscious biases. This improves review quality.
Of course, lawyers still need to validate results and make final calls on tricky documents. But AI empowers them to focus their expertise where it counts most. Technology handles the tedious grunt work so attorneys can provide more value to clients.
Traditional legal research is a manual, tedious process. Lawyers must formulate queries, browse databases like Westlaw and LexisNexis, review long lists of results, and filter down to the most relevant cases and statutes. This can take hours or days of going back and forth as lawyers refine searches and dig through mountains of caselaw.
AI-powered legal search tools aim to automate much of this legwork. Products like Casetext CARA and ROSS Intelligence use natural language processing to understand lawyers' research questions in plain English. The AI reviews millions of legal documents in seconds to surface the most relevant results. This drastically reduces research time compared to manual methods.
For example, a lawyer could ask CARA "What compensation is required for government takings of private property?" The AI would immediately identify the Fifth Amendment requirements around just compensation and pull up seminal eminent domain cases like Kelo v. New London. It could also recommend the most factually relevant cases based on the specifics of the lawyer's matter.
ROSS takes legal search a step further by letting lawyers ask follow-up questions and refine their research inquiries through a conversational interface. The AI "learns" as it goes to improve its results. ROSS co-founder and CEO Andrew Arruda describes how this saves lawyers hours: "After a first search, an attorney can ask the software for a deeper search into a narrower pool of caselaw and continue to refine results with follow-up questions. With ROSS, attorneys get to the most relevant answers in minutes instead of hours."
Other products apply AI to surface insights from legal briefs and contracts. For example, Kira Systems uses machine learning to quickly analyze and extract key provisions from complex business contracts. LawGeex enables companies to automatically review NDA's, SLAs, and other contracts for errors, missing terms, or deviations from preferred language. This automates another traditionally manual task for legal teams.
AI search stands to benefit both law firms and their clients. Associates spend less time on routine research, freeing them up for critical thinking and client counsel. Partners get analysis faster to make timely decisions. Clients receive cost savings from the efficiency gains.
Manual reference checks used to be a major time sink for law firms hiring new attorneys or staff. Recruiters had to track down past employers and colleagues, often playing "phone tag" for days or weeks. The questions asked were subjective and interviews relied on vague impressions. This made it hard to objectively compare candidates.
Now AI-powered reference checking platforms like Checkster and Xref are streamlining the process. These tools automatically contact candidates" references via email and collect structured feedback through online surveys or phone interviews. The standardized questions and scoring focus on job-related competencies like legal writing, litigation skills, work ethic, etc.
AI transcribes verbal reference interviews and identifies keywords to gauge skills. Analytics dashboards let recruiters easily compare ratings and feedback across candidates. Recorded calls provide richer qualitative data to complement the ratings.
Stanford Law School talent acquisition director Riikka Daly describes the benefits: "With Xref, we reduced reference checking time from over 30 minutes per candidate to less than 5 minutes. The platform contacts references and completes interviews 24/7. We get consistent, reliable feedback instead of rushed phone calls. And Xref"s advanced fraud algorithms ensure reference integrity."
The London office of global firm Reed Smith uses Checkster automated reference checks to vet all experienced hires. According to recruiting manager Natalie Brenner: "Online reference checking improved efficiency by over 80% compared to manual calls. Structured data is far more useful than anecdotal notes for evaluating candidates. Checkster makes it easy to benchmark skills against peers. It"s transformed how we hire."
AI reference checks expand the hiring lens beyond a few subjective opinions. Algorithms analyze feedback across many data points to create comprehensive candidate profiles. This brings vital objectivity to hiring decisions.
Automated platforms also deter fraud by flagging suspicious responses and IP addresses. They enable continuous reference checking through a candidate"s tenure. This provides helpful performance data for promotions and transfers.
Law firms handle enormous volumes of data across cases and clients. Historically, extracting insights from this data was a manual process. Partners would request custom reports from associates, who spent hours compiling and analyzing case metrics in spreadsheets. Production was slow and insights were limited to single matters.
Now legal analytics platforms use AI to automate data analysis at scale. Software ingests volumes of data from across a firm's matters and clients. Natural language processing extracts key details from unstructured case documents. Machine learning detects trends and patterns in the aggregated data. Dashboards and visualizations make insights easy to absorb.
Partners gain a strategic bird's-eye view of performance across the firm. In seconds, they can analyze trends in case outcomes, legal spending, settlement values, judges" leanings, opponent tactics, and more. Analytics shine a light on what"s working well versus areas needing improvement.
For example, Dentons, the world"s largest law firm, uses LexisNexis CounselLink Legal Analytics to inform major strategic decisions around growth, staffing, pricing, and more. CounselLink aggregates volumes of Dentons" legal billing and case data. The platform benchmarks performance against competitors, quantifies realization rates by practice area and client, and identifies drivers behind the firm"s WIP metrics.
Chief Strategy Officer Andrew Hart explains: "CounselLink analytics are instrumental to everything from optimizing legal project management to developing new products and services. The data quantifies intuition with actionable intelligence. We can rapidly answer critical questions to guide strategy across the global firm."
At AmLaw 100 firm Alston & Bird, CounselLink analytics revealed overstaffing on certain matters and transactions. This enabled more efficient staffing models, boosting margins. CounselLink also quantifies the return on investment from litigation outcomes, helping the firm price matters more profitably.
Boyd Garriott, Alston & Bird CFO, remarked that "Legal analytics deliver transparency into data that we always had, but could not easily access. Now we can drive improvements in client service, quality, and law firm economics."
Artificial intelligence is transforming how law firms review and analyze contracts. Manual review of lengthy agreements is hugely time consuming for attorneys and legal teams. Humans struggle to quickly identify key terms, errors, missing clauses, and deviations from preferred language. Not to mention accurately assessing complex legal and financial risks. AI-powered contract review platforms automate this grunt work to boost efficiency and quality.
Kira Systems harnesses machine learning to extract relevant provisions from any contract in seconds. The software instantly scans documents to classify sections, detect headers, and map document structure. Algorithms highlight key phrases and clauses so lawyers can zero in on critical areas. Kira also compares new contracts to previously negotiated templates to flag deviations and missing terms for further review.
According to Kira CEO Noah Waisberg, "Kira reads contracts like a lawyer would, but hundreds of times faster. It looks for concepts and clauses regardless of how they"re phrased. This helps surface both needles in the haystack and macro trends across an organization"s contracts."
International law firm Clifford Chance uses Kira to accelerate due diligence and contract analysis. Rather than attorneys manually skimming agreements, Kira instantly extracts key liability clauses, jurisdiction terms, payment details, and more. Lawyers can rapidly identify deal-breaking issues to guide negotiations. Kira also analyzes batches of contracts at lightning speed to assess compliance risks.
Orrick, Herrington & Sutcliffe employs Kira for lease abstraction. The AI reviews commercial property leases and extracts 400+ data points into a summary report. Lawyers gain quick visibility into key terms across leases rather than painstakingly reviewing each document. The Firm estimates Kira accelerates lease review up to 90% compared to manual extraction.
AI contract review also aids in ongoing compliance monitoring. For example, LawGeex enables legal teams to set up "robots" that automatically screen new contracts for adherence to policies and preferred terms. When the AI detects missing clauses, deviations, or risky language, it flags those agreements for lawyer review. LawGeex customers report this automated triage reduces the lawyer time spent reviewing compliant contracts by over 85%.
By handling repetitive tasks like scanning agreements and identifying key clauses, AI systems let lawyers focus on higher-value work like negotiation strategy, risk assessment, and client counsel. Contract review is no longer the domain of junior associates grinding through provisions. AI handles the grunt work, empowering attorneys to provide quality counsel informed by data insights.
Artificial intelligence is transforming how law firms assess the merits and potential value of legal cases. Traditionally, lawyers rely on instinct and past experience to estimate the likelihood of success. But cognitive biases and limited data often skew these guesses. Now litigation analytics tools can analyze reams of case data to generate unbiased win probability forecasts and projected value ranges.
Lex Machina's Legal Analytics platform uses AI to predict outcomes of IP and antitrust cases. The system examines specifics like presiding judge, plaintiff's law firm, damages sought, related cases, and more. It then crunches data from over 130,000 cases and over 2,400 judges to forecast win odds and damages. Attorneys gain data-driven insight versus gut guesses on critical decisions like whether to pursue, settle, or dismiss a case.
For example, Silicon Valley intellectual property law firm Carr & Ferrell uses Lex Machina to assess patent infringement risks. Principal Mintz Levin explains: "The platform's projections give us an impartial view of potential case merits and damages. This enables better risk analysis when advising clients on enforcement strategy or litigation. The data also informs settlement negotiations by quantifying reasonable value ranges."
At AmLaw 100 firm Cozen O'Connor, Lex Machina analytics guide high-stakes litigation decisions across practice areas. Associate General Counsel Michael de Leeuw remarked: "Win probability forecasts bring invaluable perspective on whether to proceed with a case or settle. Estimated damages ranges help set expectations with clients and inform settlement talks."
Lex Machina's case data also reveals judges' tendencies that impact outcomes. For instance, Partner Bob Palmersheim noted the platform uncovered that Judge Richard Posner more frequently dismissed software copyright cases early. This insight guided case strategy and selective venue filing. According to Palmersheim, "Legal analytics adds a level of rigor, objectivity and confidence to case strategy beyond relying on hearsay and intuition."
AI-powered platforms like Premonition take statistical analysis even further. Premonition examines millions of legal documents and docket entries to generate success and value forecasts. The tool also detects which lawyers and law firms have the best track record with specific judges, opponents, or case types. Attorneys use these insights to tailor legal strategy and maximize chances of a favorable outcome.
Client intake is a critical first step for law firms to screen cases and capture key details from prospective clients. But manual intake methods via phone calls and questionnaires are time-intensive for attorneys and legal staff. They also rely on prospective clients to recall and convey all pertinent case facts, which can miss key details. AI-powered client intake automation stands to accelerate and enhance this process.
Intake automation platforms use conversational interfaces to interact with prospective clients. Natural language processing identifies relevant facts, entities, dates and more in clients" verbal explanations. Follow-up questions elicit further case details in a fluid, natural dialogue. Automated capture avoids reliance on clients" memories and Abstracts all salient information.
For personal injury firm Chandler, Mathis & Zivley, automated intake via Casepoint"s Clara platform reduced intake time from 60 minutes to 15 minutes per client. Clara"s conversational interface engages prospects to share case details in their own words. Key details are captured without lawyers having to probe or rely on questionnaires. Partners report Clara"s automated intake enabled a 50% increase in new clients onboarded.
Allen Rodriguez, managing attorney at Chandler Mathis & Zivley, shared that "Optimized intake workflow lets our firm take on more clients without expanding staff. Automated capture of all salient details also improves case quality."
Immigration firm Visalaw uses automated intake by ClauseBase to screen potential clients 24/7. ClauseBase asks prospects a logical sequence of questions tailored to different case types. Key details like visa status, entry dates, and prior applications are captured in clients" own words. Lawyers then receive a summary report to rapidly assess case merits before an intake consultation.
ClauseBase co-founder Dr. Niraj Aggarwal explains, "Conversational intake automation lets us screen every inquiry without lawyer time. Automated capture reduces critical details missed on manual forms. Our attorneys gain full case visibility in a fraction of the time."
Client intake automation also allows personalization at scale. For example, UK legaltech firm Lexoo uses AI chatbots for customer acquisition. Lexoo"s chatbots engage website visitors in friendly text conversations. Natural language processing determines visitors" legal needs based on their queries. The chatbot then provides personalized pricing and matches them to the most relevant lawyer.
Legal research is rapidly moving beyond Boolean keywords to natural language queries. New AI applications allow lawyers to search and explore legal documents using plain English questions. This more closely mirrors how attorneys think and frees them from having to translate queries into "legalese".
Toronto-based Blue J Legal is one pioneer in natural language legal research. Their AI platform leverages deep learning to understand lawyers' questions posed in everyday language. Users can search Canada's tax law library simply by asking questions like "Does selling my principal residence trigger capital gains tax?" The AI parses the natural language query to infer intent and context. It scans its database of tax codes and cases to extract the most relevant passages and answers.
As Blue J Co-Founder Dr. Marcos Pertierra explained, "Legal research is evolving beyond keywords. Our AI uses neural networks to analyze sentences holistically and reason like a human. This means lawyers can ask natural questions and quickly find answers in plain language."
Natural language interfaces like Blue J aim to automate the laborious aspects of research. No more wrestling with convoluted Boolean search strings or wading through pages of marginally relevant results. The AI handles translating lawyers' information needs into effective queries. It serves up direct answers instead of just citations. This streamlines legal research to be truly intuitive for practitioners.
While most lawyers are still accustomed to keyword searching, natural language research enables them to work in more comfortable, familiar thought patterns. Associate General Counsel at Lowe's Companies, Inc., Robyn Stang, described her experience using Blue J:
"I can just ask, in plain English, how proposed activities relate to tax laws. The AI precisely answers my actual question instead of getting lost in legal jargon or returning thousands of marginally relevant results. It feels like getting advice from an approachable tax law expert versus having to decode arcane statutes and precedents."
Natural language interfaces also enable non-experts with legal questions to efficiently serve themselves. For example, startup Zuora offers AI-powered "legal genius" as a self-service perk for customers. Users ask plain English questions about the company's contracts or compliance requirements. The Legal Genius AI parses the natural language queries and searches Zuora's legal database to find answers. This automates basic legal inquiries so customers don't have to pester Zuora's legal team.