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The legal industry is on the cusp of a technology revolution that promises to reshape the role of associates at major law firms. While artificial intelligence has already infiltrated corners of legal work like discovery and research, its next frontier is assuming more lawyerly responsibilities. Enter the "robo-associate" - algorithms trained to handle legal tasks once reserved for humans.
Document review has long been the grunt work of first-year law firm associates. But now AI tools can analyze hundreds of pages in minutes through optical character recognition, machine learning and natural language processing. Projects that once took weeks can be done in hours without the drudgery of poring over boxes of files. Law firms are already seeing 30-60% time savings by combining associates and smart document review software.
Research is another area being augmented by AI. Algorithms can rapidly analyze case law, statutes and past firm work product to find the most relevant materials. This can turbocharge the preparation process and allow associates to focus on devising case strategy. Research that used to be farmed out to cheaper attorneys can now be handled in-house with AI support.
AI writing tools are even stepping in to help draft legal documents. While not yet capable of handling complex briefs solo, algorithms can generate first drafts, contracts, memos and letters that associates then review and finalize. This saves associates from starting from scratch, freeing up time for higher-level analysis.
eDiscovery powered by AI is also making document production less arduous in the age of ballooning data volumes. Predictive coding algorithms can find "hot docs" in expansive data pools through continuous active learning. This allows associates to skip laborious manual document review entirely in some cases.
When it comes to billing, AI tools are boosting efficiency as well. Automated time capture eliminates manual entry by pulling billable activities directly from calendars, documents and timesheets. Machine learning models can then recommend appropriate billing codes and task categorization to optimize the process. This saves on administrative costs and reduces human error risks.
Document review has historically been one of the most labor-intensive and mind-numbing aspects of legal work. Associates are routinely tasked with reviewing hundreds if not thousands of pages of materials to identify key documents, extract important information, and assess case relevance. Before AI, this meant long hours poring over boxes of files, folders, and loose pages - not exactly the "Perry Mason" courtroom drama that lures many to law school.
Now AI is stepping in to automate large portions of document review, freeing associates from the most tedious aspects. Algorithms can rapidly scan and index uploaded documents through optical character recognition (OCR), converting them into machine-readable text. Natural language processing techniques identify concepts, entities, relationships and other semantic information within the text. Machine learning models are then leveraged to classify documents based on tags and continuously improve categorization accuracy.
With the grunt work handled by algorithms, associates are left to focus on higher-level strategic tasks. For example, Wilson Sonsini automated the process of redacting and logging privileged documents with Neota Logic software, reducing review time by 60%. DLA Piper uses Brainspace"s AI to rapidly sort case documents, clustering those with similar fact patterns and legal issues. This allows associates to quickly find precedential materials.
Clifford Chance deployed Eversheds Sutherland's Kira machine learning to review acquisition agreements. Kira accelerated contract review by over 90% compared to manual methods. Now associates can focus on devising negotiation strategy rather than line-by-line contract scrutiny. This allows them to take on more matters with the time savings.
Legal research is the backbone of building strong legal arguments, but trawling through volumes of case law, statutes, and scholarly articles is enormously time consuming. AI looks poised to revolutionize legal research through machine reading and natural language generation. By digesting huge swaths of legal materials in seconds, algorithms can rapidly surface the most relevant sources for attorneys.
Several startups now offer AI-powered legal research tools that allow lawyers to get to the right precedents and references much faster. Casetext"s CARA algorithm reviews the language used in a drafted legal brief and suggests additional supportive materials from its database of over 3 million briefs, case law, statutes and more. Ross Intelligence builds on IBM"s Watson technology to offer an AI legal researcher that can respond to natural language queries, summarize long legal documents, and continuously improve its responses through machine learning.
Both CARA and Ross promise to cut research time in half or more. A recent study found 3rd year law students using Ross were able to find relevant materials in about 26% of the time it took manually. LawGeex"s AI can review contracts and identify missing terms, compliance issues, and potential red flags in seconds versus the hours it would take lawyers poring over dense legal prose.
Big law firms are also developing their own AI tools to boost associates" efficiency. Baker & Hostetler trained Neota Logic"s AI on 50 years of its legal work to create COIN, an in-house advanced search system for the firm"s corpus. COIN provides lawyers relevant background materials to precedent cases automatically, skipping slow conventional research.
Orrick Herrington & Sutcliffe created an internal tool called Orion that searches court dockets nationwide to identify upcoming appeals that may impact current cases. This allows associates to get a jump on how new rulings could affect their legal strategy. Clifford Chance built an AI prototype called Scope that scans UK and EU regulations to assess potential impacts of Brexit on finance deals.
Legal drafting is a complex and time-intensive process that requires synthesizing facts, legal principles, and language into cohesive written work products. Associates have traditionally shouldered the lion's share of initial drafting for pleadings, discovery, motions, memos, contracts, and other documents. But AI tools are stepping in to automate pieces of the drafting process to boost associate productivity.
At the most basic level, tools like Kira and LegaI by Design offer templates and clause libraries that insert commonly used legal language into documents with a click. This saves associates from repeatedly drafting routine boilerplate sections.
More advanced contract AI like LawGeex speeds up drafting by automatically surfacing non-standard terms in new contracts and suggesting modifications based on past firm documents. This allows associates to quickly customize templates rather than starting from scratch.
For general memos and motions, tools like Casetext CARA and LegalSifter analyze uploaded documents like briefs and legal memos, then suggest additional relevant citations and arguments that associates can insert to strengthen their drafts. This automates finding supplemental materials to back arguments.
When starting from a blank page, tools like ThoughtRiver and Lawyaw use natural language generation to create first drafts of memos, letters, and motions based on prompts like desired arguments and document goals. The AI assembles logically structured and cited documents that associates can then tweak as needed rather than manually building from nothing.
As data volumes have exploded in the digital age, sifting through expansive document pools to identify materials relevant to legal cases has become increasingly onerous. eDiscovery, or identifying, preserving, collecting, processing, reviewing, and producing electronic documents related to litigation, traditionally requires associates to spend countless hours manually reviewing files. But now AI is stepping in to remove much of the drudgery of eDiscovery.
Algorithms can rapidly scan terabyte-scale document sets to flag those most likely to contain case-relevant information. Natural language processing identifies concepts and semantic relationships within documents to assess topicality. Machine learning models continuously improve at identifying "hot docs" most pertinent to legal matters. This allows associates to skip manually reviewing every single document, instead focusing their time on the materials most likely to impact the case.
According to Casey Flaherty, principal at legal tech consultant Procertas, "In some cases, AI-assisted reviews are so accurate that statistical models show manual review would not find any more documents than the AI already located." For example, legal AI provider Everlaw accelerated a mortgage-backed securities case document review by 95% compared to manual processing. The relief charity Kids in Need of Defense saw document review time drop from weeks to hours using predictive coding for immigration cases.
Matthew Holohan, a commercial litigator at Bennett Jones LLP, highlights how AI eDiscovery improves efficiency and outcomes: "I can now get the key documents sooner, allowing me to build a theory of the case faster and go into examinations fully loaded. It lets me focus on lawyering rather than document processing."
While AI excels at finding relevant needles in massive document haystacks, lawyers retain a key role guiding case legal strategy. As Michele Lange, Director of Thought Leadership at Kroll, an eDiscovery provider, explains: "AI doesn't know what questions to ask when determining relevancy, so it needs humans to provide that context around issues in the case." Associates adept at contextual eDiscovery AI can thus handle more cases simultaneously with less fatigue.
Billing is one of the least glamorous but most important aspects of legal work. Billable hours make up the lifeblood of law firm economics. But manually tracking, categorizing and billing time often sucks up associates' hours that could be better spent on substantive legal work. This is where AI billing solutions come in to optimize workflows.
Intelligent time tracking software removes the need for associates to manually log every minute of their day. Programs like TimeMiner automatically capture activities through integrations with email, calendars, documents, phones and more. Machine learning algorithms then analyze this activity data to make suggestions for time categorization and coding based on past patterns. Associates simply review and confirm the AI's recommendations with a click rather than building timesheets from scratch.
According to Lisa Hartkopf, COO at commercial firm Brownstein Hyatt Farber Schreck, since implementing TimeMiner her firm has seen time capture increase 25%, with over 50% of hours captured automatically. This translates to over 130,000 billable hours recovered annually that were previously lost through human error and distraction.
Other solutions like LexMac's Billseye leverage AI to recommend the most appropriate billing codes for logged activities based on analyzing thousands of past examples. This helps optimize billing classification so that work is accurately represented. As shareholder Frances Dewing notes, Billseye has saved her firm Dykema over 400 hours a year in manual billing code review.
AI can also ensure adherence to billing guidelines and prevent "padding" of hours through analysis of time increments. Richard Granat, CEO of legal research platform ThinkSmart, notes that algorithms can discern patterns like repetitive 0.1 hour time entries and flag potential violations. This protects both the firm and clients.
On the client end, AI is being used to audit and analyze legal bills for potential fee disputes. Case in point: when insurance giant Zurich analyzed its legal spend data with Brightflag's AI, it identified over $2 million in excess legal fees incorrectly classified or overbilled.
According to CEO of legal AI service Beagle.ai, Vijay Chokkalingam, algorithms can examine thousands of invoices and retainer agreements in seconds, saving clients time while optimizing law firm processes: "It's a win-win for both sides."
Law firm associates have traditionally been immersed in a culture of intense competition, cutthroat billable hour quotas, and a "sink or swim" mentality when it comes to on-the-job training. But as AI legal tools proliferate, a new spirit of collaboration over competition is emerging that allows junior associates to learn the ropes with reduced stress. Many now see algorithms less as adversaries, but as robot coworkers handling the grunt work and supporting associates as they gain experience.
As Luke Kelly, a first-year associate at Latham & Watkins recalls of his early days, "I felt like I was thrown in the deep end those first few months. I had to constantly beg more senior associates for help finding case law and putting memos together. It was incredibly inefficient and frustrating." But now new recruits at his firm and others are partnered with AI tools from day one to augment their skills. Luke uses natural language processing-powered legal researcher Ross Intelligence to quickly find the most legally relevant materials to support his legal arguments. Drafting tool Lawyaw then assembles his research into logically structured memos he can refine instead of writing completely from scratch.
Junior associates see AI accelerating their ability to work independently and manage their own caseloads. Many also praise how AI helps avoid "reinventing the wheel" by automatically surfacing past work products relevant to current cases. As Alan Devore, a newly minted associate at Goodwin Procter, explains: "Whether it's AI-powered document review, contract analysis, billing, you name it, I can hit the ground running. It lets me focus on high-value work for clients right away instead of spending months buried in repetitive tasks."
Rather than AI automating away entry-level work, in many cases algorithms are handling the most tedious aspects while leaving the pieces requiring human skills and judgement. For instance, contract review AI like LawGeex flags unusual clauses then leaves it to associates to assess risks, negotiate terms, and advise deal strategy based on client goals. Billing automation tracks activities then relies on associates to confirm appropriate task categorization.
AI collaboration is also fostering improved work-life balance among new recruits, an important priority. Lena Wu, a junior associate at Gibson, Dunn & Crutcher, finds that AI tools allow her to get work done faster with less stress: "I'll review a hundred documents flagged by our AI system, then use the rest of my night to hit yoga or meet up with friends instead of poring over boxes of files."
The future of the legal profession will be one of human-AI collaboration, with technology amplifying rather than replacing the unique skills and judgment of lawyers. As AI handles rote tasks like document review and research more efficiently, attorneys will be empowered to focus on higher-value analysis, strategy, client counseling and litigation activities.
According to Jordan Furlong, global analyst at Law21, "The best way to think about the rise of legal AI is not automation but augmentation. AI won't replace lawyers; it will make them better at what they do." For instance, contract review AI doesn't aim to fully automate deal lawyers. Rather, it speeds the review process through clause analysis and templating so attorneys can spend more time on negotiating leverage and creative solutions.
As Michael Mills, Chief Strategy Officer at legal AI provider Neota Logic explains, "AI doesn't do the whole job, just the tedious high-volume part humans don't want to do. This frees lawyers to have more human interactions and use their skills where they add the most value."
Many firms are already realizing productivity gains through augmenting associates with AI. AmLaw 200 firm Quarles & Brady saw time spent on research and drafting for a complex ERISA lawsuit drop from weeks to hours after implementing case law analytics tool vLex Justis. This allowed associates to focus strategic planning rather than document processing.
According to Dennis Garcia, Assistant GC at Microsoft, "AI is allowing lawyers to return to what originally drew them to the profession: thinking critically about complex legal issues and providing expert counsel." Rather than fearing automation, he sees lawyerly skills becoming more critical: "An AI won't empathize with a client over a cup of coffee anytime soon."
As technology handles repetitive tasks, lawyers will also be able to focus more on professional development through training, mentorship and skill acquisition. Stephen Poor, Chairman of Seyfarth Shaw, points out: "We spend less time worrying about utilization rates and billable hours now. Associates have more space for creative thinking and expanding their expertise."