How Embedded AI Is Redefining Legal Practice for Mid-Market Firms
A Conversation with Assembly’s Daniel Farrar and Jim Garrett
Artificial intelligence is accelerating a profound shift in the legal services sector, particularly among mid-market law firms that have historically lagged in technology adoption. These firms — often caught between the resource-rich enterprises and the nimble boutiques — are now under pressure to modernize. Rising client expectations, increasing caseloads, and competitive demands for efficiency are forcing practices to re-examine how technology fits into daily operations.
While early digital transformation in legal was marked by fragmented point solutions and patchwork systems, the focus today is on platform-based approaches that unify data, workflows, and decision-making. Embedded AI is at the heart of this transition, promising to automate repetitive tasks, streamline case management, and support more informed strategic choices. The goal is not simply to add another layer of software but to make AI a seamless, ever-present element of the practice — as invisible as spell-check, yet as essential as billing.
To explore how this transition is unfolding, BizTechReports spoke with Daniel Farrar, CEO of Assembly Software, and Jim Garrett, CTO of Assembly Software. Their insights highlight how mid-market law firms can move beyond fragmented tools toward integrated platforms, and how AI is reshaping the economics, operations, and governance of legal practice.
STRATEGIC ASSESSMENTS
BTR: Daniel, how would you characterize the broader shift taking place in mid-market law firms as they confront new technology demands?
Farrar: Historically, many firms have operated with a patchwork of tools — spreadsheets, word processors, homegrown databases. That fragmentation has been a drag on efficiency and made it difficult to scale. What we’re seeing now is a move away from software as a collection of features toward platforms that can unify workflows, provide a single source of truth, and adapt to each firm’s way of practicing law. Neos, our cloud-based platform, was designed with this in mind — microservices, open integration, analytics, payments, and now embedded AI. It’s about giving firms the flexibility to shape technology around their operations rather than forcing them to conform to rigid systems.
BTR: Jim, from a strategic standpoint, how does AI fit into this evolution?
Garrett: At a strategic level, AI is becoming foundational infrastructure. It’s no longer about experimenting with shiny new apps. When AI is embedded into the platform, it disappears into the workflow — more like spell-check than a separate program. That’s the future of digital transformation in legal. The firms that embrace this approach are positioning themselves to handle more cases, respond faster to client needs, and elevate their decision-making without fundamentally changing their business model.
OPERATIONAL IMPERATIVES
BTR: Early experiments with AI in legal have produced both optimism and caution. How are mid-market firms approaching adoption today?
Garrett: Early pilots showed the risks of depending on large, public models that weren’t designed for legal use — the most infamous being “hallucinated” case citations. That experience shifted the conversation. Now, firms want embedded AI that runs inside their trusted systems and is constrained to their data. Adoption accelerates when AI doesn’t feel like another application to learn but rather an enhancement of the tasks staff already perform — summarizing documents, drafting fields, or querying a case. That’s why our beta program had a 60 percent adoption rate across 62 firms: the technology fit into existing workflows rather than disrupting them.
BTR: Daniel, what operational challenges do law firms face in this transition?
Farrar: The key operational challenge is governance. Firms need to know where their data lives, how it is being used, and that it won’t leak into public models. That’s why we built Neos on Azure microservices and firm-specific data lakes. It keeps everything contained. But governance doesn’t stop with the platform. At the end of the day, fact-checking and responsibility remain with the firm. The tools can provide efficiency and insight, but lawyers have to ensure accuracy — because reputation is on the line.
FINANCIAL IMPLICATIONS
BTR: Jim, what are the financial implications for firms adopting embedded AI?
Garrett: The biggest impact is capacity. By automating repetitive, low-variance tasks, firms can handle more cases with the same headcount. In plaintiff practices, where contingency fees drive revenue, that capacity translates directly into financial performance. We’ve seen efficiency gains equivalent to adding 2.5 full-time employees without increasing payroll. That changes the economics of the practice.
BTR: Daniel, how do you see pricing models evolving as AI adoption grows?
Farrar: We think the future is consumption-based. Right now, many firms buy bundled packages even if they only use a fraction of the features. With AI, we’ll see more transactional models where firms can choose specific capabilities — document summaries, field generation, predictive analytics — and pay based on usage. That’s particularly important for mid-market firms that need advanced tools but can’t justify enterprise-level contracts. It makes AI more accessible and levels the playing field.
TECHNOLOGY DEVELOPMENT
BTR: Daniel, what role does embedded AI play in shaping the technology roadmap for legal platforms?
Farrar: It’s central. In our first generation of Neos AI, we focused on time savings — document summarization, data extraction, and workflow automation. The next generation is about probability and predictability: helping firms assess which cases to accept, how long they might take, and what settlement ranges are likely. Ultimately, AI will be woven into every stage of the workflow. It will not be a sidecar — it will be the operating layer that makes the entire system more intelligent.
BTR: Jim, how do you distinguish between dedicated AI applications and embedded AI capabilities?
Garrett: Dedicated applications sit outside the workflow. You have to log in, move data over, run the task, and then bring the results back. That adds friction and raises security risks. Embedded AI lives inside the platform itself. It draws on the firm’s data in real time, within the same workflows people already use. That makes adoption smoother, reduces errors, and builds trust. Over time, lawyers won’t think of it as AI at all — it will just be part of how they practice.
Conclusion:
The legal sector is at an inflection point. Mid-market firms, once slow to adopt technology, are now embracing AI not as a bolt-on tool but as part of a broader platform strategy. The shift from fragmented systems to unified platforms is redefining how cases are managed, how risks are governed, and how firms compete in a tightening market.
AI will be most valuable when it is embedded in the very fabric of legal operations. The firms that treat technology as infrastructure — not as a collection of apps — will gain the most leverage, whether in expanding caseloads, improving financial performance, or enhancing client service. As the hype fades and adoption matures, AI in legal will increasingly resemble utilities like spell-check: ever-present, reliable, and indispensable.
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