As artificial intelligence reshapes legal practice, boutique and mid-sized law firms have taken a measured approach to adoption, weighing whether specialized legal AI products offer enough value over increasingly capable general-purpose tools.

According to Clio’s 2025 Legal Trends for Solo and Small Law Firms, most solo and small-firm lawyers use AI in some capacity. However, only 8% of solo practitioners and 4% of small firms have adopted AI widely or universally, compared with 35% of large firms.

Among smaller firms, generic non-legal AI platforms such as ChatGPT are the most commonly used AI tools, with 57% of solo lawyers and 54% of small-firm lawyers reporting they use them. AI-powered legal research platforms followed closely behind.

Thus, for many, the debate has shifted from whether AI works to whether specialized software delivers enough additional value to justify the added expense.

Ernie Svenson, a legal technology consultant and former large-firm commercial litigator, said advances in technology have consistently lowered barriers for smaller firms.

“When I came out of law school, only large firms could afford Lexis,” Svenson said. “Each of these technologies that was previously affordable only by larger organizations increasingly became affordable by smaller firms.”

The same dynamic is now playing out with AI. Legal AI companies such as Harvey and Legora market themselves as purpose-built platforms that automate legal research, drafting, and document review while incorporating legal workflows and firm knowledge. However, they also rely on the same underlying frontier large language models that power general-purpose tools like ChatGPT and Claude.

Meanwhile, major AI developers are increasingly building legal capabilities directly into their products. Most recently, LLM developer Perplexity AI launched its own suite of legal technology integrations, OpenAI hired a former legal technology executive to lead its legal vertical, Anthropic introduced legal-focused integrations for Claude, and Microsoft launched AI features for legal drafting in Word.

The growing capabilities of general-purpose AI come with a markedly lower entry cost. ChatGPT Plus starts at $20 per month, while practice management software such as Clio EasyStart begins at $49 per user per month. By comparison, legal-specific AI platforms are typically sold as premium products. Thomson Reuters’ CoCounsel starts at about $784 per month for a solo attorney under an annual plan, while pricing for enterprise platforms such as Harvey and Legora is not publicly disclosed.

Svenson said rather than paying for specialized platforms, many small-firm lawyers are finding that general-purpose models can handle much of what they need and benefiting from learning how to use the underlying tools directly.

“In fact, you have a greater adoption capability just because of the inherent slow-moving nature of larger organizations,” Svenson said. “The advantage, at least theoretically and practically in many cases, is to the solo and very small firm.”

What differentiates legal-specific platforms such as Harvey from general-purpose tools is the infrastructure behind them. These systems are built to pull from curated legal data, case law, and a firm’s own documents to support workflows tailored to legal practice. They also typically include enterprise security features intended to ensure that confidential client information is not used to train shared models or accessed by other firms.

General-purpose AI tools, by contrast, are not designed specifically for legal workflows. They are trained on broad datasets across the open internet and deployed across multiple industries. While they can be adapted for legal tasks, they do not come pre-configured with legal databases, citation systems, or matter-specific workflows that legal platforms integrate into their design.

But Svenson said the underlying risk is often misunderstood. Because most modern legal software – whether specialized or general-purpose – is cloud-based, firms are already operating within systems that rely on third-party infrastructure. In that sense, he said, evaluating AI tools is less about confronting a new category of risk and more about applying established standards for cloud security, vendor due diligence, and data governance.

“Most of the threat of data being compromised is because it’s in the cloud somewhere,” Svenson said. “This is not a new problem.”

In practice, firms are already required to manage these risks under existing professional obligations around confidentiality and client data protection.

AI tools, like any other legal technology, are typically evaluated through existing compliance and procurement guidelines. Even as enterprise versions of general-purpose tools introduce stronger privacy controls, firms continue to vet them closely before allowing use in sensitive legal work.

“Every new tool that is to be used firmwide goes through a standard vetting process to ensure client and firm data is appropriately protected in accordance with firm policies,” said Kritika Bharadwaj, partner and AI committee chair at Day Pitney. “Just because it is an AI tool does not change the normal procurement processes within the firm.”

As a mid-sized law firm with over 300 attorneys across five states and the District of Columbia, Day Pitney has integrated both legal-specific and general-purpose AI tools into its operations. Bharadwaj said the firm uses AI for drafting assistance, synthesizing large volumes of information, due diligence reviews, and administrative tasks, all subject to independent attorney review.

In addition to confidentiality, Day Pitney also considered scale. Legal-specific platforms are often designed for firms managing large volumes of matters, documents, and attorneys. In that context, tools that can process and standardize information or coordinate work across teams can create efficiencies.

But many boutique and mid-sized firms operate on a different scale. With smaller teams and narrower practice areas, they may not need the same level of workflow automation. Instead, ease of use and flexibility often matter more.

“There are certain efficiencies at scale that come with using tools that are created for legal purposes and legal workflows, but at the same time, some of these tools may not fit every type of law firm, every type of practice, every size,” Bharadwaj said.

When evaluating new technology, usability is often the most important factor, Bharadwaj said. The question is whether products designed for large-scale legal operations actually solve a problem the firm has.

In some cases, it may make more sense to build customized workflows around general-purpose models instead of investing in specialized products.

“Firms that don’t have the ability to invest in all of these tools and try to figure it out will take that approach of testing multiple options before making them,” Bharadwaj said.

For smaller practices, that often means starting with tools that address immediate operational needs before expanding into more substantive legal applications.

Janice Dantes, founder and managing partner of Pinay Law, a three-attorney firm based in Chicago, Illinois, said her firm takes a similar approach.

“These tools are just an extension of paper, but it’s making it easier, so you find a tool that fits or enhances your process,” Dantes said. “You don’t fit the hand to the glove, you fit the glove to the hand.”

Today, her firm primarily uses AI for business operations, including marketing, client communication, and case management. To answer frequently asked client questions more efficiently, Pinay Law created hundreds of informational YouTube videos explaining different stages of a case, with AI used to help write scripts and generate voice recordings. The firm also developed a client-facing application that tracks individual cases.

For legal research and drafting, Pinay Law is in the process of evaluating the capabilities of Vincent, a legal-specific AI platform developed by vLex and now part of Clio.

“You have to produce deliverables that the client sees and knows that there’s progress being made on their case, and these tools can create amazing deliverables,” Dantes said.

As a result, more time has been spent on litigation strategy and client interaction rather than data collection and administrative work. But while AI has helped automate many routine tasks, Dantes emphasized that technology should be intended to support attorneys, not replace their judgment.

Whenever AI is used in substantive legal work, Dantes said its outputs are reviewed by attorneys and staff, cases are independently verified, and legal analysis remains the responsibility of the lawyer.

“You still have to be very careful about what the case law says, because you’re still a lawyer, you still have to use your brain to analyze the data,” Dantes said. “You just have so many more tools to enhance the service.”

With these more efficient workflows has come a shift in client expectations.

Bharadwaj said clients increasingly expect faster turnaround times as AI becomes more common in legal work. Clients also arrive with analyses generated by their own AI tools and ask lawyers to validate, revise, or challenge those conclusions.

Rather than viewing AI as a competitive advantage in itself, Bharadwaj said firms should focus on how they use the technology to better serve clients. As AI becomes more widely available, she said, lawyers will increasingly distinguish themselves through their expertise and the value they add beyond what AI can generate.

“I think I see this as more of an opportunity to find our niche and value-add,” Bharadwaj said.