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Key Takeaways 1. Technology Is Driving Change Faster Than Many Firms Expected Legal has historically been slower to adopt operational and technological transformation than other professional services industries. According to Seth, that dynamic is changing because clients now have access to technology that allows them to perform more work internally and demand new service models from outside counsel. 2. Outside Capital Is Changing Competitive Dynamics Whether firms pursue outside investment or not, the presence of private capital is introducing new expectations around growth, operational maturity, leadership structure, and technology investment. 3. Not Every Law Firm Is the Same Business Contingency-fee firms, creditors' rights firms, subscription-based firms, and traditional billable-hour firms operate under fundamentally different business models. 4. Investors Focus on Predictability, Not Tradition Factors such as client concentration, leadership depth, operational processes, recurring revenue, and dependence on individual practitioners heavily influence valuation. 5. Leadership Matters Beyond Legal Expertise The firms attracting investment tend to have leadership structures and management depth that allow them to function as organizations rather than extensions of a single founder. 6. AI Is Accelerating Existing Trends Automation, client adoption of AI, and changing economics are forcing firms to reconsider staffing models, pricing structures, and the value they deliver. 7. Legal Is Entering a Broader Business Transformation The legal industry's future may increasingly resemble other professional services sectors that have already undergone consolidation and technology-enabled transformation. ________________________________________ Guest Notes Seth Deutsch | LinkedIn Profile Founder and Managing Partner, Samson Partners Group Seth Deutsch is the Founder and Managing Partner of Samson Partners Group, an advisory firm that helps founders understand enterprise value, prepare businesses for liquidity events, and navigate transactions involving private equity and strategic buyers. Throughout his career, Seth has acquired more than 80 founder-led businesses and evaluated thousands more across professional services, accounting, architecture, engineering, and other industries.

Key Takeaways 1. Institutions Can Appear Stable Until They Are Not Ross's experiences at both Brobeck during the dot-com collapse and Credit Suisse ahead of the financial crisis reinforced an important lesson: organizations that appear durable can deteriorate much faster than most people expect. Understanding underlying incentives and business fundamentals often matters more than reputation or size. 2. Incentives Drive Behavior More Than Policies When organizations struggle with legal risk, compliance failures, or governance breakdowns, the root cause is often misaligned incentives rather than technology limitations. Understanding how people are rewarded or penalized provides valuable insight into decision-making behavior. 3. Legal Departments Are Becoming Information Hubs Modern legal teams increasingly serve as repositories for organizational knowledge, contracts, obligations, commercial terms, and business facts. Many emerging legal technologies are helping organizations organize and access information just as much as they are delivering legal analysis. 4. Access to Information Does Not Eliminate the Need for Judgment As AI tools become more widely available, competitive advantage shifts away from who has information and toward who can interpret it effectively. The ability to understand client goals, evaluate tradeoffs, and apply context remains a critical differentiator. 5. AI Will Change Legal Education But Not the Need for Critical Thinking Law schools, firms, and employers continue to wrestle with how AI should be incorporated into education and practice. The challenge is not whether future lawyers should use AI, but how they can develop professional judgment while leveraging increasingly powerful tools. 6. Litigation Decisions Are About More Than Legal Merits Evaluating litigation involves financial, operational, reputational, and strategic considerations. Predictive tools may help organizations assess outcomes, but human judgment remains essential for evaluating the broader business implications of disputes. 7. Relationships Outlast Professional Credentials One of Ross's strongest observations is that decades later, he remembers the people who influenced him far more than the specific lessons he learned in school. Long-term success often depends on building and maintaining meaningful professional relationships. ________________________________________ Guest Notes Ross Weiner | Linkedin Advisor, Board Member, Former General Counsel, and Entrepreneur Ross Weiner has built a career at the intersection of law, finance, technology, and entrepreneurship. His experience includes roles at major law firms, investment banking institutions, and M&A advisory firms, as well as co-founding Axon Partners and serving on advisory and nonprofit boards. Throughout the conversation, Ross offers a practical perspective on navigating uncertainty, managing risk, evaluating business decisions, advising founders, and adapting professional expertise in an increasingly AI-enabled world.

Key Takeaways 1. The Legal Industry Is Embracing AI Without a Clear Consensus on the Destination While AI adoption is accelerating across legal technology, there is still considerable disagreement about where the technology is heading and how disruptive it will ultimately become. Organizations are making strategic decisions in an environment where even industry experts see the future differently. 2. Education and Enablement Remain the Biggest Obstacles to Successful Adoption Many of the challenges associated with AI stem not from flaws in the technology itself, but from misunderstandings about how it works and where its limitations lie. Training, oversight, and user education are essential components of responsible adoption. 3. Defensibility Matters More Than Novelty Whether legal teams use traditional review methods, active learning, or generative AI, the standard remains the same: produce reliable, defensible outcomes that can withstand scrutiny. The technology may change, but the obligation to validate results does not. 4. Measuring Success Requires More Than Accuracy Aron provides one of the clearest explanations of recall, precision, and richness ever discussed on the podcast, emphasizing why those measurements matter more than simple accuracy when evaluating discovery workflows and review performance. 5. Translating Attorney Intent Is Becoming a Core AI Skill As legal professionals increasingly rely on AI tools, success depends on effectively communicating legal judgment, subject matter expertise, and case strategy to AI systems. Organizations that build repeatable methods for capturing and translating that expertise will have a significant advantage. 6. Change Management Is Emerging as a Critical Component of AI Governance AI systems evolve continuously. Organizations must develop processes for testing, validating, and communicating model changes while maintaining consistency and defensibility across legal matters. 7. Accountability Has Not Changed, Even If the Technology Has Every participant in the legal ecosystem has responsibilities when deploying AI, from technology providers to law firms and end clients. Ultimately, professionals remain responsible for the work product they produce and the decisions they make. ________________________________________ Guest Notes Aron Ahmadia, Vice President of Applied Science, Relativity Aron Ahmadia leads applied science initiatives at Relativity, where he helps guide the development of AI capabilities used across legal technology, investigations, and discovery workflows. With a background in computational science, machine learning, and large-scale data systems, he focuses on translating complex technical concepts into practical applications for legal professionals. LinkedIn Bio: Aron Ahmadia | LinkedIn

Key Takeaways 1. AI Is Different from Previous Legal Technology Waves Unlike e-discovery, which emerged to solve a clearly defined problem, generative AI arrived as a powerful solution before many organizations fully understood what problems it should solve. 2. Law Firms Must Start with Business Problems Organizations often say they "need AI" when what they actually need is to automate a process, improve decision-making, reduce costs, or address inefficiencies. 3. Innovation Requires Executive Buy-In Successful technology adoption depends on visible support from firm leadership. Without top-level sponsorship, new initiatives often struggle against organizational resistance. 4. Process Comes Before Technology You cannot automate what you cannot define. Before deploying AI, firms should document workflows, identify bottlenecks, and understand how work gets done. 5. Lawyers Are Trained as Risk Managers Legal professionals may be slower adopters, but their focus on risk and defensibility provides a valuable lens for evaluating emerging technologies. 6. AI Should Augment, Not Replace Judgment Current AI tools are valuable for drafting, summarizing, organizing information, and identifying insights, but they still require human review and oversight. 7. Invisible Technology Often Wins The most successful legal technology solutions are often those that solve problems without forcing lawyers to become technologists. 8. Governance Matters More Than Ever As AI becomes easier to access, issues such as data security, model risk, code provenance, and compliance become critical considerations. 9. Law Firms Are Successful, Which Makes Change Hard Because many firms continue to perform exceptionally well financially, there is often limited urgency to fundamentally change traditional business models. 10. Skepticism Is Healthy Tom advocates experimenting with AI while maintaining a healthy filter for marketing claims, hype, and unrealistic expectations. Guest Bio Tom Barnett is a Senior Director at Maker5, a legal technology venture studio and advisory firm. With more than two decades of experience at the intersection of law, technology, data science, and innovation, Tom has built and led advanced analytics, e-discovery, and AI-focused teams at some of the nation's leading law firms, including Sullivan & Cromwell, Paul Hastings, and Jackson Lewis. At Paul Hastings, he founded and led a 20-person data science, analytics, and investigations team that was ultimately acquired by one of the world's largest private equity firms. Today, he advises organizations on AI strategy, legal technology innovation, risk management, governance, and the practical realities of technology adoption in legal services. Tom Barnett | LinkedIn Profile

Key Takeaways Proactive data privacy and security programs are essential for AI, and legal, IT, and compliance teams must collaborate to govern it effectively.States are rapidly developing their own AI regulations and organizations are facing a growing patchwork of compliance obligations.Effective regulation must balance innovation, consumer protection, constitutional rights, and business realities.AI is accelerating the shift toward value-based legal services and away from the billable hour.AI can unlock institutional knowledge across decades of pleadings, filings, and legal work product.Firms may gain competitive advantages through better retrieval and application of accumulated expertise, helping to solve succession and retention challenges.Tamara Lemmon | Wilson Sonsini Guest Notes Tamara Lemmon Tamara Lemmon is an associate in Wilson Sonsini's litigation and investigations practice based in Salt Lake City. Her work focuses on complex litigation, investigations, data privacy, and regulatory compliance.

Key Takeaways 1. Attorney Adoption of AI Has Accelerated Faster Than Any Prior Legal Technology Unlike predictive coding and earlier e-discovery tools, attorneys can interact directly with AI and immediately see results.The visibility of AI outputs has significantly increased enthusiasm for adoption.Law firms are experiencing unprecedented demand from lawyers who want to experiment with and deploy new AI tools. 2. AI Is Leveling the Legal Playing Field Generative AI reduces barriers to sophisticated legal analysis and document review.Smaller firms, plaintiff-side practices, and self-represented litigants now have access to capabilities that were previously resource-intensive.The impact of AI on access to justice continues to create both opportunities and new challenges. 3. Hallucinations Are Only One AI Risk Legal professionals often focus on hallucinated citations, but consistency problems, omissions, context drift, and validation issues remain equally important.Asking the same question multiple times can generate different results.Human review and verification remain critical. 4. Governance Must Precede Implementation Outside counsel guidelines, client requirements, ethical walls, and vendor approval processes all influence whether AI tools can be deployed.Some clients require approval before firms may use specific technologies.Effective governance is becoming a competitive advantage for firms adopting AI. 5. Administrative Workflows May Deliver the Fastest ROI AI is helping litigation support teams automate internal processes, improve ticketing systems, manage document production issues, and surface institutional knowledge.Many of the biggest efficiency gains are happening outside of substantive legal work.Reusability and knowledge capture may ultimately produce more value than individual use cases. 6. Firms Are Evaluating Technology on Six-Week Timelines AI capabilities are evolving faster than traditional technology planning cycles.Organizations can no longer treat AI adoption as a multi-year initiative.Evaluation periods are shrinking while implementation decisions carry long-term consequences. 7. Vendor Proliferation Creates a New Challenge Firms are seeing a flood of highly specialized AI products.Point solutions may solve individual problems but create operational complexity.Legal organizations must balance innovation against integration, governance, and support requirements. 8. Litigation Timelines Remain Much Longer Than Technology CyclesAI tools may evolve monthly.Litigation matters often last years.Technology decisions made today may impact legal workflows long after today's tools have changed. 9. AI "Slop" Creates Real Costs for Clients Firms are seeing increased filings and legal activity enabled by generative AI.Even weak or repetitive claims require a response.The resulting workload can become a hidden operational cost for organizations. 10. Success Depends on Balancing Innovation and Verification Firms must remain innovative while validating outputs carefully.Human expertise remains essential for evaluating legal strategy and quality.The future belongs to organizations that combine technology with strong quality controls. Guest Notes Chris Bojar Head of E-Discovery and Litigation Support, Barack Ferrazzano Chris Bojar leads e-discovery and litigation support for the Chicago-based law firm Barack Ferrazzano. His work spans discovery management, legal technology evaluation, vendor governance, document review workflows, and AI implementation across litigation matters. Chris operates on the front lines of legal operations, helping attorneys navigate both traditional e-discovery challenges and emerging AI technologies. Chris Bojar | LinkedIn Profile

Key Takeaways 1. AI Is More Transformative Than the Internet Bennett argues that generative AI represents a technological shift comparable to electricity and potentially more disruptive than the computer or the internet. 2. Data Is an Organization's Most Valuable Untapped Asset Most corporate knowledge sits dormant in data repositories. Organizations that can structure, govern, and leverage their data effectively will unlock the greatest AI value. 3. The Traditional Billable Hour Model Is Under Pressure Based on analysis of legal work patterns, Bennett believes AI can perform a significant portion of the work traditionally completed by lawyers, creating major pressure on traditional law firm economics. 4. AI Should Be Viewed as Augmentation, Not Replacement His preferred analogy is "Iron Man, not Terminator." AI's greatest value comes from enhancing human expertise rather than replacing it. 5. Information Governance Has Become Strategic Data classification, content labeling, and governance are no longer compliance exercises. They are foundational requirements for successful AI implementation. 6. Responsible AI Requires Deliberate Design Organizations can shape AI behavior using governance frameworks, retrieval systems, constitutional rules, validation controls, and ongoing measurement. 7. Organizations Must Move Beyond Efficiency Simply using AI to do current work faster is not enough. Sustainable advantage will come from reinventing business models and leveraging unique organizational knowledge. 8. AI Governance Is Both Technical and Policy Driven Success requires balancing innovation with privacy, security, regulatory compliance, risk management, and human oversight. Bennett B. Borden | LinkedIn Guest Notes Bennett Borden Founder & CEO, Clarion AI Partners Bennett Borden is an attorney, data scientist, AI strategist, and former law firm partner who began his career at the CIA. He has focused on the intersection of law, technology, data governance, and artificial intelligence. Areas of Expertise Artificial Intelligence GovernanceAI Risk ManagementInformation GovernanceE-DiscoveryData AnalyticsAlgorithmic FairnessAI EthicsRegulatory Compliance

Key Takeaways 1. AI adoption should not be measured only by efficiency Legal teams often focus on time saved, headcount reduction, and cost containment. Marisa argues these are incomplete metrics. Organizations also need to assess AI’s effects on human well-being, discernment, decision-making, and cognitive capacity. 2. Humans are not fully “ready” for AI Marisa introduces the idea of human readiness — including cognitive, psychological, and social readiness. AI may be advancing faster than people’s ability to responsibly process and evaluate its outputs. 3. Cognitive overload leads to offloading judgment When users are overwhelmed by too much information, they unconsciously defer to the machine. In legal practice, that can mean diminished critical thinking, less discernment, and increased reliance on outputs that may be wrong but appear convincing. 4. AI bias isn’t only about race or gender Marisa highlights lesser-discussed cognitive biases such as automation bias, authority bias, and the privacy paradox. These biases shape how people interact with AI systems and can undermine judgment. 5. AI systems are designed for continuous engagement One of the episode’s most striking points: many AI interfaces are built to keep users engaged, much like social platforms or slot-machine mechanics. That raises serious questions about manipulation, dependency, and user autonomy. 6. Training is the answer — but not just tool training Organizations need more than prompt training. They need training in how AI affects thinking, relationships, judgment, and work habits. Human readiness requires practice, reflection, and institutional support. 7. Legal professionals have a unique role to play Lawyers are trained to parse complexity, evaluate facts, and question assumptions. Marisa suggests the legal profession is well-positioned to help establish stronger practices around AI accountability and safe use. 8. There is a serious client confidentiality risk in consumer AI tools Marisa raises a practical concern: clients and legal staff may unknowingly upload sensitive information into public AI tools. This creates risks around admissibility, confidentiality, and competence. 9. AI has significant environmental consequences The conversation also explores the strain AI places on the electrical grid, carbon emissions, and fresh water resources used by data centers — an underdiscussed but growing area of concern. 10. The goal is not fear — it’s discernment Marisa is not anti-AI. Her core message is that AI can be beneficial, but only if people learn how to use it without surrendering agency, judgment, or responsibility. Marisa Zalabak | LinkedIn Guest Notes Marisa Zalabak co-authored the IEEE 7010 standard which addresses recommended practices for assessing the impact of autonomous and intelligent systems on human well-being. She also chairs the IEEE AI Ethics Education Committee, works on standards related to emulated emotions in AI systems, leads the Planet Positive 2030 Initiative, and is the founder of Open Channel Culture. Her work focuses on the intersection of AI ethics, human development, education, cognition, and societal impact.

Guest Notes Derrick Logan leads development of innovative technology solutions tailored to meet intricate legal and regulatory challenges. By aligning advanced services with client objectives, he helps address immediate needs but also drive long-term success. Derrick’s focus on integrating forensics and AI capabilities has significantly enhanced offerings at Consilio in the New York City area. With experience spanning major legal technology and eDiscovery organizations, Derrick has helped shape managed services models, scalable legal data programs, and AI-driven solutions that support Fortune 500 clients facing complex legal and regulatory challenges. Derrick Logan | LinkedIn Key Takeaways 1. True managed services solve business problems, not just technology problems. Early success in eDiscovery came from moving beyond hosting and pricing conversations to building lifecycle-based, portfolio-wide programs. 2. Listening to the client remains the differentiator. Derrick emphasizes that legal tech providers succeed when they understand the actual problem a client is trying to solve, rather than forcing a prebuilt solution into every situation. 3. Builder mode must continue after success. Reaching “maintenance mode” is a sign of success, but organizations have to keep scanning the market, watching regulations, and anticipating the next client need. 4. Bespoke solutions can become scalable products. Many of the strongest legal tech products begin as client-specific solutions, then evolve through iteration, market demand, and structured product development. 5. AI in legal work requires security, governance, and adaptation. AI-centered programs are not just about using large language models. They require wrappers, controls, security layers, and thoughtful integration into legal workflows. 6. Human-in-the-loop should really mean human-at-the-center. AI can accelerate work, but human oversight remains essential for validation, judgment, defensibility, and dealing with hallucinations or incomplete outputs. 7. The billable hour may be increasingly challenged by AI-driven efficiency. As legal work becomes faster and more scalable through technology, Derrick sees the legal industry moving closer to value- and outcome-based delivery models. 8. Before adopting AI tools, organizations need governance and risk tolerance. Legal teams should define the problem to solve, create a strong governance model, vet tools for security and accuracy, and decide what level of risk and imperfection they can tolerate.

Key Takeaways AI is helping experts work faster, but it may create a training gap for the next generation.Legal teams need process and governance before they buy or deploy AI tools.Strong data management is essential to trustworthy AI outcomes.AI is beginning to reshape legal pricing, staffing, and service models.As technology gets stronger, human judgment, trust, and accountability matter even more.

Guest Notes Kim Wolfe has a background in operations and years of experience supporting legal organizations. She focuses on legal department maturity, innovation, contracting transformation, and organizational effectiveness. Kim Wolfe | LinkedIn Key Takeaways AI helps experienced professionals move faster—but judgment still matters. Kim highlights a growing concern that younger lawyers may not have enough foundational experience to spot AI mistakes.Adoption starts with comfort, not perfection. Before focusing on advanced governance, Kim is prioritizing getting lawyers to actually try the tools.Peer-to-peer learning can accelerate legal AI adoption. Her “lawyer-to-lawyer” approach pairs AI-comfortable attorneys with more hesitant peers.Banking adds regulatory pressure to AI decisions. In heavily regulated industries, legal teams must balance innovation, control, and compliance.AI can help make legal playbooks dynamic. Instead of relying on static, outdated documents, legal teams may be able to generate living guidance using AI and prior work product.Simple use cases matter. Kim’s example of using AI to tailor tone and audience in written communications is highly practical and relatable.The real opportunity is not reducing headcount. Kim argues that legal AI should be used to redeploy capacity and improve decision-making, not simply shrink teams.Better legal outcomes require connected data. The future lies in pulling together legal, operational, and business data so lawyers can provide better advice.

Guest Notes Tyson Ballard is the CEO and Founder of Stella Legal, a consulting firm focused on AI enablement, contract excellence, legal operations, spend management solutions, and e-discovery. A longtime legal industry entrepreneur, Tyson has built his career around identifying patterns, improving processes, and helping legal professionals rethink how work gets done. Tyson Ballard | LinkedIn Key Takeaways Curiosity is a competitive advantage. Tyson’s approach to legal innovation starts with pattern recognition, continuous improvement, and questioning assumptions.AI makes more data measurable—but measurement alone is not strategy. Just because legal teams can analyze more information doesn’t mean they’re asking the right questions.The “why” still matters. Legal teams often focus on tools, mapping, and adoption without first defining the business problem they are trying to solve.Big Law and in-house teams face different AI tensions. Law firms worry about business model disruption, while legal departments question why efficiency gains aren’t reducing outside counsel cost pressure.The next generation needs development, not just tools. Tyson sees responsibility in building younger professionals’ subject matter judgment alongside their technical fluency.AI may change tasks more than professions. He offers an optimistic view that legal work will evolve, but human judgment will remain central.Community matters in legal ops. Tyson highlights the value of in-person conversations and peer collaboration at CLOC beyond formal sessions.Driving change is often indirect. One of the most effective skills in consulting is guiding lawyers to arrive at the answer themselves.

Guest Notes Kevin Bielawski has more than 20 years of experience in the legal industry and a background in finance. He works directly with law firm partners on performance metrics, pricing, operational strategy, and technology adoption. He is also involved with CLOC’s legal department maturity assessment and planning efforts and discussed the rollout of the new Compass tool at the conference. Kevin Bielawski | Senior Director of Legal Operations | The Link Virtual Office | Husch Blackwell Key Takeaways Legal ops isn’t just for in-house teams. Law firms face many of the same adoption, efficiency, and business-process challenges as corporate legal departments.Trust is the currency of adoption. Successful rollout of legal tech depends on credibility with attorneys and careful selection of pilot groups.Word-of-mouth beats mandates. Kevin emphasizes piloting tools with influential users first to create internal demand.AI adoption requires marketing and change management. New tools don’t succeed just because they exist; professionals have to explain value in practical terms.Client expectations are changing law firm behavior. Clients increasingly expect efficiency, pricing transparency, and better business alignment.Efficiency creates a cultural tension. Even when AI saves time, firms still have to wrestle with billable-hour incentives and attorney workload expectations.CLOC’s Compass tool is designed to help legal departments measure maturity. Kevin explains how the platform supports conversations with legal leadership and the C-suite.

Guest Notes LaResa Young has spent nearly two decades at St. Jude, beginning as a contract management specialist and helping grow the legal team from four people to nearly 30. Her work focuses on building intentional legal operations functions, improving processes, supporting mission-critical legal work, and aligning operational decisions with organizational values. Key Takeaways Process has to come before technology. Buying tools without a clear process leads to wasted funds and failed adoption.Legal tech procurement should be intentional. LaResha emphasizes strong scoping documents, structured demos, and clear guidance for vendors.Highly regulated organizations need tailored solutions. Generic demos don’t work when contract types, workflows, and compliance requirements are highly specific.Vendor accountability matters. St. Jude tests whether vendors actually listen and can align to real organizational needs.Mission and values should shape legal ops decisions. LaResha frames procurement and implementation through the hospital’s core values, especially stewardship.Legal ops can be measured by impact, not just dollars. Faster contracting can directly affect care delivery, equipment access, and provider onboarding.AI should drive upskilling, not panic. Rather than assuming AI will eliminate entry-level work, LaResha argues organizations should retrain and elevate talent.Conference learning should be shared internally. She models how legal ops leaders can bring insights back to their teams in practical ways.

Guest Notes James Sherer is based in New York, where he co-leads the Emerging Technology Team within the Digital Assets and Data Management (DADM) Group at BakerHostetler. With a unique background that includes a J.D., an MBA, and serving as an in-house litigator for a Fortune 500 company, he provides commercially focused advice on the intersection of law, data, and advanced technology. James A. Sherer - BakerHostetler Key Takeaways Ethical duties still apply, even as AI changes fast. Lawyers remain responsible for anything produced under their license; AI does not displace duties of competence, diligence, candor, or confidentiality.Privilege and discoverability depend heavily on how the tool is used. Emerging case law shows that courts are not treating all AI-assisted work product the same, especially where confidential litigation material is entered into outside tools.Agentic AI will almost certainly become part of discovery. Prompts, chat histories, logs, model choices, impact assessments, and related governance records may all become relevant sources of evidence.Explainability and governance matter as much as output. If organizations want the benefits of AI systems acting on their behalf, they must have documentation, evaluation, maintenance, and defensibility. Hallucinations and misuse are a supervision and accountability problem as much as a technology problem.Vendor dependency is a major hidden risk. Organizations may deploy AI tools without having contractual rights to access the technical information they later need for audits, litigation, or regulatory scrutiny.Knowledge loss is becoming a governance problem. As systems are implemented and the people who built or managed them leave, organizations may lose the human context needed to explain why a system exists, how it works, or what data it touches.The profession is under real pressure to adopt AI quickly. Market demands to “do more with less” are pushing firms and companies to use AI at scale, often before governance, training, and controls are mature. Technical competence is a continuing obligation, not a one-time certification. Lawyers should approach AI with the same rigor used in litigation, discovery, and expert-driven matters.For future lawyers, the message is ownership plus curiosity. Sherer urges law students and young lawyers to remember that their license is their own, and that long-term success will depend on disciplined learning, visible value creation, and adaptability.