How does it feel to sit down with a long list of custodians and their interview responses, preparing your case strategy for a new matter you know will entail a long, drawn-out review of millions of documents that are mostly irrelevant, but must be checked anyway?
Or to open a review workspace post-ingestion, see that the document count has ratcheted up from “difficult” to “actually quite impossible,” and begin to determine how to carry your case forward into the quagmire?
“Untidy,” Pippin declared when describing Fangorn Forest in The Two Towers—book two of J.R.R. Tolkien’s The Lord of the Rings trilogy.
“Just dim, and frightfully tree-ish,” Merry concurred.
Does it feel a bit like that? (With the possible exception of tree-ish?)
As I sat down to write this article and reached into my English major for a good metaphor, I was reminded of the Fellowship’s journey through the forests of Middle Earth, particularly Fangorn Forest: home of the Ents—ancient, giant, tree-like shepherds of the forest.
(I do hope Tolkien will forgive me; sometimes life imitates art.)
Spunky Hobbit, cunning Man, and brave Dwarf alike all hesitate to set foot in Fangorn Forest. They fear the woods; legend says the place is cursed.
Even Legolas, an experienced Elf of the Woodland Realm, proceeds with caution. Still, using his sense for these things, he deems it safe enough.
"I do not think the wood feels evil,” he says to his companions. “It is old and full of memory.”
The data in your most forbidding workspace is no different. It may only be “old” in technology years, but it’s vast and dense enough to seem endless. And full of memory, indeed.
As today’s lawyers find themselves encountering new tangles of data volumes and complexities, they generally won’t find any Ents to herd trees (or smite Orcs) for them. But they will find some friends and a few tools to help them uncover the insights that matter as quickly as possible.
“This shaggy old forest looked so different in the sunlight,” Pippin later says as they leave the forest to continue their expedition. “I almost felt I liked the place!”
A bit of sun, a good team, some creative thinking, the right tools: these are enough to get an intrepid person through just about anything.
Even a really perilous document review.
The reality of document review has changed dramatically. The day-to-day tasks it entails and the broader, bird’s-eye view of what it means look fundamentally different than they once did.
“My first projects involving document review were with paper, including emails that had been printed. My first electronic document review was a Lotus Notes project that was done by installing Lotus Notes on my office desktop, loading in client emails, creating Responsive and Not Responsive folders in the inbox, and then sorting emails into those folders,” recalled Tara Emory, senior vice president of legal AI strategy and general counsel at Redgrave Data. “Obviously, much has changed.”
We all know e-discovery hasn’t happened on paper in years, and it also isn’t predominantly about emails anymore. In fact, it’s not always simply a doc-to-doc exercise at all (what is a Slack “document,” anyway?). And the question of automation and AI isn’t an “if” or even “when” so much as “how.”
Fortunately, the practice of law is guided by a steady compass: delivering zealous and ethical representation for one’s clients, to the best of one’s abilities. Modern legal technology—like Relativity aiR for Review, which leverages Azure OpenAI Service to locate data responsive to discovery requests—being, simply, a better ability.
Excellent legal practitioners are skilled at finding the truth in a complicated story. They know how to prompt witnesses and custodians with pointed but open questions that get to the heart of an issue without undue influence. They excel at spotting an important thread and tugging on it just right, until facts become evident.
In other words: a lawyer’s interrogative skills and their sense of curiosity can help reveal the appropriate path through unfamiliar wilds of data, technology, and workflows. Asking the right questions—of colleagues and of vendors—enables collaboration and the responsible application of technology, particularly artificial intelligence, to modern matters. It can also build a trajectory for professional development in a high-tech, high-stakes, high-expectations legal landscape.
“The thing is, e-discovery has been used as a catch-all for solving legal data problems over time. While I was in a law firm, I was asked to do more and more projects that involved analyzing data that weren’t just for responding to discovery requests,” said Cristin Traylor, senior director of AI transformation and law firm strategy at Relativity, during a recent conversation. Prior to joining Relativity, she spent 20 years practicing as a litigation and e-discovery attorney at McGuireWoods LLP. “People would come to our group for contract analysis, data breach responses, due diligence, HR data analysis, and more.”
Following the breadcrumbs of discovery-adjacent projects and expanding her skills has helped Cristin—and many others like her—evolve her career into something fascinating and deeply helpful to countless colleagues and clients.
For legal professionals looking to keep pace with the transformation overtaking document review, asking those thoughtful questions is the best way to blaze a trail through a forest of data. The first ones to ask include:
- How can I leverage modern technology in a proportional, reliable, and defensible manner to ensure my case team generates the fastest and most insightful results during a doc review?
- How can I prove the ROI of these technologies to my colleagues, clients, and other stakeholders?
- How can I land on an approach that not only improves my work product, but also helps me advance my career?
Don your proverbial boots and binocs and let this be your guide through the tangles of trees.
“Just, speedy, and inexpensive” is the mantra of every lawyer (Stateside, at least) whose work hinges on document review. The “speedy” and “inexpensive” guideposts are moving targets.
In an adversarial legal system, discovery protocols often become an issue counsel must duke out in court—arguing the merits of a certain workflow, or the defensibility of a chain of custody, or the inclusiveness of a search terms list. But as data volumes and complexities grow, the appetite for slow-moving discovery disputes and inquisitions is shrinking.
Courts’ institutionalization and approval of technology often lags behind a timeline suited for innovation. It took six years of formal discussion for the United States’ Federal Rules of Civil Procedure to reflect the use of electronic documents in legal disputes; the changes went into effect in December 2006, well after email became ubiquitous. However, counsel under pressure from clients seeking to do more with less are compelled to embrace accelerative technology much faster.
This can be an uncomfortable prospect for risk-averse lawyers who’d prefer the imprimatur of judicial approval before using new tech. Many wait to see what their competition is doing, thinking that, if other firms use it, it must be safe.
The problem with this thinking is twofold:
- Many law firms like to hold their cards close, protecting the “secret sauce” of any e-discovery processes that help them work faster and smarter.
- By the time these secrets are out, it’s because virtually everyone is doing it anyway. And if you’re the last one on board, you’ve fallen way behind.
Between these two mindsets—the desire to protect any competitive advantage and the desire to know what one’s peers are doing—opposing parties are obligated to discuss the protocols by which they will exchange electronically stored information (ESI) deemed relevant to a matter. FRCP 26(f) stipulates that counsel discusses, among other things,
… a proposed discovery plan that indicates the parties’ views and proposals concerning:
- what changes should be made in the timing, form, or requirement for disclosures under Rule 26(a), including a statement as to when disclosures under Rule 26(a)(1) were made or will be made;
- the subjects on which discovery may be needed, when discovery should be completed, and whether discovery should be conducted in phases or be limited to or focused upon particular issues;
- what changes should be made in the limitations on discovery imposed under these rules or by local rule, and what other limitations should be imposed; and
- any other orders that should be entered by the court under Rule 26(c) or under Rule 16(b) and (c).
In other words: parties discuss how to produce documents from certain relevant custodians, on designated topics, on a reasonable timeline, and in an agreed-upon format.
These provisions are complicated enough, but Cristin says parties have often made meet-and-confer sessions more taxing than necessary as e-discovery has evolved. Sometimes this is borne out of distrust of opposing counsel or the hope of finding errors that can be strategically leveraged; other times, it’s rooted in ignorance of the technology involved.
“ESI protocols were originally intended to help reduce disputes by seeking advanced agreement on things like production format. Efforts to expand the topics covered by protocols to include review methodology and validation protocols have only served to increase disputes and reduce the likelihood that technology-assisted review will be used,” she observed. “In most cases, it is far more efficient for each party to use their knowledge of the case and their own documents to make a timely production and to use technology, which they are ethically obligated to understand, to get the best results.”
A 2021 guide prepared by the Federal Practice Committee of the U.S. District Court, District of Minnesota, specifies that, while counsel and client absolutely should discuss the appropriate methodologies to accelerate their e-discovery strategy in a case, for counsel and opposing counsel, “disclosure, discussion, and agreement on each of the[se] issues … is not a requirement.”
Additionally, published “Recommendations for ESI Discovery Productions in Federal Criminal Cases” from the Department of Justice (DOJ) and Administrative Office of the U.S. Courts (AO) Joint Working Group on Electronic Technology in the Criminal Justice System (JETWG) notes that “parties increasingly will employ software tools for discovery review, so ESI discovery should be done in a manner to facilitate electronic search, retrieval, sorting, and management of discovery information.” These recommendations advise parties to discuss issues like software and hardware limitations and whether legacy or proprietary data formats are present, but the closing checklist says nothing about methodologies or the use of TAR or AI.
Lawyers, obligated to provide competent representation for their clients, must understand their options for optimizing document review—and be empowered to strategize accordingly. That includes, of course, advising on the use of tools like aiR for Review to increase review efficiency and accuracy.
“The assumption that TAR (including review using generative AI) requires more agreements and validation than would be required by a manual review makes no sense to me. Defensibility of the process should only be required if there is an identifiable deficiency or specific concern with the production,” Alison Grounds, managing partner of Troutman Pepper eMerge, explained to me, echoing Cristin’s perspective. “Otherwise, the costs of defending the use of technology will outweigh its benefits.”
She continued: “We never exchanged coding memos or reviewer attorney bios when we did manual human review.”
“The more we haggle over specific metric rates and processes, the more we deter people from using technology, even if it gives everyone better results,” Cristin concluded. “There is something to be said for recognizing that generative AI may be used in your protocol, so there are no ‘gotchas’ in the end—you want everyone on the same page. But we need to recognize that there are lots of tools to get the job done, and we need to make sure we are using those tools defensibly and measuring their efficacy to meet discovery obligations.”
This insight is reassuring, but if the issue of confidence doesn’t come down to opposing counsel’s concurrence—how can lawyers build that confidence in AI’s usability and defensibility for themselves?
It helps that the legal community is universally hungry for reliable standards and will often collaborate on the mutually beneficial exercise of establishing and sharing them.
Tara Emory, for example, recently coauthored an article in The Sedona Conference Journal—alongside colleagues Jeremy Pickens and Wilzette Louis—that sets out to “provide a reference model to serve as a foundation for first-pass workflows that use artificial intelligence/machine learning to integrate them into the established process of TAR.”
In other words, e-discovery practitioners are taking what they’ve learned from older models of technology-assisted review—known as predictive coding, which arrived on the scene more than 10 years ago—and applying it to generative AI. They’re using established precedent to obtain greater efficiencies via contemporary technological innovations.
Back to those thoughtful questions about leveraging technology in a proportional, reliable, and defensible manner: its ability to hold up to scrutiny in a courtroom rests on its ability to hold up to scrutiny of its security, usability, and accuracy.
“You have done your due diligence by selecting a solution that is tested and better than human review,” stated Cristin Traylor.
Few practicing lawyers have the time or expertise to exhaustively test each piece of tech they consider onboarding for their e-discovery matters. That’s a software vendor’s job, and lawyers are beholden to, well, hold them to it.
Good partnership—a willingness to explore, together, the unique needs of each client, matter, and organization—is how e-discovery practitioners and software developers can collectively move the needle and keep pace with mushrooming data volumes and data complexity.
“e-Discovery professionals must understand the context of their work: What is the client’s end goal? What are the actual legal issues in dispute? What facts are needed to prove/disprove defenses or support claims? Without that context, expertise, and knowledge, it is impossible to have an effective document review project,” Alison said.
Savvy attorneys bring that context to their technology partners, whether they’re evaluating potential investments in in-house software or engaging a service provider to help.
“The evolution of TAR to now include generative AI solutions requires all legal professionals dealing with document review to understand how the various solutions work, and how to architect these solutions into the most effective document review game plan for a specific matter or client,” Alison told me. “The key to future success in our space is to continue finding pragmatic solutions that reduce the cost and improve the accuracy of analyzing information necessary to resolve a legal matter in the best way for the client.”
This strategizing necessitates asking a lot of questions about how a particular tool will meet these needs in practice.
Will this tech ensure that the highly sensitive data involved in each matter will remain private and secure? Is it rigorously tested in its ability to do so, and does it have certifications or designations to prove it? The security of the cloud is well established by now, but maintaining the pressure to keep data protection top of mind both internally and with your providers is never a bad idea. Looking for qualifications like FedRAMP ATO, HIPAA compliance, SOC 2 Type II and III, and others can help validate a product or provider’s cybersecurity chops.
What about the sometimes-confusing territory of ethical artificial intelligence? Is a tool's AI leveraged in a just, thoughtful way—mindful of algorithmic bias, respectful of data used for training, and fit for purpose rather than flashy? Exploring a developer’s guiding principles for AI innovation is an excellent way to see how seriously they take issues like these.
And, especially urgent for those looking at generative AI in sensitive legal use cases: how does a developer instill confidence in their AI product’s recommendations? How do they mitigate risk of hallucinations?
Hallucination, in the context of generative AI, is when a model provides outputs that are inaccurate or simply fabricated. You might ask a chatbot a question, for example, and get a decent, if brief, answer. But if you ask for more, again and again, it might eventually start making up responses to fulfill your prompt—even though it’s fresh out of information.
“Text-based generative AI systems work by analyzing vast amounts of text and learning to predict the next word in a sequence based on the words that come before it. This allows these types of models to generate coherent and contextually relevant text when prompted,” explained Nathan Reff, a manager of applied science at Relativity.
“Because of this feature on predicting sequential words, these models can sometimes generate incorrect, misleading, or completely fabricated information. We call these ‘hallucinations.’ There are other reasons why these models can hallucinate as well, such as the underlying training data quality, and complexity of prompts provided,” he continued.
“For most legal tasks, including document review, we want definitive facts and predictions to be grounded in the underlying matter, following complex rules and procedures in a way that mitigates the possibilities of hallucinations occurring,” Nathan explained.
We sure do.
But how?
According to Nathan, Relativity aiR for Review is engineered “to provide evidence for document predictions in a way that is familiar to how actual review teams operate—which is to not only make decisions on whether a document is or is not what they are looking for, but also provide actual citations from the documents on why they are coming to a particular conclusion.”
A user instructs aiR for Review to retrieve documents relevant to an issue using a natural language prompt.
The user defines relevance criteria or issues …
… and defines the criteria that makes a document “key,” such as “hot” or “important.”
The software turns that instruction into a technical prompt, effectively creating a review protocol for the algorithm.
aiR evaluates the relevance of each document in its entirety, one by one, like a human only at machine speeds.
When it’s finished, aiR displays a list of documents predicted to be relevant.
Clicking in, one can see citations from the text itself …
… its rationale for why it has made a determination …
… as well as considerations for why its determination might be incorrect.
“In addition to extracting citations from the documents, we have built aiR’s model to provide a written rationale for its decisions. We also have the model reflect on why it might be incorrect—essentially playing devil’s advocate on itself, telling the human reviewer what they might want to consider as they evaluate its results,” Nathan said. “Finally, aiR for Review offers a prediction on the strength of its decision. All of these elements together provide a more transparent experience for review teams.”
Relativity engineers took it one step further to guard against hallucination (good engineering is all about thoughtful redundancies).
“We use an independent algorithm to validate that the citations are truly in the document, and we highlight these for the user,” Nathan explained. “This gives our model not only transparency but defensibility, and lifts these insights to the user, front and center, as fast as possible.”
So, much like a traditional, manual review, attorneys using aiR provide a review protocol concerning their case and what subject matter is potentially relevant; the AI reviewer reads through the data set and makes decisions on potential relevance based on what’s written within the proverbial four corners of each document; and relevant documents are collected with contextual highlights and written notes from the reviewer on why they made each decision.
And that’s aiR for Review.
Now, back to Fangorn.
While you may feel equipped to confront the data challenges standing between your client and their just, speedy, and inexpensive resolution, the task of receiving stakeholder approval to do this work in contemporary ways—with generative AI—remains.
Defensibility, accuracy, security. All of these are inarguably essential. But money talks, too.
Traditional conversations about the return on investment of e-discovery strategies have relied on familiar metrics: doc-to-doc speeds and docs per hour, richness, volumes culled. The advent of TAR introduced some new ones: precision, recall, elusion, and overturns.
Each of these metrics remains important and aids in tracking project progress, evaluating how a team performs across multiple matters, measuring the efficiency of a strategy, keeping tabs on budgets and resources, and documenting a team’s pursuit of proportionality.
And it’s true that document review is a numbers game. You start with millions of pages of information and may end up with just a few dozen exhibits. That’s some serious math.
Still, the days of assembly-line e-discovery protocols completed via rote work are fading. When you start to shift from seeing data as a liability and begin to recognize the insights it hides (albeit beneath unnervingly large numbers), your sense of curiosity can begin to carry you through the trees with eyes wide open—ready to discover something fascinating.
“How ROI is defined can be very different in e-discovery and legal in general. A better legal outcome, more time, less hours, lower cost—these tend to be slightly more important than straight revenue in many cases,” Chris Haley, vice president of practice empowerment with aiR at Relativity, told us in a recent interview. “There are many times that firms in particular will invest in a technology solution that may have negligeable direct revenue or margin impact, but it may lead to winning more work, be seen as a differentiator in an RFP, or make the work more efficient or more profitable in an alternate fee arrangement.”
Business clients seeking outside counsel want to see a balance of thoughtfulness, efficiency, and innovation. Facing tighter budgets and high expectations from their own organizations, they increasingly demand accountability for billable hours and exceptional legal advising.
Investing, and advertising that investment, in technological innovations that improve efficiency and free up attorneys to do more substantive work is an important way modern law firms win business.
“Our expertise with managing data allows us to architect and deliver technological solutions for clients managing large data volumes for many business needs, including information governance and knowledge management, contract lifecycle management, incident response, corporate transactions, and litigation readiness,” Alison noted.
eMerge’s unique purpose centers around leveraging their tech investments to help clients extract as much value as possible from their data, which Alison says they do “by spending the time to understand not just the needs of a specific matter, but how each client’s systems, business, industry, and data management practices impact their legal risk and profile. And by finding ways to reuse work product and information learned through the analysis of client data across matters, to reduce duplicative effort, identify proactive ways to reduce future risk, or identify other opportunities.”
When Troutman Pepper formed eMerge to manage the firm’s technology investments and strategies, Alison said, “people asked if our offering would reduce work for other attorneys.”
Twelve years later, that has not been the case.
“Other attorneys remain very busy, focusing on key documents and facts, thinking strategically about using documents and information exchanged, reducing the time they spend reviewing irrelevant information, and focusing on deal terms rather than re-drafting standard documentation,” she said. “I am pleased to see the increased excitement among our attorneys for using AI and other technology solutions to improve the quality of our work and differentiate ourselves in a competitive market. I am also grateful to work with clients who share that excitement and appreciate our investment and use of technology to serve their needs.”
On the services side, sentiments around the promise of big data stores, the increasing demands of clients, and the necessity of investing in emerging tech are remarkably similar. And, in Tara’s experience, beginning with the end goal—and demonstrating success against agreed-upon metrics—is key.
“Because the value of data depends on a client’s project goals, the first step is to ensure I understand those goals: cost savings, building long-term processes, et cetera. Once I understand the specific goals, it’s usually possible to determine metrics to measure success,” Tara explained. “For most clients, value and success are not real if they can’t see them quantified.”
In any setting, no meaningful project can begin without a grasp of how its success will be measured.
For modern legal data reviews, this can mean tracking those familiar metrics—doc-to-doc speeds, culling rates, precision, recall—as well as potentially fuzzier impressions of the insights, opportunities, and organizational efficiencies gained by taking a closer look at data. Because the paradigm of ROI in e-discovery has evolved: for those at the forefront of legal technology, practice, and data intelligence, it’s becoming more proactive, and more strategic, than ever.
“I routinely see people miss opportunities for more effective document review by merely relying on search terms and not investing the time to understand the full context of the legal issues and the full suite of technology options,” Alison agreed. “Professionals who can see the bigger picture and craft a solution to find what is required and needed will be best positioned to continue to succeed in document review in the future.”
Read: the real pros find ever more ways to capitalize on their investments.
“Beyond standard e-discovery processes in litigation, government investigations, and internal investigations, similar applications in my work extend to information governance, compliance, and divestiture data transfers,” Tara observed. “I’ve used essentially the same processes of search and retrieval, and review, in projects involving defensible disposition of data eligible for deletion, auditing of compliance policies, and identifying data for migration and transfer.”
“Our customers have been really excited to use aiR for Review. They see the possibilities for more efficiency and better outcomes,” Cristin Traylor told us. “Attorneys are zoned in on how it can help them better understand each matter and give them the ability to better advise their clients. But beyond that, they all look at aiR for Review and see endless use cases to help solve large data problems for their clients even outside of e-discovery.”
Adapting existing technologies and workflows to accommodate broader use cases helps firms and service providers explore entirely new practice areas, opening new lines of business and the ability to serve existing clients more holistically.
There are few better returns on investment than that.
All told, there is quite a lot to be gained from braving that Fangorn of data. Legal practitioners set themselves up for the successful navigation of cases and client books by investing in state-of-the-art equipment.
Getting a good grasp of innovative tools can also just be thrilling.
In the e-discovery profession, chock full of self-described “geeks” and “nerds,” bright technologists make careers for themselves through curiosity—building shiny new software and advising legal teams on how to use it.
Chris Haley is a great example.
“I am curious, enjoy learning, and enjoy a good challenge. I also really enjoy helping people. So as my career progressed, there was this natural evolution of growth in who I could help,” he recalled for me. “I moved from traditional litigation support in a law firm where I helped the attorneys, to eMerge where we could impact not just attorneys but also our clients—and not just in litigation, but in all areas where legal and technology intersect. Now at Relativity, I hope to help our entire industry as we adopt generative AI and leverage technology solutions for all areas of legal data intelligence.”
But similar opportunities to grow aren’t limited to those with deep technical knowledge—particularly in this era of generative AI.
According to 2024 AI Visionary and Bayer e-Discovery Expert Michallynn Demiter, the legal profession relies upon precisely the sort of soft skills training that practitioners need to succeed and thrive in a generative AI world: critical thinking, an innovative mindset, openness to collaboration, and a commitment to ongoing learning.
Richard Finkelman—managing director at BRG and another 2024 AI Visionary—also suggests that legal professionals’ skills could make for exceptional prompt engineering, in addition to helping them weave AI into their everyday work quite naturally, if they have the interest.
The aforementioned ability to ask the right questions, the natural inclination toward curiosity, their experience strategizing and storytelling: all of these are readily transferrable skills for attorneys looking to capitalize on an artificial-intelligence-powered renaissance in the legal profession.
Practicing lawyers sometimes miss this forest for the trees, tackling one data challenge at a time without the luxury of contemplating how far they’ve come.
But for burgeoning lawyers and those with fresh eyes on the abundance of available paths forward, the possibilities for personal and professional growth are bound up in technology.
“I have, thankfully, had the opportunity to practice law in a civil law system before starting my J.D. in the United States, so it’s a blessing and a curse that I’ve had to conduct due diligence and document review the old-fashioned way—surfing through reams of paper where documents would inevitably get overlooked or, simply, lost. Coming to the United States and not only learning about e-discovery but taking a class from Professor William Hamilton, one of the best to do it, has thus far been a true blessing,” Nader Abou Mrad, a rising 3L at the University of Florida’s Levin College of Law, recently told me when interviewed for this story. “Beyond evolving e-discovery’s substantial impact on lawyers’ quality of life and work, it exerts palpable effects on the civil justice system by making courts more accessible to smaller and smaller organizations that can increasingly afford document review. It can also further the rule of law through facilitating faster, more transparent, and better-informed judicial decision making.”
Nader explained that, while advancing technologies have the potential of increasing the divide between well-resourced organizations and those with less to invest in their tech stacks, the promise of technology like generative AI and aiR for Review outweighs the risks.
“The most exciting thing about this time to be a lawyer for me, as a first-generation professional, is the way technology decreases the barriers to entry into the legal market. Nowadays, a tech-savvy lawyer can accomplish, on her own, what she otherwise would’ve needed a prohibitively expensive team to do,”
Nader continued.
Jordan Karp, another rising 3L at UF’s Levin College of Law, agreed with Nader.
“I feel like the practice of law is rapidly changing. We are all learning how to use and teach these tools at the same time, which may promote more collaboration,” he said in our interview.
Referring to a 2017 case he and his classmates studied in their e-discovery course, Jordan noted the importance of attorneys “not just understanding the process [of e-discovery and its technological applications] but also being able to communicate it to the client.”
Technical aptitude—and the ability to rely on technologists’ guidance where one’s own technical skills or understanding fall short—will be essential for lawyers who hope to succeed in a futuristic practice of law.
Jordan is ready, and said he hopes “to learn from industry veterans how far this technology can go before it crosses the line between augmentation and substitution. We have already seen and have been amazed at what it can do now. With an eye toward the future, I want to know what other tasks it could perform.”
The drive to learn from shared experience—and use those lessons as fuel to move forward—is strong for both students.
“I hope to meet professionals who have litigated before and after e-discovery became cool to learn about how the legal landscape developed: market trends, clients’ attitudes, lawyers’ approval, and adoption of legal tech, et cetera,” Nader said. “With that information, I hope to prepare myself for an agile career and to navigate the incoming waves of transformative technology, such as generative AI.”
With that thought, we’re back to the necessities we laid out at the start of our journey: a bit of sun, a good team, some creative thinking, the right tools.
Technology flies right alongside time. Businesses evolve. And data outputs grow impossibly fast. The result is that, for legal teams charged with uncovering stories from all that data and advising on critical decision-making accordingly, there is much to explore—and the savviest attorneys donned their explorers’ gear ages ago.
The next generation is rapidly being equipped with the same.
Together we’re off, into the forest. Deep into the data. If we keep our feet, perhaps we, like the Ents, those insightful trees, will discover much along the way there. And back again.
Recommended for you
