Data First, Partner Better. Jennifer McIver on Legal Ops Benchmarks, AI Agents, and Pricing Reality Checks

Greg Lambert and Marlene Gebauer take The Geek in Review on the road to Atlanta for a conversation following the GDS AI Summit. Meeting with leaders from across industries offers a fresh perspective on familiar legal technology challenges: messy data, uncertain ownership, and the pressure to turn artificial intelligence experiments into measurable business improvements. Their shared takeaway offers some reassurance for legal innovation teams. Law firms face many of the same obstacles as their clients, and cross-industry conversations provide useful lessons for moving forward. Data quality anchors the discussion, with Greg offering a memorable description of law firm information: “Our data puddles are mud puddles.” Greg and Marlene examine structured data, unstructured data, and the less obvious problem of missing or unidentified information. Some organizational knowledge lives exclusively in the heads of experienced professionals, creating continuity risks when those people leave. The conversation also explores how priorities differ across industries, from protecting confidential information in legal services to safeguarding reputation when AI-generated content reaches an audience. Hidden workarounds emerge as another barrier to meaningful process improvement. Drawing on a conference discussion about nurses solving problems outside formal reporting channels, the hosts consider similar habits among legal assistants, paralegals, attorneys, and technology teams. When employees quietly resolve recurring problems, leadership receives an incomplete picture of daily operations. Over time, the workaround becomes the process. Introducing AI without understanding those informal practices risks embedding existing problems into a new system. Greg and Marlene also discuss the discipline required to run useful AI pilots. Their breakout group narrowed a broad data challenge into a more focused project around client data intake, ownership, and tracking. The exercise prompted questions about pilot duration, accountability, and measurement, including the risk of an extended experiment becoming a permanent process without a deliberate decision. Adoption numbers offer only part of the picture. Understanding whether people use a tool effectively requires clearer objectives, meaningful metrics, and responsibility for gathering evidence. The episode closes with a larger strategic question: Should AI adapt to existing business operations, or should organizations rethink their operations around AI’s capabilities? Greg questions the value of adding AI to established products while preserving the same underlying approach, while Marlene distinguishes enterprise-wide integration from solutions serving specific legal workflows. Alongside reflections on their own conference presentation and an evening at the Margaret Mitchell House, the hosts bring the conversation back to practical priorities for law firm leaders: define the problem, understand the process, and evaluate results before expanding an AI initiative.

This week we talk with Ross Guberman, founder and CEO of BriefCatch, and Heather Rodriguez, the company’s enterprise account executive. Ross built BriefCatch to put the lessons from his books and workshops in front of lawyers while they write. It started as a rules engine that flagged everything from typos to sentence flow. He admits he was never a coder, and says he would have looked for a technical co-founder if he had known the term existed. Today BriefCatch pairs that original engine with generative AI to review whole briefs, motions and judicial opinions for persuasiveness and consistency. The company has spent much of 2026 on checking how accurately briefs describe cases and statutes. That leads to RealityCheck, BriefCatch’s citation verification tool. Heather explains that it checks whether the cited opinion supports the proposition, pulls up the relevant language and flags potential problems. An experienced lawyer can disagree with its reading, and the lawyer still owns the argument. Ross adds that running RealityCheck on briefs from the 1990s through the 2010s turns up plenty of case law errors, so the problem predates generative AI. The talk then turns to how alike AI prose sounds, and Marlene warns she may lose her mind if she reads “it hits differently” one more time. Heather describes how BriefCatch offers choices the attorney can accept or reject. Ross gives the example of lawyers who use “indicate” for everything; the tool suggests more precise verbs such as specify, convey or reflect. On Anthropic’s plan to watermark text written by Claude, Ross praises the accuracy of the AI detector Pangram. He hopes the profession will start asking a better question about AI-assisted work: was it a good brief?

Law firms and legal departments face pressure to adopt AI, yet buying another tool does not answer the hardest question: Which problem needs solving? Dr. Maryam Salehijam, founder and CEO of Andarzi, joins Greg Lambert and Marlene Gebauer to discuss how legal teams choose between existing systems, new vendors, and tools built for their own workflows. Her starting point is to observe the work, identify a useful outcome, and then decide what technology belongs in the process. Salehijam describes legal teams paying for AI platforms while struggling to get consistent use from them. She urges organizations to examine tools they already own and ask vendors for more training before adding another subscription. A legal department’s use of ServiceNow for intake provides one example. The conversation also tackles shifting model capabilities, consumption pricing, and the cost of assigning an expensive model to a routine task. What counts as a return on legal AI investment? Time and money matter, but Salehijam argues for measuring employee satisfaction as well. Repetitive contract review and document work consume attention lawyers would prefer to spend on judgment, business problems, and client relationships. She describes using anonymous feedback before and after a workflow change to learn whether people’s work has improved, while Greg raises the tension between hours saved and the billable hour. The discussion turns to local AI models, data security, and the growing interest in AI-native law firms. Salehijam argues for decisions based on the task and the client’s requirements, including a direct conversation with clients about how their information should be handled. Greg presses her on whether smaller firms and large firms face different constraints. Marlene asks whether the emerging “legal engineer” role will endure, prompting a pointed exchange about technical skill, credentials, and what lawyers need to learn. For teams putting AI into practice, Salehijam favors hands-on learning with peers, repeated use, and work product over a quick certificate. She also warns leaders to account for the time and effort a new workflow demands before promising immediate gains. In the closing questions, she reflects on a legal AI market with fewer new entrants than she expected and predicts more law firms and legal departments will ask whether owning a workflow serves them better than renewing another set of software seats. LINKS Andarzi

Paul Lee, co-founder and CEO of Patlytics, joins Greg Lambert to explain how an AI platform built specifically for intellectual property work is changing the patent lifecycle. Patlytics supports workflows spanning patent drafting, prior art analysis, office action responses, portfolio management, litigation readiness, and claim-chart preparation. Lee reports that the company now works with roughly 55 percent of the Am Law 100 and hundreds of corporations across technology, biotechnology, pharmaceuticals, and other patent-intensive industries. Lee traces Patlytics’ origins to his experience as a venture capitalist and more than 100 conversations with patent attorneys. Those interviews exposed a practice filled with expensive, labor-intensive processes, from drafting detailed patent specifications to constructing claim charts for litigation. His interest also grew from the Apple and Samsung patent battles, the IP expenses faced by venture-backed companies, and conversations with Patlytics co-founder Arthur Jen and former Latham & Watkins patent litigator Bob Steinberg. The conversation turns to Patlytics’ work involving USPTO patent examiners and the broader effect of placing AI on both sides of the examination process. While confidentiality limits the details Lee discusses, he identifies quality and the examination backlog as two areas where specialized technology offers meaningful assistance. He also contrasts Patlytics with broad legal AI platforms such as Harvey and Legora, arguing that patent professionals need tools designed for the precision, technical detail, and specialized workflows of IP practice. Human judgment stays central to Lee’s vision. Patent attorneys still own the work product, approve key decisions, and remain responsible when an AI-generated analysis falls short. At the same time, client expectations continue to rise. Clients want faster work, higher quality, and lower costs, while law firms need sustainable margins. Lee sees flat-fee arrangements and more predictable workflows as one route toward sharing the “AI dividend” between clients and their outside counsel. In-house teams also gain more capacity for infringement analysis, patent-portfolio reviews during M&A, cross-licensing strategy, and litigation preparation. Looking ahead, Lee describes a striking change in attitude among patent professionals, from widespread skepticism a year ago to broad optimism today. His crystal-ball concern is less about whether lawyers will adopt AI and more about whether its economics will hold together. As free experimentation gives way to consumption-based pricing, firms will need to measure the value of each workflow and avoid spending $50,000 in AI costs on a $5,000 matter. Token maxing had its moment. ROI gets the next meeting invitation. Listen on mobile platforms: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠ [Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.] Email: [email protected] Music: Jerry David DeCicca

What happens when you separate elite legal talent from the traditional law firm structure? This week on The Geek in Review, we talk with Manuel Deó, co-founder and co-CEO of Ambar Partners, about a model designed around senior independent lawyers, flexible capacity, enterprise technology, and client choice. Deó explains why he and co-founder Rosa Espín did not set out to replace Big Law, but instead to address a gap between permanent in-house hiring and traditional outside counsel. At the center of Ambar’s model is a simple idea: legal demand comes in different shapes, and the delivery model should match the problem. Deó describes how Ambar gives lawyers control over the clients, projects, fees, and schedules they take on, while giving clients greater visibility into cost and the individual lawyers doing the work. He also discusses Ambar’s recent Chambers recognition and argues that the term “alternative” is starting to lose some of its usefulness as clients grow more comfortable assembling legal services from a wider range of providers. Deó walks through what Ambar calls its legal operating system, built around belonging, business, backbone, and badge. The model combines a professional community with business development, compliance, contracting, billing, technology, and institutional credibility for independent lawyers and specialist boutiques. Ambar’s public materials describe a shared technology environment that includes tools such as Harvey, Microsoft Copilot, Legora, and other legal technology products. The goal, according to Deó, is to give independent lawyers access to the infrastructure associated with a large firm without requiring them to give up professional independence. The conversation also turns to AI, knowledge, and professional judgment. Deó argues that legal knowledge is becoming more abundant while judgment grows more valuable. He describes Ambar’s work on “expert twins,” where approved knowledge, prior work, playbooks, and experience associated with an individual lawyer form a trusted layer for AI-assisted work. That emphasis on institutional knowledge and permissions echoes a broader trend across legal AI, where vendors are increasingly focused on connecting AI systems to trusted internal work product and organizational context. Finally, Deó offers a broader view of where legal delivery is heading. He sees legal departments assembling teams dynamically from in-house lawyers, traditional firms, independent specialists, boutiques, managed services, and AI agents based on the needs of a particular matter. Instead of asking which firm to hire, clients increasingly have reason to ask what combination of people, technology, expertise, and risk structure best fits the work. For Deó, the future belongs less to a single dominant delivery model and more to legal departments acting as orchestrators of capability. Listen on mobile platforms: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ | ⁠Substack⁠ [Special Thanks to ⁠⁠Legal Technology Hub⁠⁠ for their sponsoring this episode.] Email: [email protected] Music: Jerry David DeCicca LINKS Ambar Partners Ambar Community Ambar Hauss Membership Plans Dr. No Newsletter