BDO's Legal Tech Talk Podcast

Key Takeaways

1. Access to Legal Knowledge Remains an Access to Justice Issue

Damien argues that legal information has historically been locked behind expensive subscriptions and proprietary systems. While the law itself is public, access to the information needed to analyze, organize, and use that law often remains inaccessible to many individuals and organizations. He views open legal infrastructure as a critical component of expanding access to justice.

2. Taxonomies Create Interoperability

SALI and FOLIO are designed to provide common identifiers and shared definitions for legal concepts. By creating standardized legal vocabularies, organizations can connect documents, systems, data sources, and AI tools more effectively while reducing fragmentation across the legal ecosystem.

3. AI Is Most Powerful When Combined with Structured Data

Rather than relying exclusively on probabilistic large language models, Damien advocates pairing AI with structured legal knowledge, taxonomies, and knowledge graphs. He argues that the future of legal AI depends as much on organization and structure as it does on model performance.

4. Data Can Supplement Legal Judgment

The conversation explores whether legal outcomes can be quantified and analyzed at scale. Damien suggests that many decisions currently framed as professional judgment may actually be informed by patterns that can be measured, analyzed, and incorporated into legal decision-making processes.

5. Creativity Often Exists at the Edge of Statistical Probability

Drawing connections between music and law, Damien argues that creativity frequently emerges when someone departs from the most statistically likely outcome. The same principle applies to both memorable melodies and breakthrough legal arguments.

6. The Next Wave of Legal AI Involves Capturing Human Expertise

One of the episode's strongest insights is that future legal workflows may focus on converting human expertise into reusable artifacts that AI systems can reference, apply, and expand upon. According to Damien, much of legal AI's future may involve capturing expertise already residing in lawyers' heads.

7. Law May Become Increasingly Computable

Through structured legal concepts, knowledge graphs, and semantic relationships, legal analysis may become more predictable, measurable, and machine-readable. While human judgment remains important, Damien sees significant opportunities to make legal reasoning more computable than it is today.

Guest Notes

Damien Riehl | LinkedIn

Solutions Champion, Clio

Damien Riehl is an attorney, technologist, musician, and legal innovator whose career spans complex litigation, digital forensics, legal analytics, software development, legal AI, and access-to-justice initiatives. After serving in legal and technology leadership roles, including work with vLex and Docket Alarm, now part of Clio, Damien has become a leading advocate for open legal standards and structured legal knowledge.

He is also a creator of SALI and FOLIO-related initiatives and is widely recognized for the music project that algorithmically generated hundreds of billions of melodies and placed them into the public domain to address copyright disputes. Throughout the conversation, Damien shares perspectives on legal data, AI, interoperability, access to justice, legal analytics, and the future of computable law.

Key Takeaways

1. Billable Hours Were Designed for a Different Era

Traditional time-based billing solved important business problems, but AI is forcing firms and clients to reconsider whether hours remain the best proxy for value.

2. The Value of Legal Work Is Shifting

As AI reduces the cost of production work, firms are increasingly being valued for expertise, accountability, and the willingness to stand behind legal advice and work product.

3. Legal Value Should Be Measured from the Outcome Backward

The proper starting point for defining value is not the effort expended, but the business objective that created the legal need in the first place.

4. Metrics and KPIs Are Not the Same Thing

Metrics track activity, while KPIs should be directly connected to organizational objectives and critical success factors.

5. Law Firms Need to Move Beyond Measuring Activity

Utilization, realization, and profitability remain useful operational measures, but they do not necessarily indicate whether a client received meaningful value.

6. Alternative Fee Arrangements Require Better Measurement

AFAs depend on firms understanding profitability, predictability, and performance before work begins, not after matters close.

7. Understanding Client Context Creates More Value

As production work becomes more automated, understanding a client's business objectives becomes a larger differentiator for law firms

8. KPI Programs Must Align with Organizational Goals

Effective KPI design begins with objectives, flows through critical success factors, and only then leads to measurement.

9. AI Creates Capacity, Not Necessarily Value

The greatest opportunity may not be doing the same work faster, but using newly available time to provide higher-value strategic guidance.

10. Some Elements of Legal Value Remain Difficult to Measure

Relationships, trust, creativity, and human judgment continue to influence legal outcomes in ways that cannot easily be captured by dashboards or metrics.

Guest Notes

Mori Kabiri, CEO, InfiniGlobe

Mori Kabiri is the CEO of InfiniGlobe and a recognized leader in legal operations analytics, KPI development, and legal performance measurement. He is the author of both Legal Operations KPIs and Law Firm KPIs: The Professional's Handbook for Pricing, Productivity, and Profitability. Through his work with legal departments, law firms, and legal operations communities around the world, Mori helps organizations create measurement frameworks that connect legal work to business objectives and outcomes

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.

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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.

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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.

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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 Cycles
• AI 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 Governance
• AI Risk Management
• Information Governance
• E-Discovery
• Data Analytics
• Algorithmic Fairness
• AI Ethics
• Regulatory 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.