First, Microsoft Word does not actually have a feature called Paragraph Glue. That’s just an umbrella term I use to describe two (sticky) features. The first (Keep With Next) will hold one or more paragraphs together on a page; and the second (Keep Lines Together) will hold the lines of a particular paragraph together on a page. To the extent you’re wondering why you haven’t seen the buttons for these features, well that’s because there aren’t any buttons for them. Welcome to Microsoft Word! Important functions a legal user would likely need in just about any document are often concealed. However, that doesn’t mean they are inaccessible. The following is a description of these features, when they’re appropriate to use, and how to utilize them. Keep With Next: Let’s begin with an explanation of the problem this feature resolves. In the screenshot below, you see a very common issue in legal documents which I’d refer to as an awkward page break. I would obviously want paragraph 2.3 to move to the top of page 3 and not be marooned at the bottom of the page 2. Unfortunately, most users try to resolve this issue by adding hard returns (Enter key) or page breaks above the title. To be clear, that technique is only a temporary fix and you should never use that technique again if that’s your typical approach. The problem with those faux fixes is that if the document undergoes further editing and more text is added or deleted above it, you’re likely going to have to remove those hard returns or page breaks later or you’ll end up with gaps/blank pages in your document. You may be thinking, “well I only do that when the document is finalized - so I don’t have to worry about further editing.” Respectfully, that’s still not a valid defense because almost all new documents in law offices are created from existing documents (as templates). Thus, it’s likely that the document you finalized today may be used as a template at some future date, in which case it will undergo further editing. When that happens, the latent defect you created in the original document is likely to show itself.  A superior way to deal with this is to simply glue paragraph 2.3 in the foregoing example to any subsequent paragraph so you don’t have to worry about it again and you don’t have to add any hard returns or page breaks. To utilize this feature, you should not select paragraphs 2.3 and (a) although that would seem a logical first step. Instead, you want to focus solely on the paragraph/title/heading you want to glue to a subsequent paragraph. Simply right-click the paragraph/title (Holdover in this case)  choose Paragraph from the menu that appears  click the Line and Page Breaks tab at the top of the subsequent dialog  check Keep with next  click OK.  You don’t want to select paragraphs 2.3 and sub (a) before right-clicking is because that will glue paragraph 2.3 to (a), and subparagraph (a) to subparagraph (b). If you glue too much text together, I guarantee you’ll start seeing bizarre page breaks in your document. So a little glue is good, but you don’t want it on everything or you’ll have a whole new formatting issue to resolve (random gaps and inexplicable page breaks). As such, you need to be surgical in your application of this feature, and be careful not to apply it where you don’t actually need it.  Keep Lines Together: This is a slightly different type of glue compared to Keep With Next. In this case, you’re not glueing one paragraph to another, you just want to hold the lines of a particular paragraph together. For example, you would not want a page break to occur in the middle of the highlighted paragraph below.  If you work with footnotes, you may have also seen Word decide to split a footnoteacross multiple pages(and I’ve still never met anyone who wanted one footnote split across multiple pages). This feature solves(and I’ve still never met anyone whowanted one footnote split across multiple pages)that problem as well. If you want to hold the lines of a paragraph together on a page, simply right-click the paragraph/title (holdover in this case), choose Paragraph from the menu that appears, click the Line and Page Breaks tab at the top of the subsequent dialog - check keep page with next - then click OK. Using Both Features Together : "Using Both Features Together: It is often appropriate to use both types of glue together on a block of text. For example, in the foregoing acknowledgment, I want the state and county to stick to the “Be it remembered…” paragraph, I want the lines of the paragraph in the middle stuck together on a page "and I want the paragraph to also adhere to the signature blank for the Notary. As such, I would select/block the entire acknowledgement - right click - paragraph - check both Keep with Next and Keep lines together - click OK. After doing that, the entire acknowledgement will move as a unit and stay together on a page. Make These Features Easier to Access : Interestingly, you  can  add buttons for thesefeatures to the Quick Access Toolbar or even the main ribbon. How to do that is beyond the scope of this article, but if you already know how to add custom buttons to Word, be advised that although the features are called Keep with next and Keep lines together , the buttons for them are called Para keep with next and Para keep lines together , respectively. If you think it’s ridiculous that Microsoft would alphabetize features that begin with “k” under the letter “p,” well, join the club. Barron K. Henley , a founding partner of Affinity Consulting Group, is a legal technology expert with a diverse background. After practicing law from 1993 to 1997, he transitioned to providing technology services for lawyers. At Affinity, Henley leads the document assembly/automation and software training departments, specializing in automating complex documents using platforms like HotDocs and Contract Express. He conducts technology audits, assists in launching new law firms, and delivers engaging continuing legal education seminars across the US and Canada on technology, practice management, and ethics. Beyond his professional endeavors, Henley is deeply involved in the legal community. He's a Fellow of the College of Law Practice Management and the American Bar Foundation, a member of the Ohio Supreme Court Commission on Technology and the Courts, and Co-Chair of the ABA Joint Law Practice Management Group. Personally, Henley is an enthusiastic cook, preparing all meals for his family at home. He enjoys outdoor activities like hiking and biking with his wife and dog, and has a passion for smart home technology. Despite describing himself as "always in a hurry," he proudly maintains a clean driving record of 37 years.

Introduction In the rapidly evolving landscape of LegalOps, the relevance of integrating advanced methodologies to drive efficiency and innovation has never been more apparent. LegalOps is a much-needed new paradigm that is finding its voice. LegalOps teams, with their unique blend of change management expertise, business analysis, process improvement, legal engineering, legal technology, and now, innovation specialists, particularly with the urgent integration of AI, are defining what it means to deliver legal services effectively. As such, LegalOps roles between organisations differ widely because of the need to straddle these disciplines (I am yet to find a job description which doesn’t require a blend of skills) and great work is being carried out globally to stratify and define roles and responsibilities. We want the same thing One thing that has been evident from the outset and from my experience working in Legal Operations for several years is: the desire to make an impact .  This desire is shared by the profession, which is why I (much like Colin) know I belong here. The impact we seek in LegalOps is sometimes crudely framed as a drive for increased profitability-or as many LegalOps teams are regarded as a cost-center-greater efficiency.  Through an altruistic lens, impact can mean greater access-to-justice.  Holistically it extends to the well-being of the lawyers we serve and by extension providing greater outcomes for our clients. The impact I  seek is rooted in a moral obligation to do better : we can, therefore we should. In my own search for strategies to create impact, my curiosity met with serendipity and I was introduced to the methodologies and frameworks of Implementation Science .  I hope to show in this article what can be learned from research rooted in public health which is increasingly (and empirically) helping professionals and the communities they serve deliver and assess impact . What is Implementation Science? Implementation Science arose as a response to the gap between medical and health-related innovations and their practical application, engaging professionals and researchers from healthcare, public health, and social services. Implementation Science is used to improve the application of evidence-based practices and innovations with the goal to enhance outcomes (make an impact) by ensuring that effective interventions are not only developed but also correctly implemented and sustained  in real-world settings. The discipline has grown on its own merits to show value in other fields such as business, education, change management, process optimization, and stakeholder engagement. Enhancing LegalOps I’m conscious that my journey into LegalOps may reveal my naivety, as experienced change management professionals will readily find common ground here.  However, the LegalOps industry will only grow at the necessary speed required by encouraging and welcoming people from a wide range of backgrounds – not just lawyers and paralegals, but professionals from other industries like software engineers, marketers and data scientists. As a lawyer (I’m still a lawyer even if regrettably referred to as a ‘non-lawyer’ if working in business services by some firms), I naturally gravitate towards the precision of language and syntax as tools of delivery. I have discovered that by looking at the language  of Implementation Science we can draw parallels and borrow ideas to augment our approach to delivering impact.  And, as a lawyer, there is a strong caveat  to my opinion in that this does not mean we need to start again—consider this progressive enhancement . There are a number of established methodologies and frameworks which provide strategies for delivery which have strong correlation and sometimes direct overlap with change management in a business context.  In the sections that follow, I will delve deeper into Implementation Science, illustrating how its principles can be directly applied to the challenges and opportunities within LegalOps today.  Key Principles of Implementation Science The field of implementation science systematically aims to bridge the 'know-do gap'—the disconnect between existing knowledge and practical application—by identifying and overcoming the barriers that impede the adoption of proven health interventions and evidence-based practices.  Similarly, in Legal Operations, there is a distinct need to close the gap that exists between both existing and emerging legal technologies and their practical implementation within legal departments. The key principles of Implementation Science are: (a) the use of evidence-based practices; (b) understanding and adapting to context; (c) engaging stakeholders throughout the process; (d) developing and supporting capacity for change; (e) employing iterative cycles of implementation; (f) evaluating progress continuously; (g) ensuring sustainability and (f) dissemination of knowledge. These pillars collectively guide the successful integration of knowledge into practical, real-world  applications to ensure effective and sustainable outcomes.  After all, long term impact is critical to justify the very real investment law firms are making to our profession. Interventions vs Initiatives A quick note on "interventions". This term is used frequently (but not exclusively) in Implementation Science owing to its place in healthcare settings. The analogous term in Legal Operations, in my opinion, is "initiatives," which encompasses a variety of strategic efforts and programs aimed at improving legal processes, integrating new technologies, and enhancing overall efficiency and effectiveness within legal departments. These initiatives are structured and targeted actions (just like interventions) designed to address the challenges and opportunities in law. Now—back to the pillars. a)    Evidence-Based Practice Implementation Science prioritizes the use of interventions that are supported by strong empirical research and evidence. This principle ensures that the strategies and practices implemented are not only theoretically sound but have also been proven effective in practical applications. In Legal Operations, this means understanding ‘what we know works’.  So how do we determine what actually  drives value in legal services delivery when we are in the midst of exploring what’s possible with new technologies and AI and adopting mature best-practices from other professions such as Finance, Marketing and Accounting? In the absence of scientific research, here are some places to look: -        Internal case studies from other departments : draw upon success and best practices in other teams and draw parallels to form your sector strategy. -       External success from other firms in your peer network : Benchmark against other organizations within your industry to identify effective practices and technologies that have contributed to their success. (This does require transparency and openness which I will address later). -  Industry insights : Regularly review literature from thought leaders and industry-specific research that discuss trends, case studies, and best practices. -  Feedback from stakeholders:  Gather insights directly from clients, staff (particularly at the coal-face), and other key stakeholders to understand their needs and the effectiveness of current practices. -  Professional networks and conferences : Engage with professionals at industry conferences or through professional networks to learn about innovative practices and real-world results. We are fortunate to have a growing number of dedicated events where we can connect and learn – adjust your budgets accordingly!   b)      Contextual Adaptation In Implementation Science, the principle of contextual adaptation is central to the successful integration of change efforts. This principle acknowledges that each intervention must be customized to align with the specific cultural, organizational, and environmental contexts of the setting where it is being applied.  Implementation Science differentiates between two contexts: inner and outer. -         Inner context  refers to factors internal to the organization, such as the prevailing culture, governance structures, available resources, and the existing skill sets. These elements can significantly influence how an intervention is received, implemented, and sustained. For example, an organization with a strong culture of innovation and teamwork may more readily adopt new health interventions compared to one with a rigid, hierarchical structure. The inner context in LegalOps might include the department’s strategic alignment with the broader corporate goals (mergers and consolidation in the market impede this), the technology infrastructure in place (particularly across offices), and the staff's willingness to embrace new processes or technologies. For example, a corporate or litigation department that is well-versed in using advanced analytics and / or AI or contract analysis or e-Discovery will be more adept at integrating and capitalizing on new legal tech solutions. -         Outer context  involves external factors that impact the organization, including regulatory requirements, technological advancements, economic conditions, and broader social and cultural trends. These elements can either facilitate or hinder the adoption and success of interventions. For instance, changes in healthcare policy or funding can drastically affect the implementation of new medical practices or treatments.  This is never more apparent than with changes in political leadership at the very top of government.    The outer context for LegalOps could include changes in legal regulations (I’m looking at you GDPR), evolving industry standards and expectations (data-driven decision making, remote working and paperless offices) and general market conditions that influence legal practices. Stakeholder Engagement Implementation Science stresses the importance of involving all relevant stakeholders in the planning, execution, and evaluation of interventions. This includes practitioners, clients, policymakers, and community members. One of the primary barriers in LegalOps is resistance to change .  For example, despite widespread agreement on the merits of fixed-fees, adoption is slow and uneven both between firms and across departments.  Lawyers are unfairly tarred with being rigid and considered to be skeptical of new technology.  I don’t believe that is the case anymore, likely there are barriers to innovation in the inner context rather than in lawyers intrinsically. The main barriers to impact are two-fold: finding the time to innovate and knowing what to do. In LegalOps, facilitating stakeholder engagement is pivotal for the successful implementation of new initiatives and technologies. In order to do this, we should actively plan and engage with the following concepts: 1.      A Clear, Well-Defined Discovery Process:  The initial step in LegalOps often involves a discovery process to identify the specific needs and pain points of all stakeholders. Just like in Implementation Science, it sets a foundation for accurate problem definition, assessing readiness to change, identification of high impact  initiatives and anticipating potential barriers—paving the way for smoother implementation and adoption.   2.      Feedback Loop : Continuous feedback from stakeholders provides insights into how well the initiatives are working and what adjustments may be necessary. You should expect to course-correct and fine tune often. Share that expectation early.   3.      Win   Hearts and Minds : Adoption of new practices requires more than just acknowledgment from stakeholders­—it requires their enthusiastic support and active participation. LegalOps must work to win over the stakeholders by clearly communicating the benefits and potential early impact of initiatives. We can humanize our successes and attach real people to real progress -- leveraging all measures of impact to secure buy-in. 4.      Develop a Culture of Innovation : We could debate whether culture beats strategy. Both are important.  Strategy sets the roadmap for innovation whereas culture  is the willingness to go on the journey.  Cultivating an environment that encourages experimentation (e.g. safe places to explore, particularly with AI) and supports new ideas is vital. This comes from the top (that’s where the buy-in becomes important) where leaders empower staff to be creative, actively valuing or incentivizing experimentation, knowing that their efforts here are just as valuable as their billable hours. c)       Capacity Building For Implementation Science, building the capabilities of individuals and organizations to implement and sustain new practices effectively involves training, providing resources, and creating supportive infrastructures. For LegalOps to thrive, it's essential to provide access to training that extends the skills of each team member according to their unique career backgrounds and current roles. A tailored approach to skill development is particularly important in a field as interdisciplinary as Legal Operations, where team members come from diverse professional backgrounds. For example, lawyers transitioning into LegalOps roles may require additional training in technical skills such as data analytics, procurement or software development to effectively manage and leverage legal technologies. Conversely, developers or engineers who move into LegalOps might need to enhance their understanding of legal processes, compliance requirements, or develop business analyst skills to better align technological solutions with legal needs. A tip for smaller teams: where specialization is less feasible due to lower headcount, each member should have a broad set of skills that allow for flexibility and adaptability. For larger teams, specialized roles can be filled by individuals with deep expertise in a specific area, whereas smaller teams rely on each member's ability to perform multiple functions. Engaging with agencies can bridge the knowledge gap,capacity limits and the need for ongoing technical assistance.   Extending culture to capacity building looks like encouragement from-the-top for dedicated time for lawyers. This commitment should be reflected in strategy (for lawyers and LegalOps team members) through access to learning resources, mentorship programs, and opportunities for practical application of new skills. d)      Iterative Process Implementation science promotes the use of iterative and cyclical processes that include continuous testing, feedback, and refinement of strategies. This adaptive approach allows for adjustments to be made in response to what is learned during the implementation process itself . In Legal Operations, employing iterative, cyclical processes might look like: starting from a software trial or minimal viable product (MVP) and evolving through cycles of continuous testing, feedback, and refinement until full release or roll-out. Managing stakeholder expectations through these stages can be challenging depending on whether you are putting out a fire of unprofitability or building a boat to sail to new markets. Unlike Implementation Science, where there is a proven methodology backed by research that’s gone through a rigorous selection criteria, LegalOps is, to some extent, building-the-plane-on-the-runway. Sunk-cost fallacy has an opportunity cost in LegalOps that can do irreparable damage.  The ability to pivot, even after substantial investment, is the sign of a healthy LegalOps team. e)       Integrated Evaluation Closely linked to iterative processes, continuous evaluation  in Implementation Science involves regular monitoring of outcomes and processes to assess their effectiveness. Continuous evaluation identifies areas for improvement and refinement to the ongoing implementation process. A key component of this approach in LegalOps is the identification and implementation of Key Performance Indicators (KPIs). These KPIs should be thoughtfully selected to what matters: we must look deeper than direct revenue as firms should account for employee turnover, resistance to market cycles and maintaining a competitive advantage. That being said, quantitative metrics from tracking the turnaround time for legal documents to assessing the accuracy and impact of legal advice are good places to start. How do we monitor these KPIs?  -       LegalOps teams need to establish robust mechanisms for collecting both qualitative and quantitative data. This might involve integrating legal technology tools that can automate data capture and streamline data analysis processes.  Choose your vendors wisely as limited access to the data within a platform can restrict meaningful evaluation.  -   Engaging with data scientists or specialists in legal analytics is another critical step. Being able to sift through the data to elicit valuable insights is the best way to understand the factors influencing performance and uncover potential areas for improvement.   f)       Sustainability There is a focus in Implementation Science for the long-term sustainability of the intervention. This involves appraisal of factors like funding, resource allocation, and integration into existing systems to ensure that the practices can be maintained over time after the initial implementation phase. In Legal Operations, this means that from the very beginning, there should be a focus on ensuring that any new systems, processes, or technologies are not only implemented successfully but are also viable and effective in the long term. This is really hard. The ability to experiment, pivot and ditch remains paramount. The key to sustainability in LegalOps is, then, considering factors such as funding (get your budgets confirmed for tech spend), resource allocation (hire accordingly and allow time for lawyers to engage), and integration into existing systems (know your API capabilities). g)     Dissemination of Knowledge The goal of dissemination is to spread knowledge and the associated evidence-based interventions to those who can usefully apply them in practice. Through publishing research in academic journals, presenting findings at conferences and contributing to policy briefs, researchers makes waves as ideas are spread and adopted. Something wonderful is happening in Legal Operations. There is a collaborative approach which fosters a global community where we move forward together, benefiting the entire legal sector rather than just individual firms. It counters the zero-sum game law firms have traditionally played which may make it uncomfortable for those who relied on gatekeeping or opaqueness for their competitive advantage. On LinkedIn you might learn how one firm successfully integrated a new document automation tool.  Or at a conference you might have a workshop on the technical aspects of an AI comparison tool. This openness does raise the question: what is our USP as a law firm?  I have ideas around that (it involves empathy) for another article.  However, being part of the-change-we-seek as a body means that we are driving with both hands on the wheel rather than being spoon-fed by oligopolies towards techno-feudalism.   Conclusions I hope by delving deeper into the specific language and pillars of Implementation Science, you have (like me) gained insights that augment change management approaches and give you a map you can translate towards impact. Where do you go from here? There are four key take-aways you can apply today to your initiatives. 1.      Adopt an Evidence-Based Approach:  LegalOps, regrettably, does not yet have an army of researchers (although universities and institutions are closing that gap) therefore we must create and share our own evidence.  Be methodical, write your hypothesis, analyze the data and make decisions not on a whim but with data or performance metrics. 2.      Emphasize Contextual Adaptation:  Tailor strategies and technologies to the unique cultural, organizational, and technological environment shaping  each legal department’s innerworkings. 3.      Enhance Stakeholder Engagement : Actively involve all relevant stakeholders—from senior management to frontline staff—in the planning, execution, and evaluation of new initiatives. Make sure your working groups are not only partner-led or driven by only a few senior associates. Use strategies from Implementation Science such as workshops, feedback sessions, and inclusive decision-making processes to build consensus and encourage buy-in throughout the organization. 4.      Track your numbers: Implement continuous evaluation mechanisms to track the effectiveness of new initiatives. Establish clear metrics with KPIs and qualitative feedback that align with the desired outcomes of each project.  Build your reporting dashboards and give your stakeholders a voice. Finally, raise your own voice by participating in and engaging with the communities shaping Legal Operations: ●     Legal Ops Career Path : a UK based initiative to create a defined career path for legal operations professionals in order to create a market standard role structure and pay bands. ● CLOC : The Corporate Legal Operations Consortium (CLOC) is a global community of experts focused on redefining the business of law. ●  IILOP : International Institute of Legal Operations Professionals (IILOP) is a new platform which provides training, certification, and continuing professional development for legal operations professionals. ●    Legal Ops Uncensored : a global community with a forum and in-person events whose mission is to create a safe space where our members can build community throughout all phases of their career in legal operations and legal technology. Jay Smith is a qualified lawyer, legal engineer, developer and founder. He has worked with both fast-growth and global law firms around legal technology and legal operations, holding Head of Legal Operations and Senior Legal Operations manager roles. A technologist at heart, Jay uses his knowledge of software engineering, product management alongside his legal expertise to build, design and implement systems to improve legal service delivery.

Daniel B. Rodriguez is the Harold Washington Professor at Northwestern University Pritzker School of Law and served as dean of the Law School from January 2012 through August 2018. Professor Rodriguez has taught full-time at several law schools including the University of Texas-Austin, the University of San Diego (where he also served as dean), and at the University of California, Berkeley. He has also been a visiting professor at Harvard, Stanford, Columbia, USC, and Virginia.   His scholarship and teaching spans a wide range of topics in public law, including administrative law, local government law, constitutional law, and property.  He is also deeply interested in the law-business-technology interface.   A graduate of California State University Long Beach and the Harvard Law School, Professor Rodriguez has served as the Chair of the ABA Center for Innovation, a member of the ABA Commission on the Future of Legal Services, as the President of the Association of American Law Schools, and chair of the AALS Deans’ Steering Committee. He is presently a council member of the American Law Institute and also a member of various task forces working on access to justice issues.   How do you envision the role of technology evolving in legal education, and what steps should law schools take to prepare future lawyers for this changing landscape?   I think there is a “present” and a “future” element here that I would separate to make the broader point that we will need to do a better job as legal educators and as law school leaders to ensure that our students will have all the tools and to exposed to the many perspectives essential to understanding and using technology to practice law at the highest level and further justice.    On the “present,” we are witnessing  a tremendously active and energetic burst of tech-related education in law schools, the likes of which I do believe are unique in the 30+ years I have been in law teaching.  For example, there are courses on technology-enabled research – most of which move beyond the use and utility of the traditional tools such as Lexis & Westlaw and consider how Google has impacted research, and how large databases have given lawyers information to analyze cases, regulations, and statutes and, significantly, augmented our ability to predict legal outcomes.  To take just one example from many, the ODR (online dispute resolution) movement has created the need for understanding how these technologies work to aid and even undertake decision-making as an alternative to traditional adjudicatory models.  There are many other examples.   On the cutting edge of this is generative AI, including the fast-moving emergence of Chat GPT and other kinds of technology that utilize LLMs (large language models) to not only aid research and analysis, but to contribute direct information and even modalities of advocacy.  Many schools – and within a couple of years I would predict most law schools – provide education in generative AI.  Some do this through stand-alone courses on AI & Machine learning and its applicability to law & legal practice.  Other law schools are embedding this education in traditional law school courses.  Ultimately, law schools are wisely figuring out ways of ensuring that students are getting contemporary, relevant, and even essential instruction in what they will need to be successful lawyers and leaders in this third decade of our 21st century.  And so, in summary, I am enthusiastic and rather bullish about what law schools are doing to improve tech-related legal education.  At the same time, we need to continue to press the envelope, to activate and incentivize legal educators and other decisionmakers to ensure that these strategies become, if not already, a priority in our curriculum.  It is not extra; it is essential.   How do you see recent technological advancements influencing the study and practice of public law?   As to the study, we can see how technology can enrich our understanding of how political institutions shape and implement public policy.  A few examples: To study the practice of statutory interpretation, legal technology, including LLMs and other methods, can assist us in searching for how particular phrases and terms have been enacted into law and have been interpreted by courts and agencies. Lexis/Westlaw, for all its virtues, was less useful for this purpose, but new methods have really enhanced all this.  Another example:  Now that the Supreme Court and many lower courts have put a stake in the ground in favor of so-called originalist methods of interpretation, the role and salience of historical research has moved to the fore.  Technology is being used to assist in dense historical research, not only in searching for comments by legislators and other political officials, but for filling out the context in which critical debates happened and decisions were made.  This doesn’t replace the human touch in doing careful historical research, but it is undoubtedly an aid for historians, legal scholars, and teachers.   As to the practice, we are seeing federal and stage government agencies relying on insights and tools from technological developments to improve public policy.  For example, governments are frequently developing and employing algorithms to make various decisions more efficient and error-proof, in variegated areas such as sentencing and government benefits administration.  Algorithmic decision-making is not without its challenges to be sure, but the genie having come out of the bottle, we will surely see continuing use of new tools along these lines to improve policymaking and implementation – in ways not unlike its balanced use in health care.  Another example is the use of tech-enabled decision-making – sometimes called, if hyperbolically, “robot judges.”  When used to augment human decision-making, more automated modes of adjudication is tremendously valuable in administering justice in a way more fair and efficient for both the government and for ordinary citizens. Indeed, it is critical to further develop these tools in areas of huge backlogs and what has been called “high volume/low value adjudication.”  This development, what the legal futurist Richard Susskind has labelled “online judging,” is enormously interesting and important. What technological innovations do you believe hold the most promise for transforming the delivery of legal services, and what are the potential challenges in adopting these technologies?   If I had to pick a couple, I would say, echoing some of my earlier comments, I would first say that the use of technology that helps assist ordinary citizens who cannot afford lawyers seek and obtain justice is the single most important domain and a promising one.  Entrepreneurs in this space have developed myriad tools, including chat-bots, legal check-ups, court navigators, document retrieval and guidance mechanisms, and others, that share in common the aspiration of reducing the barriers to justice access, which has historically been a mix of complexity and cost.  Frankly, what stands in the way of an even greater use of such technologies is not the tech and not even the financial funding, but regulatory barriers that interfere with the deployment of such technologies on the grounds, which I believe are more often dubious than persuasive, that tech-enabled assistance constitutes the “unauthorized practice of law.”  In short, we will need regulatory reform to remove the friction that keeps ordinary citizens from technologies that will help close the serious access to justice problem in the U.S.   The other innovation would be generative AI, as I mentioned previously.  We are early in the history of this tool and we can perhaps scarcely imagine the many ways in which ever-more-sophisticated generative AI tools can transform the delivery of legal services.  Using a tool such as Chat GPT to help craft compelling legal arguments and to answer questions that require rapid access to information can, when used responsibly, enhance the efficiency of lawyers, reduce barriers to access to justice, and, we hope, improve the quality of legal decisions.  After all, let’s remember that the essential meaning of artificial intelligence is the use of tech tools (think especially here of tools that are computational in the broad sense of that term) to do things more quickly and effectively than human decisionmakers, the latter of whom are ultimately limited in fundamental ways.  To the extent that we can employ generative AI to improve human decision-making, then progress is quite promising.   The challenges are many.  First, we need to understand well the contents of these technologies, and the brutal truth is that a fairly small sliver of the population, and generally not including lawyers, really understand in any depth and detail how these technologies “work.”  There is a black box quality to many law-relevant technologies, and it will be important for lawyers and others in the law space (including judges!) to get their heads around these technologies, at at least the level necessary to assess their utility, diagnose the challenges, and improve them.  Second, there are some familiar ethical issues, which I will discuss more in my answer to the next question.  Finally, there can be inequalities in how technology is made available and deployed.  Right now, ChatGPT is basically given away for free, but that won’t be true forever.  There are already gates being created around certain technologies – maybe call this the “landlording of technology” – that can create a system of haves and have nots.  If we truly want to use technology to democratize legal decision-making and improve access to justice, we will need to figure out how best to deal with economic considerations and various barriers to entry.  I honestly don’t have any great wise answers to these challenges, but I just mention them as examples of challenges we will need to earnestly tackle. What are the key ethical considerations that must be addressed as technology becomes more integrated into legal practice, especially regarding client data privacy and the impartiality of AI in legal decision-making?   I cannot speak with any special knowledge about client data privacy, so I will leave that to the experts, so let me focus on the impartiality part of your question.  First, it is important, as I always say to my students and to colleagues, to look at impartiality through a “compared to what” lens.  Yes, there are issues of algorithmic bias that stem from various imperfections in how data is scraped and formed into algorithms.  But whatever level and severity is this kind of bias, it must be compared to the biases that humans have, including in legal decision-making.  Humans have various prejudices; they also can be subject to noxious external influences; they have various cognitive limitations; and they have other human frailties (e.g., they are busy, they get tired, they get sick, etc.).  We know that these issues interfere with “objective” decision-making.  This is partly why there is pressure to develop more automated mechanisms of decision-making.  While this is no excuse for letting algorithmic bias to persist, it is important not to let the best be the enemy of the good.  We should work hard to reduce and even eliminate algorithmic bias and other impediments to the ideal of impartiality in legal decision-making.  But we should acknowledge that imperfect automation may in some instances be an improvement on deeply flawed legal judgment. How can legal institutions and professional bodies better support and foster innovation at the intersection of law and technology?   By not imposing regulatory barriers and burdens that make it harder, and sometimes impossible, to use technology to improve justice and to foster better legal decision-making.  This is a message for regulatory entities, such as state bars; it is also a message for a national organization such as the American Bar Association, an organization that has done obviously valuable work in improving the welfare of lawyers and in advancing the cause of the rule of law but, where innovation and technology is concerned, has often lagged behind.  The ABA could be part of a collective solution; it should, at the very least, be not an obstacle to progress.   So far as other institutions in the law space are concerned (AALS, ALI, other groups), they can continue to use their bully pulpit to push for innovations that will improve access to justice and enhance legal education to ensure that the next generation of lawyers and allied legal professionals will be able to do great work in the legal space.  As to law reform groups, such as the ALI and the Uniform Laws Commission, they should assist us by their measured work in improving the legal regime so as to foster excellent use of technology – “excellence” here being used in the broadest sense of the term.   Lastly, these groups can also assist by proposing and enacting the guardrails that will ensure that some of the more troubling (real or potential) elements of technology can be ameliorated.  This is important, too, to ensure that we are using technology in the most responsible ways possible.

As Generative AI has marched through law practice since late 2022, law schools and legal educators around the world have scrambled to adapt: what new skills do law graduates need to thrive today and looking ahead? And overall, they’ve gotten the answer wrong. Online, in classrooms, and in faculty meetings, law professors have resolved to equip their students with basic facility with AI systems and related technology for gathering, analyzing, and deploying data. That’s easier said than done in practice; in practice, the transition is akin to retrofitting a semi-trailer truck with the nimbleness and speed of a Formula 1 racecar. But that’s the path that the academic profession seems to be set on: teaching lawyers to work the virtual steering wheels, throttles, and gearing of massively complex, fast moving, and opaque IT systems. Employers – a category that includes large corporate clients as well as private law firms – seem to be egging all of this on. To law schools, the message is: give us more well-behaved junior technocrats. As those systems get better, faster, and much more useful than they are today, new lawyers trained to be good technocrats may find themselves not simply out of jobs but, more important, out of careers. The 20th century lawyers’ career path offered hands-on practical training to junior lawyers that led eventually, if not always directly, to status as a senior elder whose judgment and wisdom were the coin of the field. The 21st century lawyer who starts a career working with data will only get better … at working with data. Where will the new generation of wise seniors come from? Here's my different vision. For the last dozen years, I’ve been teaching leadership to my students. “Leadership” is, in my case, a broad label that includes introductions to a host of human skills – not simply so-called “soft skills,” but the range of critical connecting, communicating, collaborating, and judgment-generating skills that all professionals know are fundamental to professional success from day 1 to day last – not just lawyers. I walk my students through an abbreviated history of the 20th century legal profession and changes wrought by 21st century demands (the broadening of client and social need, expansion and competition in modes of delivering legal services and information) and tools (data analytics and now Generative AI). Together, we explore the roles of creativity, imagination, and curiosity in understanding the roles of the contemporary and future professional. I introduce students to emotional intelligence concepts. We explore the critical importance of understanding collaboration and team-based projects. We talk about how “leadership” in practice means understanding and enabling others to thrive. Conflict resolution is a critical theme; so are systems and feedback loops; we spend little time on “problem solving” and focus instead on risk assessment. Because contextual learning always drives the lessons home best, as a group my students and I explore our own experiences with each of these concepts (like any good teacher, I share some my own experiences), often recapturing practices that might previously have been dismissed as trivial or irrelevant and translating them as leadership lessons. I have leaders among my students – section leaders in the marching band, sales managers for small businesses – who learn to see themselves in a different, broader light. Those are conversations and skills that law schools, like many professional education programs, have long given lip service to but rarely followed through on. Virtually every law school in the US claims that they train “leaders,” but only a handful put real faculty and curricular power behind the claim. Even among that handful of law schools, “leadership” means different things, in the hands of different teachers and deans. To some, it calls to mind reviving an older, almost romantic view of lawyers as civic leaders, personifications of personal and community virtue and the rule of law. Leadership education in that spirit leans heavily on ethical norms, on codes of professional responsibility, and the call of the 2007 Carnegie Report on legal education to infuse legal education with training in “professional identity formation.” To others, leadership emphasizes that fact that some number of law graduates will end up in corner offices and managing partner roles, in small and large private law firms, in corporate law departments, and in public sector and nonprofit organizations. Leadership education in that spirit leads heavily not only on ethics but also on life at the top of the proverbial heap. My vision for leadership aims instead helping all students succeed on their own terms. My goal is to help new lawyers build a suite of capabilities that will help them find their own ways in the world, whether as practicing lawyers or otherwise, and – importantly – will help them help others do the same. That’s the definition of leadership that I’ve adopted: find your voice, and help others find theirs. Training students to be “better at AI” trains them to steer eventually into career dead-ends – unless their career ambition bends toward tech, rather than law. Training students to be “the best humans they can be” trains them to steer toward opportunity. And even for the tech-motivated students, leadership skills are essential. The great thing about separating “leadership” from “law” in that way and separating “leadership” from “life at the top” is that it opens the door to training everyone in the skills that all professionals should have: collaborating and communicating effectively; developing and using EQ; targeting and recognizing success; and understanding and adapting to failure and loss. In my world, there is nothing “special” or distinctive about lawyers as leaders, or about lawyer-leaders. Technological and economic changes to the legal world are catalysts for that vision of leadership development. Look around the practice of law today, and you’ll see technology and economics rapidly and massively changing both the boundaries of what counts as “law” in the first place and what counts as “legal” – the shorthand for critical functions in business organizations – in the second place. “The legal profession” simply is not what it once was. Training new lawyers in leadership skills equips new graduates not simply to accept the profession as it has existed to this point and not simply to accept the roles that they step into when they begin careers. Leadership skills help those new lawyers to see and understand the systems changes that are happening all around them, including how those changes pay off via changing expectations in specific workplaces. At best, leadership skills will, in time, help those same lawyers not simply respond or react to change (a key concept behind “resilience” training, part of many leadership curricula) but also to participate in planning and driving change. That practice and those skills are keys to building successful, productive, and useful careers – and lives. In worlds increasingly defined and processed by technology, including new and evolving legal worlds, human presence and human contributions will matter more than ever, even if where and how they will matter is – presently – not always easy to see, and not always given simply by how they have mattered in the past. I am fortunate to have had my own deep training and practice in the leadership skills that I now share with my students; few law professors have equivalent or even similar personal histories. So, what I teach and how I teach it is, in some ways, unique to me. But I take every opportunity I can to share my vision and my practice, and I am always eager to talk with anyone who is interested in picking up any portion of what I do and running with it however they think best. To that end, my leadership curriculum is fully open and online and can be found here . That includes the content of readings and other assignments. A short law review essay that describes my motivation and personal history was published in the Tennessee Law Review in 2016 and can be found here . I look forward to learning from you. Professor Michael Madison is Professor of Law at the University of Pittsburgh School of Law in Pittsburgh, Pennsylvania, USA. He is a Senior Scholar with the University of Pittsburgh Institute for Cyber Law, Policy, and Security  (Pitt Cyber). At Pitt Law, he is Faculty Director of the Future Law Project and a John E. Murray Faculty Scholar. He is a principal investigator of the Workshop on Governing Knowledge Commons global research collaborative . He is a founder and leader of Future Law Works , an independent corps of volunteer leaders focused on re-institutionalizing legal education and the other institutions of the legal system. He is an affiliate faculty member with the University of Pittsburgh Center for Governance and Markets . From time to time, he publishes The Future Law Podcast , together with Dan Hunter , dean of the Dickson Poon School of Law at King’s College London.

As digital tools, including generative AI, become integral to our daily lives, legal professionals must adapt to leverage these advancements effectively. Tasks traditionally handled by associates, paralegals, and other legal professionals are increasingly being delegated to efficient, tireless digital assistants and AI tools. For instance, consider a law firm where AI-powered tools automatically sort through discovery documents, identifying relevant information faster than any human could. This shift enhances productivity and efficiency without significantly increasing costs, freeing up human professionals to focus on more strategic, value-added tasks. Fostering a culture of “human-tech interoperability” is crucial. This involves prioritizing user experience and human-centric design for digital tools to ensure seamless integration with existing legal workflows and databases. Additionally, promoting effective collaboration between lawyers, non-lawyers, and digital tools is vital for breaking down organizational silos and communication barriers. Viewing technology as “teammates” can help overcome adoption and adaptation barriers. Legal professionals should be encouraged to perceive AI-powered tools as valuable allies that enhance their capabilities rather than as replacements or threats.   Optimizing any labor-based service business involves adjusting the labor mix, processes, or tools. Many legal practices focus on growth through size and leverage, often neglecting tooling improvements. The contemporary method for adjusting the labor mix, termed “rightsizing,” involves smartly balancing capacity and demand. However, rightsizing typically favors high-fee earners and marginalizes support staff and allied professionals, leading to potential brain drain and business impact. For example, a firm that heavily invests in its partners but neglects the development of its paralegals may face operational inefficiencies and lower morale.   Rather than rightsizing, legal practices should focus on equipping the right people with the right skills for the right tasks at the right time. This strategy includes upskilling or reskilling talent while transforming some roles into digital services, thereby creating scale and more opportunities for talent. For instance, a paralegal trained in using AI tools for document review can handle significantly more work than before, enhancing their role and career prospects. Effective delegation skills are crucial for leveraging technology. If teammates struggle to delegate tasks to one another, they will find it even harder to delegate to technology. Viewing task and service automation technologies as teammates can help overcome adoption and adaptation barriers.   Incorporating digital services into legal workflows offers specific use cases where technology can enhance processes. For example, these technologies can handle routine tasks such as scheduling, document retrieval, and initial contract drafting, allowing legal professionals to concentrate on more complex and strategic work. Additionally, AI-powered tools can expedite legal research by quickly analyzing vast amounts of legal texts to find relevant information, significantly speeding up the research process. Imagine a scenario where an associate uses an AI tool to conduct comprehensive case law research in minutes rather than hours, providing more time for client strategy sessions.   Implementing a digitally integrated workspace in legal practices requires practical strategies. Begin with a thorough assessment of current workflows to identify where digital services can add value. Invest in training programs to ensure all team members are proficient in using new technologies. Develop a phased implementation plan to gradually integrate digital tools, minimizing disruption to existing processes. Continuously review and adjust the strategy based on feedback and performance metrics to optimize the integration process. For example, a law firm could start by integrating AI tools into its research department and gradually extend their use to contract management and client interactions.   However, potential challenges to adoption extend beyond mindset shifts. Ensuring data security and privacy is paramount, as legal practices handle sensitive information. The costs associated with new technologies can also be significant, necessitating careful budget planning. Additionally, resistance from employees wary of changes to their workflow must be managed through clear communication about the benefits and ongoing support available. It’s also crucial to address the varying levels of tech proficiency among staff, providing targeted training to bridge knowledge gaps and ensure everyone can use new tools effectively. Furthermore, maintaining compliance with legal standards and regulations when integrating technology requires meticulous planning and oversight to avoid potential legal pitfalls.   Adopting advanced technology in legal practices often encounters human skepticism or resistance. Many seasoned attorneys and staff fear that AI and digital tools might replace their roles, leading to job insecurity. For instance, in a mid-sized law firm, senior partners may resist using AI for contract analysis, doubting its accuracy and fearing the loss of their traditional expertise. Similarly, in legal departments within corporations, employees might be wary of automated document review systems, concerned about their reliability and the potential for errors. To address these concerns, leaders must foster a culture of openness and continuous learning. They should highlight successful case studies where technology has augmented human capabilities, such as an AI tool streamlining due diligence processes and enabling lawyers to focus on complex negotiations. By directly addressing these fears and showcasing the benefits, firms and departments can build trust and encourage the adoption of new technologies.   Experimentation and iterative learning are essential for successfully integrating technology into legal workflows. Both law firms and legal departments should pilot new digital tools in controlled environments before full-scale implementation. For example, a law firm might initially use AI-powered research tools in a small team to refine the tool’s effectiveness and address any issues. Similarly, a corporate legal department could test an automated contract management system on a limited number of contracts to gather feedback and improve the system. This approach allows legal professionals to learn from hands-on experience, make necessary adjustments, and gradually scale up the use of technology. Encouraging a mindset of experimentation helps create a dynamic environment where continuous improvement is valued, reducing the fear of failure and fostering innovation.   Viewing technology as a partner, not a competitor, is crucial for successful integration in legal practices. AI and digital tools should be seen as allies that enhance human capabilities rather than threats to job security. For instance, in a large law firm, AI can handle routine tasks like document sorting and initial case assessments, freeing up lawyers to engage in strategic thinking and client interactions. In a corporate legal department, digital tools can manage compliance tracking and reporting, allowing legal professionals to focus on high-stakes decision-making and advising the business. By emphasizing the collaborative potential of technology, leaders can change the narrative from one of replacement to one of augmentation. This partnership mindset ensures that both human and technological resources are used to their fullest potential, leading to greater efficiency and innovation in legal practices.   Legal professionals interested in embracing digital tools and transforming their practices can take the following steps:                     1.             Conduct a thorough assessment of current workflows to identify areas where digital tools can add value.                   2.             Develop a phased implementation plan  to gradually integrate digital tools, minimizing disruption to existing processes.                   3.             Invest in training programs to ensure all team members are proficient in using new technologies.                   4.             Foster a culture of continuous learning and adaptation  by regularly sharing success stories and practical examples of technology integration.   The legal profession stands on the brink of a transformative era driven by technological advancements. Embracing these changes requires a strategic and thoughtful approach to integrating digital tools and fostering human-tech interoperability. By prioritizing user experience, enhancing collaboration, and viewing technology as an ally, legal practices can unlock unprecedented levels of efficiency, productivity, and client satisfaction.   Now is the time to act. Begin by assessing your current workflows, investing in the right tools and training, and fostering a culture that embraces continuous learning and innovation. The future of the legal profession is bright for those who are willing to adapt and evolve. Lead the charge in transforming your practice, and become a pioneer in the modern legal landscape. Your journey towards a more efficient and effective legal practice starts today.

Background Generative Artificial intelligence has had a transformational impact on how quickly and effectively intelligent automation technology is deployed. What was once considered a potential replacement of automation technology has quickly become an enhancement to it. This article dives deeply into how this is unfolding in 2024. What is the point of AI? The purpose of artificial intelligence technology is to support people. This is what it is designed to do. Humans have things that they need to do and other things they like to do. The point is to use technologies like AI for what people do not want to do, yet need to do. The majority of individuals (working jobs that require a computer) tend to have a specialty that they must use their expertise for. Their tasks are typically divided into high-IQ tasks & low-IQ tasks. Leveraging AI & automation enables them to maximize their time on high-IQ activities, rather than the drudgery that can be taken over with technology. Now think about this replicated at scale. In a nutshell, this is the approach enterprises are taking on bringing in generative AI & large language models (LLMs) into their organizations. What are enterprises specifically using generative AI for? Now, there are some misconceptions of what generative AI can do vs. what it should do in 2024. These are two very different things. Initially, generative AI was scrutinized heavily on data security & potential for hallucination. However, this has slowly fallen by the wayside as enterprises have found clear ways to leverage LLMs, while ensuring their data is secure & setting up guard-rails + selecting specific use cases that eliminate most of the material potential for hallucination. For example, although enterprises can use generative AI to take in a host of information and make an underwriting decision on granting a loan to a particular individual, it should not be used for this as a person should be closely involved with any major decisions with implications of this magnitude. Enterprises should use generative AI to take over tasks that are not worth the time of a person, that take very little brainpower and are repetitious. An alternative here is to leverage generative AI to take in that host of information for that loan, organize it in a structured format, input the data into relevant systems and spit out necessary reports for a human to review and then make an underwriting decision on that loan. This is the same process yet reduces the risk of leaving the decision-making power in the hands of technology, while removing the drudgery around complex loan processing. How is this impacting established, well-known automation technologies? The two most well-known technologies across the intelligent automation space are robotic process automation (RPA) & intelligent document processing (IDP). RPA - In terms of enterprise process automation, RPA is being used in tandem with generative AI to fulfill complex needs. RPA cannot think nor execute tasks that have even the least bit of subjectivity. Yet, generative AI can take on the subjective portions of processes. For end-to-end enterprise use cases, generative AI adds very limited value by itself. However, when paired with RPA software bots that can take over data entry & data reconciliation tasks with 100% accuracy, it can add immense value. IDP - On the other hand - IDP is being impacted in that, historically, machine learning needed to be utilized within IDP engines to train semi-structured & unstructured document types like invoices, insurance claims, legal complaints, bank statements, patient prescription documents, etc. However, this was a very limited approach as one enterprise may have 300+ difference invoice types, which then requires 300+ different machine learning templates to create. This is cost-prohibitive, time-consuming and inefficient, along with being limited in accuracy of data when extracting via traditional machine learning. Instead, enterprises can now replace the traditional training via machine learning with LLMs that can take in unstructured & semi-structured documents without being trained. Pairing IDP engines with LLMs enable these engines to understand the context of data within a document immediately. With a small bit of configuration, fine-tuning and prompt-engineering, enterprises can achieve 95%+ accuracy on unstructured & even hand-written documents (assuming they're legible). This is a revolutionary change for intelligent document processing and optical character recognition (OCR) technology. It is akin to what's happening in current day with self-driving cars. In recent news, Tesla deprecated most of the original code and replaced it with AI-based technology for its self-driving features. RPA, IDP & LLMs - Enough acronyms for you? It may sound complex, but much more straightforward than it seems. This is because RPA & IDP have been used together in tandem for years. The only difference here is that now it's more like AI-based IDP is used in tandem with RPA. Nothing has changed on the RPA side, which is simply moving data around once it's been extracted. On the IDP side, it still takes care of the pre-processing & digitizing of documents, while LLMs are layered on top of IDP to bolster the accuracy and speed of data extracted from unstructured documents. This means implementation times are shorter and even business users can quickly and efficiently configure complex documents for high accuracy extraction of data. Previously, enterprises would entrust humans with the task of sifting through large sets of unstructured data. The issue is that the speed is limited to how quickly a human can read and understand this data so technology is ideal for this. Thankfully, generative AI is very good at understanding large sets of unstructured data. For example, in the legal space, LLMs are being used to summarize endless pages of legal contracts & documentation in seconds. Once data is summarized/extracted with LLM-powered IDP, RPA can be used to emulate any human actions needed on a computer screen. This includes inputting that data into a specific website, excel, word document, PowerPoint or system of record. This also includes using RPA to reconcile data from one location to another. For example, ensuring that the data on an invoice document is the same as the data that's been entered into an accounting system of record. In the case of making sense of a large amount of unstructured data… the impact that this will have on the legal, healthcare, insurance and financial services industries is high and still difficult to fully measure. Future of Automation The future of intelligent automation & generative AI is leveraging technologies like large action models (LAMs). A large action model is a model that takes actions based on the specific prompts it receives. You still must train it like RPA, but the training is much faster, yet it's output is more volatile/currently dangerous. It uses a bit of subjectivity & inference to conduct actions, rather than doing this in a purely linear fashion like RPA. This is the future of what can make waves in the intelligent automation space once this technology is able to be deployed in a manner with less risk. Gabriel Skelton is the Head of Artificial Intelligence Solutions at OpenBots. Gabriel assists firms in the selection & implementation of document processing automations involving unstructured & handwritten documents. Gabriel has a team of automation consultants throughout the healthcare, insurance and financial services industries that specialize in automations that involve the extraction of data from the most complex documents and porting that unstructured data directly into end system of records. Gabriel has a Master’s Degree in Entrepreneurial Leadership from Babson College’s Olin Graduate School of Business. Gabriel resides in Coral Springs, Florida with his wife and three children.

On a Saturday morning in 2016, I found myself sitting in a brightly lit auditorium in my role as an advisor for the Campbell Law School Law Review unaware that my life would take an unexpected turn. As a representative from Lex Machina, a legal analytics company, took the stage, I had no idea that the journey I was about to embark upon would lead me to the very frontiers of human knowledge and understanding. The platform itself was a marvel, a testament to the incredible power of artificial intelligence and machine learning to transform the way we approach the law. As I watched it process and analyze vast troves of legal documents with breathtaking speed and accuracy, identifying hidden patterns and insights that would have taken teams of human lawyers weeks or months to uncover, I felt a sense of exhilaration and wonder that I had never before experienced in my legal career.             I could imagine what would come—what did come—with predictive analysis platforms like Lex Machina that could equip attorneys with data-driven insights extracted from historical legal records, facilitating informed decision-making throughout various stages of litigation. They could gain strategic advantages by analyzing historical legal data,  helping them anticipate opposing counsel tactics, assess the likelihood of case outcomes, and refine their arguments for specific judges. But even in that moment of technological triumph, I could sense that there was something deeper at work, a fundamental shift in the very fabric of our legal universe. The old certainties and hierarchies that had governed the practice of law for centuries were beginning to crumble, giving way to a new reality shaped by the relentless flow of data and the ever-accelerating pace of change. It was as if a veil had been lifted, revealing a hidden dimension of the law that had always been there, but that we had lacked the tools and the imagination to perceive. As I delved into the labyrinthine world of legal technology, I couldn’t shake the feeling that I was grappling with something much larger than just a set of tools and techniques. It was as if I had stumbled upon a hidden chamber in the vast palace of human knowledge, a place where the very foundations of logic and reason seemed to shimmer and shift before my eyes. It seemed like Borges’s infinite library, containing all possible books, arranged in a vast and complex architecture that is at once wondrous and maddening. Looking for a foundation on which to build my understanding, I sought out the works of the great thinkers of the early twentieth century, the titans of mathematics and philosophy who had first begun to question the bedrock assumptions of their disciplines. Figures like David Hilbert, Alfred North Whitehead, Bertrand Russell, Gottlieb Frege, and Ludwig Wittgenstein had set out to build a grand edifice of logic and meaning, only to find that the ground beneath their feet was far less solid than they had imagined. In their quest for certainty and rigor, these thinkers had stumbled upon paradoxes and contradictions that threatened to undermine the very foundations of mathematics itself. The discovery of set-theoretic paradoxes, like Russell’s paradox of the set of all sets that do not contain themselves, had sent shockwaves through the intellectual world, casting doubt on the consistency and completeness of even the most basic mathematical systems. And, Kurt Gödel’s incompleteness theorems that demonstrate that any sufficiently complex formal system of mathematics will contain statements that are true but cannot be proven within that system. This fundamentally altered their understanding of mathematics, indicating that there are inherent limits to what can be definitively proven. Even as these thinkers grappled with the implications of their discoveries, they found themselves drawn deeper into a world of mystery and wonder. For the more they sought to pin down the nature of logic and meaning, the more elusive and paradoxical it seemed to become. It was as if they had stumbled upon a magic lamp, only to find that the genie inside was more powerful and unpredictable than they could ever have imagined. Mathematics would provide no foundation. Yet it  still was magical. It was the field of pioneers like Claude Shannon and Alan Turing, two towering figures whose work revolutionized our understanding of communication, computation, and the very nature of information itself. Shannon’s groundbreaking work on information theory introduced the concept of entropy as a measure of uncertainty in a message, drawing a powerful analogy between the flow of information and the laws of thermodynamics. It was a connection that had profound implications not just for communication engineering, but for our understanding of the fundamental principles that govern the universe itself. Perhaps empirical sciences, especially physics would provide the foundations to clarify the mysteries that plagued me. But, what do these mathematical formula represent? I grappled with these ideas, eventually finding myself drawn to the work of Norbert Wiener, the brilliant mathematician and philosopher who had coined the term “cybernetics” to describe the study of the structures of communication and control in machines and living organisms. Wiener’s vision of a world in which feedback loops and information processing played a central role in the functioning of all systems, from the biological to the social, influenced my understanding of the interconnectedness of knowledge and the importance of interdisciplinary thinking. It was a vision that also influenced Claude Lévi-Strauss, the French anthropologist whose structuralist approach to the study of culture and society revolutionized the field of anthropology. Lévi-Strauss drew heavily on the concepts of cybernetics and information theory in his analysis of the deep structures that underlie human thought and behavior, arguing that the human mind itself could be understood as a kind of information processing machine. This idea of the mind as a computational engine seemed appealing, It was one that had also captured the imagination of Neal Stephenson in his novel Cryptonomicon , which I read at around that time. Stephenson’s tale wove together the threads of mathematics, cryptography, and the history of computing in a sprawling, multi-layered narrative that had left a deep impression on me. His vision of a world in which information was the ultimate currency, and in which the power to control and manipulate that information was the key to shaping the future, seemed to resonate with the insights of thinkers like Shannon, Turing, Wiener and Lévi-Straus. As I reflected on these ideas, I found myself returning again and again to the words of the anthropologist, Clifford Geertz, who argued that law was not simply a set of rules or principles, but a way of imagining the real. In a world increasingly shaped by the flow of information and the power of algorithms, it seemed to me that the legal imagination would need to evolve in profound ways to keep pace with the changing nature of reality itself. This realization led me to begin exploring the emerging field of legal informatics, which sought to apply the tools and methods of information science to the study and practice of law. It was a field that was still in its infancy, but one that held enormous promise for unlocking new insights into the complex interplay of law, technology, and society. Now, it seemed, I was making progress. I traced the ripple effects of the foundational crisis of mathematics through the decades that followed, I saw how it had shaped the course of intellectual history in profound and often unexpected ways. By representing the structures of reality, foundations could be validated for coherence. This seemed promising.  I learned of the Vienna Circle’s attempt to build a “unified science” on the basis of logical positivism. This was the dream of Rudolph Carnap: to create a formal language that could capture the logical structure of the world in its grammar and syntax. All of these were in some sense responses to the challenges posed by the foundational crisis, attempts to find a new basis for knowledge in a world where the old certainties had crumbled away. This seemed to be a light of clarity for understanding legal technology! If formal languages, like Carnap imagined, could be used to capture legal reasoning, then computational law would have a clear and certain foundation! This met the world I witnessed evolving around me. Computational contracts, rule-based systems to represent laws as a set of rules, could make law relatively easy to understand! Legal programs were using languages like Prolog   to express the law as logical statements, and law could be tagged with keywords to create what the law librarians were calling “ontologies” of legal knowledge that define and structure legal concepts and their relationships, creating formalized knowledge bases. Lawyers were using tech to tackle legal complexity.  Computational contracts turn agreements into code, enabling clear and automatic execution.  Rule-based systems analyze situations and suggest legal options, promoting consistent decision-making. Legal ontologies promised a structured vocabulary and framework for representing legal concepts, making the law more machine-readable. These tools claim to make law more understandable and interoperable. They make claims like Carnap, who also hoped to remove the ambiguity and subjectivity of natural language through formalization. Perhaps Carnap’s attempts to formalize language showed the way forward! But, then, alas, I read of the fate of logical positivism, and again my heart faded. There would be no foundation for knowledge here as well. Two towering figures had brought it to a halt: Kurt Gödel, the mathematician and friend to Einstein, and Willard van Ormand Quine, the dean of American philosophy for half a century. Gödel developed a precise and cutting analysis of logical systems like the one that Carnap sought to create. And in his two “incompleteness theorems” he demonstrated that in any sufficiently expressive formal system (e.g., one capable of arithmetic), there will always be true statements that cannot be proven within that system. It exposed inherent limitations of purely axiomatic, formal approaches. This implies that no matter how sophisticated a formalization of law is, there will always be legal propositions or interpretations that fall outside its deductive capabilities. It suggests that a formal legal system will  always require human input and  oversight beyond pure computation. Quine, a longtime friend and correspondent with Carnap, advanced a radical critique of the foundational syntax of Carnap’s formal language, which undermined the idea of a sharp boundary between empirical fact and logical necessity, and opened up new vistas of uncertainty. Quine’s approach viewed knowledge as webs of signification. I traced these ideas forward into the realms of jurisprudence and legal theory, I saw how they had shaped the course of twentieth-century legal thought in profound and often counterintuitive ways. The naturalized epistemology of Quine and Brian Leiter, which sought to ground legal reasoning in the methods and findings of empirical science, was in some sense an attempt to find a new foundation for law in the wake of the foundational crisis. And yet, even as these thinkers sought to build a more rigorous and scientific approach to legal theory, they found themselves grappling with the same paradoxes and mysteries that had haunted the thinkers of the early twentieth century. For if the law was indeed a mirror of the deepest structures of reality, then any attempt to reduce it to a set of logical rules or empirical facts was ultimately doomed to incompleteness. In the midst of this swirling vortex of ideas and intuitions, I began to glimpse the true power and potential of legal technology. For if the foundational crisis had taught us anything, it was that the world was far more complex and mysterious than any logical system or formal language could ever hope to capture. And yet, at the same time, it was precisely this complexity and mystery that gave rise to the incredible richness and diversity of human experience, the endless possibilities for creativity and innovation that made the law such a vital and dynamic force in the world. And so, in the end, the law was not just a set of rules or procedures, but a living, breathing, evolving expression of the deepest values and aspirations of the human spirit. And if we could harness the power of technology to serve those values and aspirations, to create new tools and platforms that empowered people to participate more fully in the process of justice, then we would be fulfilling the true promise of the legal profession. But even as I threw myself into this new endeavor with all the passion and energy I could muster, I couldn’t shake the feeling that I was still only scratching the surface of a much deeper truth. The more I learned about the cutting-edge developments in fields like machine learning, natural language processing, and knowledge representation, the more I realized that the traditional tools and methods of legal analysis were woefully inadequate to the task of making sense of this brave new world. I learned that machine learning can analyze historical case data to predict the likely outcomes of new legal cases, considering factors like legal issues, precedent, and history. And, natural language processing (NLP) can automate the classification of legal documents (contracts, briefs, etc.) and extracts key information like dates, entities, and legal provisions. But, at what cost? What insights into law and language were revealed by these developments? Striving for more insights, I explored the work of philosophers like Edmund Husserl, and his students, Martin Heidegger and Jacques Derrida, whose radical critiques of Western metaphysics and language had shaken the foundations of modern thought. Heidegger’s notion of “being-in-the-world,” with its emphasis on the primacy of lived experience and the inextricable entanglement of subject and object, seemed to offer a way out of the dualistic thinking that had long dominated legal theory. And Derrida's concept of “ différance , with its playful deconstruction of the binary oppositions that structure our language and thought, opened up new possibilities for understanding the law as a fluid and dynamic system, always in the process of becoming. As I grappled with these ideas, I found myself exploring the work of contemporary feminist thinkers like Karen Barad and Rosi Braidotti, whose “new materialist” philosophies sought to bridge the gap between the natural and the social sciences, the human and the nonhuman. Barad's notion of “agential realism,” with its emphasis on the inseparability of matter and meaning, challenged me to think in new ways about the relationship between law and the material world. And Braidotti's vision of a “posthuman” future, in which the boundaries between the human and the technological become increasingly blurred, seemed to offer a glimpse of a new kind of legal order, one in which the old distinctions between mind and body, reason and emotion, fact and value, were giving way to a more fluid and dynamic understanding of the world. As I delved deeper into this new intellectual landscape, I began to see connections and resonances that I had never noticed before. The insights of quantum physics, with its strange and paradoxical world of entanglement and uncertainty, seemed to echo the insights of Buddhist and Hindu philosophy, with their emphasis on the fundamental interconnectedness of all things. Luhmann’s concept of “ autopoiesis ” (self-organization), which had emerged from Francisco Varela’s study of living systems, seemed to offer a new way of understanding the dynamics of legal systems, with their complex feedback loops and emergent properties. As I continued my journey through the rich and nuanced landscape of legal technology, I found myself increasingly drawn to the work of contemporary philosophers who were grappling with the deep implications of the information age for our understanding of knowledge, reality, and the human condition. Chief among these was Luciano Floridi, whose groundbreaking work in the philosophy of information had opened up new vistas of insight and understanding. Floridi’s vision of the world as a complex web of informational structures and processes resonated deeply with my own experiences in the realm of legal technology, where the flow of data and the processing of information were rapidly becoming the key drivers of innovation and change. But it was not just Floridi’s technical insights that captured my imagination. It was also his profound ethical and humanistic vision, his belief that the information age presented us with both incredible opportunities and daunting challenges for the future of human knowledge and flourishing. In a world where the boundaries between the virtual and the real were becoming increasingly blurred, Floridi argued, we needed to develop new ways of thinking about the nature of the self, the value of privacy, and the meaning of intellectual property. Alongside Floridi, I found myself deeply influenced by the work of James Ladyman and his collaborator Don Ross, whose structural realist approach to the philosophy of science seemed to offer a powerful new framework for understanding the nature of scientific knowledge in the age of big data and complex systems. Ladyman’s and Ross’s magnum opus, Everything Must Go , was a tour de force of philosophical argumentation, a sweeping critique of the traditional metaphysical assumptions that had long dominated Western thought. In place of the old dichotomies between mind and matter, subject and object, they proposed a new vision of reality as a vast and interconnected web of mathematical structures and informational processes. For Ladyman and Ross, the task of science was not to uncover the hidden essence or intrinsic nature of things, but rather to map out the complex patterns of relations and dependencies that gave rise to the observable phenomena of the world. And in this view, the traditional boundaries between fields like physics, chemistry, biology, and even the social sciences began to dissolve, revealing a deeper unity and interconnectedness that had long been obscured by the silos of academic specialization. As I reflected on these ideas, I began to see how they might transform our understanding of the law and legal reasoning itself. If the world was indeed a vast and interconnected web of informational structures, as Floridi and Ladyman suggested, then the law could no longer be seen as a static set of rules or principles, but rather as a dynamic and evolving system that was constantly processing and responding to new flows of data and information. And if the task of science was to map out the complex patterns of relations and dependencies that gave rise to the observable phenomena of the world, then the task of legal reasoning must be to map out the complex patterns of rights, obligations, and liabilities that gave rise to the social phenomena of justice and fairness. In this view, the power of legal technology lay not just in its ability to automate or streamline the tasks of legal analysis and prediction, but in its ability to help us navigate the vast and complex informational landscape of the law itself. By using tools like machine learning, natural language processing, and network analysis, we could begin to uncover the deep structures and patterns that underlie the surface complexity of legal doctrine and precedent. And in doing so, we could begin to develop a new kind of legal reasoning, one that was more responsive to the dynamic and evolving nature of the social world, more attuned to the complex interdependencies and feedback loops that shape the behavior of individuals and institutions alike. Of course, as with any powerful new technology, the rise of legal informatics also posed daunting challenges and risks. As Floridi himself had warned, the proliferation of digital information and the increasing power of algorithmic decision-making raised profound questions about the nature of privacy, autonomy, and human agency in the age of big data. For a moment, I felt like I was standing on the edge of a new frontier, a vast and uncharted territory that stretched out before me in every direction. And though I knew that the journey ahead would be long and difficult, full of twists and turns and unexpected obstacles, I also knew that I had no choice but to keep pushing forward, to keep exploring the boundaries of the possible and the impossible, the known and the unknown. For in the end, the quest for a new understanding of law and reality was not just an intellectual exercise, a game of abstraction and theory. It was a deeply personal journey, a search for meaning and purpose in a world that often seemed chaotic and indifferent, a world in which the old certainties and verities were crumbling away, leaving us to confront the raw and unmediated mystery of existence itself. And so, I continue to press forward, guided by the conviction that in the mysterious depths of computational law and legal informatics, there lie secrets and wonders yet to be discovered, insights that could transform not only the law, but the very fabric of human society. It is a journey that has taken me from the heights of philosophical speculation to the cutting edge of technological innovation, and one that I know will continue to unfold in strange and unpredictable ways in the years to come. But I am sustained by the knowledge that I am not alone on this path, that there are others who share my fascination and my commitment, and who are working tirelessly to bring about a future in which the law is a powerful instrument of social justice and human flourishing. Together, we press on into the unknown, driven by a shared sense of purpose and a deep faith in the transformative power of human reason and creativity. As I reflect on my journey through the infinite halls of this Library of Legal Babel, I am struck by the realization that, like the eternal traveler in Borges’ story, I am destined to wander forever through its labyrinthine depths. The secrets and wonders that lie hidden within its shelves are not meant to be fully grasped or possessed, but rather to be continually sought and marveled at, in a never-ending cycle of discovery and revelation. And yet, far from being a source of despair or frustration, this realization fills me with a sense of profound hope and purpose. For just as the traveler’s solitude is gladdened by the elegant hope of the Library’s underlying Order, so too am I sustained by the conviction that, beneath the seeming chaos and complexity of the legal-informational landscape, there lies a deeper pattern and meaning waiting to be uncovered. It is this hope that drives me forward, even as the path ahead stretches out into the unknown. Like the explorers and adventurers of old, I am drawn onward by the lure of the horizon, the promise of new vistas and uncharted territories waiting to be mapped and understood. And though I know that the journey will be long and arduous, filled with twists and turns and unexpected challenges, I am comforted by the knowledge that I am part of a larger community of seekers and dreamers, all striving to push the boundaries of what is possible and to build a better, more just world. In the end, then, my wanderings through the Library of Legal Babel are not a solitary quest, but a shared endeavor, a collaborative effort to shine the light of reason and understanding into the darkest and most obscure corners of the legal universe. It is a task that will require all of our creativity, ingenuity, and determination, but one that holds the promise of unlocking a new era of justice and flourishing for all humanity. And so, like Borges’ eternal traveler, I press on, forever seeking, forever learning, forever marveling at the wonders and mysteries that lie waiting to be discovered in the infinite stacks of the Library. It is a journey that has no end, but one that is all the more glorious and worthwhile because of it. For in the end, the true measure of our success will not be the destination we reach, but the knowledge and wisdom we gather along the way, and the lives we touch and transform through our ceaseless pursuit of truth and justice. Reading List Here are some books to stimulate conversation and raise questions. I have tried to avoid overly technical books, either philosophically or technologically. Philosophy of Mathematics: 1. Doxiadis, Apostolos, and Christos Papadimitriou. Logicomix: An Epic Search for Truth . Bloomsbury, 2009. 2. Sigmund, Karl. Exact Thinking in Demented Times: The Vienna Circle and the Epic Question for the Foundations of Science . Basic Books, 2017. Information and Computation: 3. Gleick, James. The Information: A History, A Theory, A Flood . Vintage, 2012. 4. Bernhardt, Chris. Turing’s Vision: The Birth of Computer Science . MIT Press, 2016. Cybernetics and Structuralism: 5. Geoghegan, Bernard Dionysius. Code: From Information Theory to French Theory . Duke University Press Books, 2022. Anthropology 6. Geertz, Clifford. The Interpretation of Cultures . Basic Books, 2017. Logical Positivism: 7. Sigmund, Karl. Exact Thinking in Demented Times: The Vienna Circle and the Epic Quest for the Foundations of Science . Basic Books, 2017. 8. Leiter, Brian. Naturalizing Jurisprudence: Essays on American Legal Realism and Naturalism in Legal Philosophy . Oxford University Press, 2007. Phenomenology: 9. Zahavi, Dan. Phenomenology: The Basics . Routledge, 2018. 10. Dreyfus, Hubert L. Skillful Coping: Essays on the Phenomenology of Everyday Perception and Action . Oxford University Press, 2014. 11. Clark, Andy. Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence . Oxford University Press, 2003. New Materialisms: 12. Coole, Diana, and Samantha Frost, editors. New Materialisms: Ontology, Agency, and Politics . Duke University Press Books, 2010. 13. Barad, Karen. Meeting the Universe Half-way: Quantum Physics and the Entanglement of Matter and Meaning . Duke University Press Books, 2007. 14. Braidotti, Rosi. Posthuman Feminism . Polity, 2022. Eastern Thought: 15. Siderits, Mark, Evan Thompson, and Dan Zahavi, editors. Self, No Self?: Perspectives from Analytical, Phenomenological, and Indian Traditions . Oxford University Press, 2013. 16. Hinton, David. Existence: A Story . Shambhala, 2016. Philosophy of Information: 17. Floridi, Luciano. Information: A Very Short Introduction . Oxford University Press, 2010. 18. Floridi, Luciano. The Fourth Revolution: How the Infosphere is Reshaping Human Reality . Oxford University Press, 2014. Metaphysics: 19. Ladyman, James, and Don Ross. Every Thing Must Go: Metaphysics Naturalized . Clarendon Press, 2007. Complexity: 20. Mitchell, Melanie. Complexity: A Guided Tour . Oxford University Press, 2009. 21. Parisi, Giorgio. In a Flight of Starlings: The Wonders of Complex Systems . Penguin Press, 2023. Fiction: 22. Stephenson, Neal. Cryptonomicon . William Morrow Press, 2009. 23. Borges, Jorge Luis. Labyrinths: Selected Stories & Other Writings . New Directions, 1962. 24. Chiang, Ted. Exaltation: Stories . Vintage, 2019. 25. Robinson, Kim Stanley. Aurora . Orbit Press, 2015. 26. Goldstein, Rebecca. Incompleteness: The Proof and Paradox of Kurt Gödel . W. W. Norton & Co., 2005. Kevin P. Lee is a Professor of Law at North Carolina Central University , known for his insights into the philosophical and social implications of law and technology. His research seeks to unravel the intricate relationships between legal frameworks, artificial intelligence, and societal values. His academic expertise spans jurisprudence, AI ethics, and law, positioning him as a pioneer in examining how cutting-edge technologies alter legal norms and human rights. Beyond the classroom, Professor Lee plays a pivotal role in shaping policies and advocating for social justice and equity. As the Intel Social Justice and Racial Equity Professor of Law, he works to foster inclusivity within legal education and the profession, championing a legal landscape that mirrors the diversity of the community it represents. His scholarship weaves together in-depth philosophical knowledge with a passionate appeal for leveraging technology to bolster human dignity and societal welfare. His innovative courses and public lectures emphasize the transformative power of education in engaging with contemporary challenges and opportunities. Professor Lee is most known for his intellectual rigor, ethical integrity, and visionary outlook. He is a leading authority in discussions on the future of law, technology, and social equity. His dynamic teaching approach and scholarly work inspire future legal minds to pursue their careers with purpose, integrity, and a commitment to societal good.

Artificial intelligence (AI) refers to the use of computer systems to perform tasks that would normally require human intelligence, such as learning, problem-solving, and decision-making. In the practice of law, AI is being used to automate and streamline various legal processes, including legal research, document review, and prediction of case outcomes.

AI is being used in several legal tech solutions to improve efficiency, reduce costs, and enhance the quality of legal services.

Some examples of how AI is being used in legal tech include:

• Legal research: AI can be used to search through large volumes of legal documents quickly and accurately, saving lawyers and paralegals time and effort.

• Document review: AI can be used to review and analyze large volumes of documents, such as contracts or discovery materials, to identify relevant information or patterns.

• Predictive analytics: AI can be used to analyze data and predict the outcomes of legal cases or disputes, helping lawyers and clients to make informed decisions.

• Legal writing: AI can be used to generate legal documents, such as contracts or pleadings, by combining standard language with specific input provided by the user. • Client intake and triage: AI can be used to process client intake forms and triage cases, routing them to the appropriate lawyer or team for further review.

Machine learning is a subfield of artificial intelligence focused on the development and use of algorithms and statistical models enabling computers to learn from data and improve their performance on a specific task.

In machine learning, a computer is fed a large dataset and uses that data to train a model to perform a specific task. The model is then tested on a separate dataset to evaluate its performance. If the model performs well, it can be deployed in a real-world application. If the model does not perform well, it can be adjusted and retrained using additional data or different algorithms until it performs satisfactorily.

There are several types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.

• In supervised learning, the model is trained on a labeled dataset, meaning that the input data accompanies the correct output. The model makes predictions based on this labeled data and is then tested on a separate dataset to evaluate its accuracy.

• In unsupervised learning, the model is not provided with labeled data and must find patterns in the input data on its own.

• In semi-supervised learning, the model is provided with some labeled data and some unlabeled data and must use the labeled data to make predictions about the unlabeled data.

• In reinforcement learning, the model is trained to take actions in an environment to maximize a reward.

Clients are aware of technology’s role in today’s world.

To meet your clients where they are demands several things. It demands recognizing that you don’t know everything and be willing to seek out support. It demands using your existing technological tools more productively and learning more about their capabilities. It demands developing an awareness of other technologies that you could be using to benefit your clients.

It’s up to you to decide how you want to make your away in an ever-more connected and yet seemingly chaotic and dynamic world. My simple advice is this: Don’t live in an alternative reality. Accept the reality that exists and learn to succeed in it. Your clients expect you to.

The legal and tech worlds are nearing an inflection point. I call it the tech inflection. Lawyers have a bad habit of thinking that something is hard or complex because it needs to be that way. Lawyers often make things more complex than they need to be.

The tech inflection describes the moment when you acknowledge that something challenging need not be so challenging. For example, before services like Uber, people just accepted that getting from A to B was challenging. After Uber, people understood that getting from A to B could be and should be easier.

The tech The same concept applies to the legal industry and the delivery of legal services. Time consuming tasks traditionally done by humans can now be automated and as a result be done be performed more easily and more accurately such as creating a new client intake form and processing that form, creating a basic will or trust, creating a new contract, or automating the review of a contract.