BDO's Legal Tech Talk Podcast

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.

Debbie Reynolds | LinkedIn

Guest Notes

• Debbie has more than 20 years of experience in digital transformation and data privacy with a background in library science and information management, which shaped her view of data flows.
• Widely published and quoted in outlets such as Bloomberg Law, Law360, PBS, Wired, and The New York Times, Debbie translates complex privacy law into practical business guidance.
• She was appointed to the U.S. Department of Commerce’s Internet of Things (IoT) Advisory Board and has served in leadership roles with IEEE on cybersecurity and next generation connectivity systems.
• Host of the long running, award winning “The Data Diva Talks Privacy Podcast,” which reaches a global audience across 150+ countries.

Key Takeaways

1. To be successful and achieve desired results, AI innovation requires governance and best practices in data management and strategy. Duplication is a huge problem, and it makes training AI more expensive. Data curation is the foundation of AI, and many organizations are missing it. They should understand why data exists, what data should be kept, and how it should be used.

2. Data deletion and end-of-life data strategy are major weaknesses. Data systems are created to save data, not delete it. Organizations are full of data generators, and they need to empower data curators. Without clear retention and disposal practices, data lingers often in neglected systems, which increases exposure.

3. Companies need end-to-end data ownership. Organizations should maintain a bird’s-eye view of the full data lifecycle. Most privacy failures aren’t malicious. They’re operational. Many incidents stem from mistakes, oversight, or poor governance, not intentional misuse.

4. Purpose and context get lost—and that’s where privacy risk happens. A key risk is data collected for one purpose is later used for another (e.g., phone numbers collected for multifactor authentication later being used for marketing), which can trigger compliance issues.

5. Old, unused data is vulnerable to attackers. Even if stale data has little business value, it can be highly valuable to hackers, and forgotten infrastructure (“old server in the back room”) becomes an easy target.

Key Takeaways

• AI market fear is real, but adoption momentum remains strong. Despite volatility and uncertainty, firms are unlikely to stop investing in AI.
• Innovation is leveling the playing field. Smaller legal tech companies can move faster, reach market quickly with limited sales resources, and win customers by solving specific, high-value problems—especially where larger vendors have over-focused on large law and enterprise.
• Firms are pursuing outside capital to fund differentiation. A major driver is the need to invest in technology and talent to compete in an increasingly commoditized market, defined by growth, profitability, and improved client service.
• Management Services Organization (MSO) structures are trying to balance capital and ethics. The MSO model helps “wall off” the learned profession—protecting duties like loyalty to clients—while still enabling meaningful investment in tech, expansion, and acquisitions.
• MSOs resemble outsourcing—strategically. The model can let law firms focus on their strengths while externalized services handle operational burdens.
• Other countries offer a preview of what’s possible. In markets where rules were liberalized earlier, Scott notes there are likely 15–20 PE-backed or public-company law firms in the UK, and 10–20 in Australia.
• Ownership separation is the key structural safeguard. The practicing law entity is owned only by lawyers, while a separate services organization sits alongside it—designed so third parties do not own the legal practice itself.
• Access to justice remains an opportunity area. Newer offerings can target unmet needs (e.g., lower-cost options for straightforward legal matters) that have often been underserved

Guest Bio

Guest Notes

As a Co-CEO & Managing Director in New York, Scott brings over 25 years of M&A, capital markets and operating experience working with global organizations in the business information, technology, news distribution and B2B media industries. Scott has deep experience working with software, data and tech-enabled services businesses across the Legal, Compliance, Public Relations and Investor Relations verticals. Prior to joining JEGI LEONIS, Scott led Bloomberg’s legal market business, UBM Tech’s media, data & marketing services business and was general counsel and global co-head of M&A for UBM. Scott earned his BA from Williams College, and his JD degree from Fordham University School of Law. He is a member of the Board of Directors of Pro Bono Net, a nonprofit organization focused on using technology and innovation to close the access to justice gap.

Guest Notes

Stephen Dooley works with a diverse team of professionals that are responsible for supporting various data management needs, including the discovery process. He has over 25 years of experience advising attorneys, clients, and colleagues on a gamut of complex e-discovery and technical topics, including data governance, data privacy, and GenAI. He tries to remain a resource and thought leader to practitioners throughout the industry through active participation with ILTA.

Guest LinkedIn: Stephen Dooley

Key Takeaways

• Design for efficiency by eliminating duplication: Look for redundancies and reuse what you build across multiple workflows (e.g., apply lessons from budget summary generation to preservation and legal hold processes).
• Create a single, transparent source of truth for AI development: A shared program repository keeps everyone educated on what’s new, what people are trying, and—crucially—where things don’t work.
• Share more to become a stronger team: Competitive instincts can reduce knowledge sharing, but teaching others makes the whole team better.
• Keep it grounded in real experience—and keep it human: Improve from lived results rather than “we think it can do this,” and remember that tech is “just tech”—the most powerful outcomes come from human purpose and meaning.