Our 2026 report calls it the “efficiency paradox,” and most solo practitioners and small firms are living with it. Ask about AI itself and you’ll hear that it’s improved the quality of the work, cut down the tedious parts of the day, and helped them get back to clients faster. Ask about revenue, and the answer is different. Only 32% of solos and 31% of small firms have grown revenue since adopting AI, compared to 39% of mid-market firms and 59% of enterprise firms. Another 24% of solos and 23% of small firms say AI hasn’t changed their revenue at all.

What should law firms do with the time AI saves?

Billing properly for the hour AI gave back only helps if that hour goes somewhere useful. Unfortunately, at most small practices, it doesn’t. The firms growing with AI have something most others don’t—a plan for that saved hour. They’ve decided in advance where the time goes, and they’ve put it on the calendar before something else can claim it. At a solo or small firm, the activities that pay back fastest are usually the easiest to push:

• Intake that moves prospective clients to a consultation automatically. A prospective client fills out a form, gets a confirmation, and lands on your calendar. No phone tag, no manual follow-up, no leads going cold because someone forgot to circle back.
• One concrete step per matter, every week. Focus on something the client can point to as proof the matter is moving, whether it’s a filed motion, a sent demand letter, or a status email with a specific next date. A surprising number of fee disputes and bar complaints start with a client who went weeks without hearing anything and decided that nothing was happening.
  
• A weekly review of unbilled time. Spend half an hour going back through last week’s calendar, sent folder, and call log, and capture the quick client emails, phone calls, and document reviews that never made it into your time entries. Most lawyers find more billable time than they expect.
  
• Scheduled intake calls. Keep two or three 15-minute slots on the calendar every week, held open specifically for new-client conversations. A thin pipeline is far more often a prospect hitting voicemail than it is a marketing problem.
  
• Relationship and referral work. Consider scheduling a referral lunch with a lawyer in an adjacent practice area, answering a common client question in a LinkedIn group or local bar forum each month, or doing an annual pass through your firm’s website to update the bio, the practice areas, and the case results.

These aren’t new ideas, which is exactly the point. They’re the work that gets skipped because nothing on the calendar is protecting the time for it. When the saved hour lives in a standing block, the time gain finally becomes something you can point to at the end of the month.

This is also where the pricing question comes back into the picture. If you’ve moved your predictable work onto flat fees but the saved hour just disappears into inbox triage, all you’ve done is make yourself faster at the same volume of work. How to increase law firm revenue with AI comes down to changing what you charge and changing what you do with the time. The two have to move together for either one to matter.

The “no time” barrier

When roughly a third of solo and small firms say that lack of time is what’s keeping them from doing more with AI, they’re likely being honest. Running a solo or small practice can feel relentless. But it’s worth looking at where the hours are actually being spent, because the question of how solo lawyers can save time with technology usually has a different answer than “find more hours.” More often, it’s a question of fragmentation.

Picture a typical week at a solo or small law firm. Case management lives in one system, billing in another, and the calendar somewhere else. Intake comes through a form that doesn’t talk to either. Documents sit in a folder you keep meaning to organize. Email is open in two tabs. A generic AI window waits in another, ready to be rebriefed on a matter it has no context on. The week disappears into the seams between those tools, and by Friday it feels like the problem is that you needed more hours, when the actual problem is what you spent the existing ones on.

The cost is measurable. A Harvard Business Review study of workers across Fortune 500 companies found they toggled between applications around 1,200 times a day and spent nearly four hours a week—roughly five working weeks a year—just reorienting themselves after each switch. For a solo lawyer toggling between multiple systems all day, that’s a meaningful chunk of billable capacity.

That same fragmentation creates a second problem. The 2026 Legal Trends for Solo and Small Law Firms Report found that 47% of solos and 48% of small firms use consumer-grade AI tools like ChatGPT or Microsoft Copilot. Yet 57% of solos and 55% of small firms have no written AI policy governing how those tools get used. That’s how confidential client information may end up in a public AI window, and from there into model training data. The damage can often show up later, as confidentiality breaches, hallucinated case citations in filings, and the sanctions, bar complaints, and malpractice exposure that follow them.

Generic tools vs. a platform built for legal work

When AI starts to feel like it’s hitting a ceiling, the instinct is almost always to buy another tool to layer on top of the case management system, the billing app, and the document storage you already pay for. Each new tool may solve a real problem on its own, but it doesn’t know about any of the others, and the client information they all rely on ends up scattered across multiple places at once. The only thing actually holding the picture together is you.

The most effective AI tools for small law firms and AI tools for solo law firms tend to share one thing: They live inside the same platform as the rest of the practice. Intake feeds the matter file. The matter file feeds your billing entries. The AI sits inside that system instead of next to it, so it already knows who the client is and what the matter is about before you type a single prompt.

A few things change once that’s the setup. You log in once, not several times. You stop re-explaining matters to the AI, because the file is right there. Client data stays in one system, under one set of security and privacy terms written for law firms. New features build on the data you’ve already entered rather than asking you to set it up again somewhere new. Training takes less time. And if a regulator or your malpractice carrier ever asks for a record of what was done, it’s already there.

This is also where the revenue gap from earlier starts to close. The hour AI saves stops getting eaten up in the gaps between tools and goes back to billable work. If you’ve been asking how solo lawyers can use AI to grow a practice rather than just speed one up, that’s one of the most direct answers.

Is AI fueling solo and small firm growth, or slowing it down?

Clio’s new research digs into why working faster doesn’t always mean earning more, and what growing firms are doing differently. Get the full insights in the 2026 Legal Trends for Solo and Small Law Firms report.

Read the report

Start small: One task, one system

The simplest way to figure out how to save time with AI at a small law firm is to pick one task you do every week and move it inside the system that holds your client information. The point is to feel the difference based on a small piece of work you understand well, and then decide for yourself whether the same logic is worth applying to everything else.

Pick a task that fits two criteria. First, you do it at least three times a week—frequent enough that small inefficiencies are adding up. Second, the AI work is currently happening outside the system that holds the matter information. Intake summaries, client memos, and drafts of routine letters are the most common starting points.

What that looks like in your practice depends on the work:

• Personal injury: Begin with intake summaries. A new-client conversation covers a lot of ground, and before anything else, you need to know whether the person actually has a case and whether it makes sense for your firm to take it. AI pulls the relevant facts into a structured summary, so you’re making that call with everything in front of you, not from memory. Once that’s working, medical treatment timelines from records are a natural next step for building the factual foundation your demand letter will rely on.
  
• Family law: Tackle financial disclosure summaries first. Each one takes hours done manually, the inputs are predictable, and the output is something you’ll reuse throughout the matter. Affidavit outlines are a strong second.
  
• Estate planning: AI generates a will document template, then builds a client questionnaire from it. The client fills out the questionnaire, and their answers populate the template automatically. The document is ready without anyone on your team touching it between step one and the final review.
• Immigration and criminal defense: Focus on client histories. Both practices come down to how well you’ve documented the underlying facts, and details like listing every address from the past ten years. AI keeps those important timelines accurate, searchable, and easy to update as the case develops.

If it works on one task, the same setup tends to work on the next one, and the migration of your practice happens piece by piece rather than as a single big overhaul. By the time you’ve moved four or five recurring tasks across, you’re no longer toggling between tools for most of the work that fills a normal week, and the calendar starts to look different in a way you can feel.

Get more out of AI for your practice

The firms growing revenue from AI tend to share three habits. They’ve moved predictable work onto flat fees, so getting faster shows up as margin. They’ve given the saved hour a standing place to go, so efficiency turns into follow-ups, intake calls, and the relationship work that fills the pipeline. And they’ve stopped running their practice across five or six tools that don’t know about each other, so the time AI gives back is actually available to spend.

Solo and small firm lawyers don’t need more hours in the day. They need fewer places for those hours to leak through. That’s how small law firms can do more with less. The gains come from closing the gaps between the tools you already pay for, not from working harder. The Legal Trends Report for Solo and Small Law Firms goes deeper into who’s growing, what’s working, and where the biggest gains are showing up at your size of practice. And if you want to see what it looks like to run your intake, billing, matter management, and AI from one place, that’s exactly what Clio is built for.

Berkeley didn’t ban AI. As a matter of policy, it made the use of AI prohibited as a default rule for almost every part of a student’s substantive work. Here’s the rule itself:

“The use of AI is prohibited for aid in conceptualizing, outlining, drafting, revising, translating, or editing any work submitted for credit. AI use is prohibited for any use for any purpose in any exam situation. Students may not upload course materials—including assignments, readings, slides, class recordings, or other class content—into generative AI systems. AI can be used for research on papers ONLY for the limited purpose of identifying sources, such as cases, statutes, or secondary sources. Students are responsible for the accuracy of their research and all other aspects of their submitted work. Citations to sources that do not exist will raise a presumption of prohibited AI use.”

The categories of prohibited use listed in the policy include brainstorming, organizational structure, summarizing, identifying repetitive passages, revising or polishing a paper, generating an exam outline, and asking AI to translate a paper originally written in another language.

This is not a blanket ban, and law professors may include AI in the curriculum if they choose. And even where they don’t, students retain a narrow allowance for research.

Instructors can deviate from the default rule in writing, with appropriate notice, and can require students to disclose any authorized AI use. So professors who want to teach AI as part of their courses can opt in.

The student research exception is narrower. AI can be used for research on papers “only for the limited purpose of identifying sources, such as cases, statutes, or secondary sources.” Students remain responsible for the accuracy of the research and all other aspects of their submitted work. And citations to sources that do not exist will raise a presumption of prohibited AI use.

What the policy gets right

A few things are worth defending in Berkeley’s policy, even if you don’t agree with where it draws the line.

It’s a clear rule. Law schools across the country have been wrestling with what to tell students about AI use, and most of them haven’t said much. This leaves students wondering whether or under what circumstances AI use is permitted, and whether other students are secretly juicing their work with artificial intelligence. Berkeley has made the default explicit. That clarity is itself a service to students, even if some students would prefer a different default.

It puts responsibility for citation accuracy on the student. The policy says students are responsible for everything they cite, and that citations to nonexistent sources will be presumed to be AI-generated. That’s a good norm to build into law students. The AI hallucination problem in court filings is real enough that it’s worth teaching at the law school level.

It requires disclosure. Where AI use is authorized, students have to disclose it. That’s a good, instructive policy, and the direction law schools should be heading.

It’s a floor, not a ceiling. Professors who want to teach AI can opt in. Berkeley isn’t pretending that AI doesn’t exist; it’s setting a strict default and pushing the affirmative decision to use AI down to the course level. That’s a structural choice that some professors will appreciate, especially those who already restrict laptops, Wi-Fi, or exam software to preserve specific pedagogical experiences.

Where the policy goes too far

The policy is also too broad in places, in ways that work against its own goals.

Enforcement is the most obvious issue. The rule against jaywalking in New York City was a fine rule that got broken constantly with no real consequences. Berkeley’s policy faces the same problem. Any student who decides to be a “secret cyborg,” to use AI quietly and never disclose it, is going to be hard to catch. The detection tools are unreliable, and the most sophisticated AI use leaves no trace. A blanket prohibition that can’t be enforced creates a two-tier system: students who follow the rules and students who don’t, and the rule-followers may end up at a disadvantage.

The chilling effect on curiosity is the bigger issue. Berkeley’s list of prohibited uses includes brainstorming, summarizing, and identifying repetitive passages. These are exactly the kinds of low-stakes uses that help students explore unfamiliar material, find their way into a topic, and build intuition. A student who would otherwise ask Claude or ChatGPT to summarize a hard case before reading it, or to brainstorm angles on a paper topic, will now think twice. Some of those uses might have helped them learn faster. Some of them might have surfaced ideas they wouldn’t have found on their own.

There’s a serendipity argument here too. Law librarians talk about the value of perusing the stacks and finding books you didn’t know existed. AI is a different kind of serendipity engine. Ask it about a doctrine and it might point you to a concept or a case you’d never have encountered. Cutting off that mode of discovery for first-year students assumes that we know exactly how learning happens, and we don’t.

Research on AI and legal reasoning cuts the other way

Recent research makes that point empirically rather than philosophically. Professor Dan Schwarcz at the University of Minnesota recently ran a study with collaborators at the University of Michigan testing the hypothesis that AI hurts legal reasoning. Two groups of law students were given cases, statutes, and regulations. Group one had less AI access; group two had more. Both groups went through four stages of work.

The hypothesis going in was that the AI-assisted group would do the early work faster but worse at the harder analytical work, and that once the AI was taken away, the no-AI group would outperform the AI group because they’d built the mental models themselves.

That’s not what happened.

• Stage one (synthesizing cases, statutes, and regulations): The AI group did the work faster and better, as expected.
• Stage two (multiple-choice questions about the law, with AI removed from the AI group): The two groups did equally well. The AI group didn’t lose any ground from having used the tool.
• Stage three (applying client facts to the law, again with AI removed from the AI group): The AI group actually did better than the no-AI group. The researchers’ hypothesis was wrong in the opposite direction.
• Stage four (both groups given AI): The AI had mixed results, helping weak writers but hurting strong writers.

Worth noting: The AI tended to raise the floor for weaker writers more than it raised the ceiling for stronger writers. Some already-strong writers actually got worse results when they used AI, because they appeared to accept lower-quality output than they would have produced on their own. But the overall finding is striking: AI use during early stages of learning the law appears to strengthen legal reasoning, even after the AI is removed.

The hypothesis on why is interesting. Getting the “CliffsNotes” version right away, without going down rabbit holes of misunderstanding, may help students build a better mental model of the doctrine in the first place. Rather than replacing thinking, AI might accelerate the foundation that makes deeper thinking possible.

This study is an empirical study, and the researchers’ starting hypothesis turned out to be wrong. That’s harder to dismiss than a thought experiment. It also cuts directly against Berkeley Law’s policy.

The legal research carve-out is backwards

One specific piece of the policy deserves its own scrutiny. That’s the policy’s carve-out for using AI in legal research.

Berkeley’s policy says AI can be used “for the limited purpose of identifying sources, such as cases, statutes, or secondary sources.” This is the one thing that generative AI foundation models are worst at. ChatGPT, Claude, and Gemini hallucinate cases more than any other category of legal output. The 1,400+ documented cases worldwide of AI-generated errors making it into court filings are overwhelmingly bad case citations, not bad brainstorming.

The carve-out doesn’t distinguish between general-purpose chatbots and purpose-built legal AI tools with hyperlinked citations to real authority. A student could comply with the policy by using ChatGPT to “identify sources” for a paper, and walk straight into a citation to a case that doesn’t exist. A different student, using a legal AI tool grounded in real case law, would get verifiable citations every time.

If UC Berkeley had drawn a line between general-purpose chatbots and verified legal AI, the carve-out would make sense. Instead, the policy permits exactly the kind of AI use most likely to produce errors and prohibits the kinds of use (brainstorming, summarizing) where errors are lowest-stakes.

Beyond Berkeley Law’s AI policy: How should law schools teach AI?

Policies like Berkeley’s are answers to a question most law schools haven’t been asked to articulate out loud yet. What’s the right approach to AI in legal education, and how should law schools train lawyers to use AI well?

The calculator analogy is useful here. Math students learn arithmetic before they’re given calculators, so that when they later use calculators, they can recognize when an output is wrong. The discipline of mathematics expanded in the calculator era. The same is true of accounting and spreadsheets. The power tools didn’t replace the thinking. Instead, they raised the ceiling on what was possible.

The same logic probably applies to AI in law. The best AI users in legal practice tend to be lawyers with strong doctrinal foundations. They recognize when an output is missing an exception, when the analysis lacks depth, when the tool cites only one case but misses the seminal case. They treat AI output as a first draft. That critical judgment is what law schools are trying to teach, and judgment is hard to build if students are either (a) never exposed to AI or (b) using AI as a substitute for the underlying analysis.

The open question is how long the doctrinal training needs to last before students are turned loose with AI. Some professors will argue to ban AI for all three years. Some will argue a single semester. The most favorable policy is probably in the middle, with significant variation by subject area and by students’ planned post-graduation work. 

There’s also a deeper change happening in the work itself. Many of the tasks that junior associates used to do—research, first drafts, due diligence, document review—are increasingly being done by machines. Those tasks were how associates built the tacit doctrinal expertise that makes great lawyers. If the tasks go away, the training opportunities go with them. Law schools are choosing more than whether to allow AI. They’re choosing how to develop tacit expertise when the traditional pipeline is changing underneath them.

Why this is really an assessment problem

Strip away the AI debate, and Berkeley’s policy is really about something else entirely: how to grade students fairly.

Law schools across the country, like all of higher education, are wrestling with how to fairly assess students in a world where some are using AI and some aren’t. How do you grade a paper written manually with grammatical errors against a paper polished by Gen AI with no errors? How do you cold-call a class when half the students might be typing the question into ChatGPT and reading back the answer in real time? How do you give a take-home exam that some students are completing themselves and others are completing with significant AI assistance?

Many law schools are getting rid of papers. Others are moving back to handwritten exams. Some are abandoning take-home assignments entirely. The assessment infrastructure that supported a half-century of legal education is being rebuilt in a two-year period.

It’s a little like the recent enhanced games in athletics, where some athletes openly used performance-enhancing drugs and competed against others who didn’t. Some of the unenhanced athletes still won. But the comparison highlights how hard fair assessment becomes when participants have radically different toolkits.

Berkeley’s policy is, in part, an attempt to solve that assessment problem by removing the variable. If AI use is uniformly prohibited, the assessment can be uniform too. That’s an understandable goal, even if the means are debatable.

What UC Berkeley Law’s AI policy means for law schools

Berkeley Law’s AI policy isn’t the final word on any of this. It’s an early move from a renowned law school, and it deserves to be evaluated for what it is rather than dismissed as a refusal to engage with AI in legal education. A few things to take away from it.

• Clarity is a virtue. A clear default rule, paired with an opt-in mechanism for instructors who want to teach AI, is more useful than the ambiguous silence at many other law schools.
• Citation responsibility is the right norm. Holding students responsible for the accuracy of every cited source, with a presumption that fake citations are AI-generated, is going to be standard practice in the profession. Teaching it now is the right call.
• The empirical evidence complicates the case. The Schwarcz study suggests that AI use during early-stage learning may strengthen legal reasoning, not weaken it. Future policies should account for that.
• The legal research carve-out is backwards. General-purpose chatbots are the worst at the exact task Berkeley permits. A better-drawn policy would distinguish between foundation models and purpose-built legal AI tools with hyperlinked, verifiable sources.
• The real question is assessment. Behind every law school AI policy is a deeper question about how to fairly evaluate students in a world where some are AI-augmented and some aren’t. Berkeley’s policy is one answer. There will be many others.

The competitive picture in legal AI keeps shifting, and law school policies are going to keep shifting with it. Berkeley made an early, strong move. It may or may not look right in five years. What’s certain is that the assessment problem and the tacit-expertise problem aren’t going away, and the schools that figure out how to teach AI literacy and rigorous doctrinal thinking at the same time are going to produce the lawyers who do best in the profession that’s emerging.

Used well, AI raises the ceiling on what law students can learn. Used carelessly, AI short-circuits the very expertise that makes lawyers most able to supervise AI use effectively in practice. The work ahead for AI in legal education is to strike the right balance, advancing traditional doctrinal coursework, assessing students fairly, and preparing them to practice in a changed profession.

An MSO law firm consists of  a split-entity structure designed to work around a state’s RPC Rule 5.4’s prohibition on nonlawyer ownership of law firms. In the typical law firm MSO structure, the attorneys retain full ownership of the legal practice: the entity that holds client engagements, carries malpractice coverage, and maintains exclusive authority over legal decisions. A separate MSO entity, typically backed by private equity, acquires the firm’s non-legal operating infrastructure: technology systems, office leases, marketing, billing and collections, finance, human resources, and administrative staff.

The two entities are bound by a long-term management services agreement (MSA), often running 20 to 30 years with renewal provisions, under which the firm pays the MSO a management fee in exchange for those services. The fee structure is calibrated to avoid prohibited revenue or profit sharing—typically fixed, cost-plus, or benchmarked to arm’s-length market rates.

On paper, the lawyers stay in control of law, and the MSO runs the business. In practice, the line between those two functions is harder to maintain than any MSA suggests, a point examined in detail below.

The real appeal of the legal MSO model: capital and focus

The reason most firms consider the MSO law firm model comes down to two things: access to capital and reclaiming time for legal work.

Getting access to growth capital when traditional financing has closed

Law firms have always faced constraints in accessing outside capital. Rule 5.4 forecloses traditional equity investment. As a result, most firms depend on partner capital contributions, retained earnings, and business loans. This financing model works for firms content with organic growth but falls short for those seeking to expand into new practice areas, open additional offices, or invest aggressively in AI-powered service delivery.

Business loans were supposed to fill that gap. But 2026 has made that path materially harder. Three sweeping changes to the U.S. Small Business Administration’s lending rules took effect March 1, 2026, and they collectively represent the most significant tightening of SBA financing in years.

Citizenship requirements that bar many attorney-owners. Under SBA Policy Notice 5000-876441, 100% U.S. citizen ownership is now required across all SBA lending programs. Even 1% ownership by a green card holder disqualifies the entire business. The rule applies to direct and indirect ownership.  The SBA will trace ownership through cap tables, holding companies, and trusts. For firms with immigrant partners, international founders, or complex ownership structures, SBA financing is now off the table entirely. This change may also limit who may own the newly-formed MSO, the whole purpose for pursuing an SBA loan. 

The end of fast-track loan approvals. The FICO Small Business Scoring Service score—the automated tool that allowed smaller SBA loans to move through a streamlined approval process—was discontinued effective March 1, 2026. Every SBA 7(a) Small Loan now requires full manual credit analysis, including documented debt service coverage ratios of at least 1.10:1, two months of commercial bank statements, and a written narrative addressing why the applicant cannot obtain financing elsewhere. What was once a predictable, fast-track process for loans under $350,000 is now a full underwriting exercise.

Collateral requirements that change the calculus for every loan. Collateral is now required for all SBA loans exceeding $50,000, down from the previous $500,000 threshold. For many law firms, the lack of corporate assets means that lawyers will be required to use personal assets as collateral. These loans may now put personal assets, like homes, at risk if the business plan fails. 

For startup and acquisition loans, borrowers must also inject a minimum of 10% equity, meaning zero-down acquisition financing through the SBA is no longer available. For managing partners who had assumed SBA financing would support a strategic acquisition or office expansion, these changes are a material constraint.

MSO deals are filling a capital vacuum that changes to traditional financing have created, or, for many attorneys, have now permanently closed.

Redirecting attorney time toward billable work

Beyond capital, many attorneys are drawn to MSO arrangements for a simpler reason: running a law firm is not the same as practicing law, and the administrative burden of the former consistently erodes capacity for the latter.

Managing a firm means overseeing HR decisions, vendor contracts, IT infrastructure, marketing budgets, accounts receivable, regulatory compliance, and dozens of other functions that have nothing to do with client service. Clio’s Legal Trends Report found that these administrative tasks make up nearly half of a lawyer’s nonbillable time. 

An experienced MSO can professionalize these functions and take them off the attorneys’ plates. That is a genuine benefit. Time spent on administrative work is time not spent on billable matters, client development, or building the firm as a legal institution.

When an MSO law firm deal stops making sense

There are real benefits to MSOs for law firms. But MSO arrangements aren’t right for every type of practice, and understanding where these deals tend to go wrong can help you make the right decision before signing a multi-decade contract.

You lose meaningful control over staffing

One of the most significant long-term consequences of an MSO deal is what happens to staffing decisions over time. Once the MSO controls HR infrastructure—recruiting systems, hiring platforms, onboarding workflows, performance management tools, and compensation budgets—it has substantial influence over who works at the firm, even if the attorneys nominally retain the power to approve each hire.

Illinois recognized this risk explicitly in House Bill 5487, which specifically prohibits MSOs from selecting, hiring, or terminating attorneys or allied legal staff. The legislature understood something that MSA drafters often obscure: when someone else controls the systems, budgets, and workflows surrounding a staffing decision, they exercise substantial functional control over that decision regardless of what the contract provides.

In practice, if the MSO determines that the firm should be staffed with lower-cost paralegals rather than experienced legal assistants, it doesn’t need to override attorney objections. It funds one model and not the other, designs intake workflows that favor the lower-cost approach, and presents business case analyses supporting its preferred outcome. The attorneys might retain formal authority, but it is the MSO which shapes the available choices.

You concede technology decisions to the MSO

Technology selection has become one of the most consequential decisions a law firm makes. The choice of practice management software, legal AI platform, billing system, document management tools, and client communication infrastructure affects service quality, data security, staff productivity, and competitive positioning—and creates switching costs that lock firms in for years.

When an MSO controls the technology infrastructure, those decisions are no longer the firm’s. The MSO will select technology that serves its interests as a multi-firm platform: standardized across its portfolio, optimized for its own reporting and cost management needs, and evaluated against criteria that may or may not align with the firm’s clients or attorneys. Important considerations like protecting attorney-client privilege and confidentiality may not be given the appropriate attention by the MSO. An MSO’s incentive is to centralize technology across all the firms it manages. The law firm’s interest is to have the best available tools for its practice. These objectives frequently conflict, and in a long-term contractual relationship, the party writing the checks for the technology has the final word.

You risk quality degradation and contract lock-in

Perhaps the most underappreciated risk in MSO law firm transactions is what happens to service quality over a long-term contract when the relationship matures, or when it becomes less strategically important to an MSO that has grown its portfolio considerably.

The initial pitch from an MSO emphasizes what the firm is gaining: capital, operational expertise, marketing capability, and technology infrastructure. What the pitch doesn’t address is what year 12 of a 20-year contract looks like when the MSO is managing 40 firms, is under pressure from its private equity investors to hit margin targets, and is identifying ways to reduce per-firm operating costs. In that version of the relationship, the dedicated account team becomes a shared resource, technology upgrades slow, marketing investment per firm decreases, and administrative support staff turns over repeatedly as compensation is reduced to protect margins.

And the firm cannot leave—not without triggering financial penalties or facing the practical reality that its operations have become so deeply integrated with the MSO’s infrastructure that a clean exit would be operationally devastating.

This is not a hypothetical. In February 2026, PM Law Group, a United Kingdom accumulator firm operating across 11 law firms with 30 trading names, suddenly ceased operations. Six hundred people across multiple offices arrived to work on a Monday to find locked doors and revoked system access. Tens of thousands of live cases were left without active representation. The Solicitors Regulation Authority has since made emergency payments to clients and received more than 50 applications to its compensation fund. When combined with the prior collapse of Axiom Ince, the cumulative loss of client money in accumulator firm failures in the UK now stands at approximately £100 million.

The UK legal market structure differs from the United States. The structural dynamics of concentrated operational control under a long-term contract do not.

What other industries have learned about MSOs, and what legal should do now

The governance literature on MSO arrangements offers a clear-eyed preview of what the legal profession is entering. In a February 2026 working paper, Assistant Professor Lev Breydo of William & Mary Law School provides the first systematic account of the governance gap in which law firm MSO transactions are proliferating. His analysis of healthcare and accounting is instructive for any attorney evaluating an MSO approach.

Healthcare: control creep and documented harm

Healthcare has used the MSO model since the 1990s, under corporate practice of medicine restrictions that, like Rule 5.4, prohibit corporate control of professional practice. The enforcement record shows a consistent pattern. Formally compliant arrangements, including governance separation, independent practice boards, clear MSAs, and compliance protocols, can evolve toward greater MSO influence over clinical decisions through incremental operational integration.

Each individual step may be defensible as a business function. Cumulatively, they transform the MSO from a service provider into a de facto practice manager. The MSO hires the office manager, then the billing staff, then implements intake systems that channel patients based on revenue optimization. The mechanism is gradual and largely invisible until a disciplinary challenge or operational crisis makes it undeniable.

The empirical record is sobering. A widely cited 2024 study found that private equity nursing home acquisitions were associated with higher short-term mortality among Medicare patients, linked to staffing reductions. Other studies of private equity-owned emergency departments, dermatology practices, and ophthalmology practices have documented increased costs and higher complication rates. KKR-controlled Envision Healthcare faced allegations of violating California’s corporate practice of medicine restrictions by controlling staffing, scheduling, and billing of the nominally physician-owned entity. Blackstone-owned entities faced similar allegations in Texas.

The risk is not that the MSO explicitly directs professional decisions. It is that operational control reshapes case selection, workflow standardization, settlement timing, and resource allocation in ways that influence professional judgment without appearing to do so.

Accounting: the profession that moved first and is now scrambling

Since 2021, private equity has completed approximately 147 transactions involving accounting firms, reshaping that profession with a speed that left regulators in a reactive position. The SEC is now closely monitoring private equity-driven structural changes for risks to audit quality and independence. The Public Company Accounting Oversight Board has flagged private equity investment as an inspection priority. The American Institute of Certified Public Accountants voted in 2025 to circulate draft amendments to its independence standards—its most significant Code updates since 2000—in direct response to the governance problems posed by private equity-backed structures.

The legal profession is watching this unfold in real time. As Breydo notes, each law firm MSO transaction creates market acceptance, data points, and precedent that institutionalizes the model and lowers the threshold for the next. The profession has a narrow window to establish appropriate governance frameworks before a domestic crisis forces the issue.

What law firms and regulators should do

The regulatory response in the United States has been minimal. Texas issued the first state-level ethics opinion on MSOs in February 2025, implicitly endorsing carefully structured arrangements while prohibiting revenue-based fee sharing. Colorado, California, and Illinois have each introduced legislation imposing substantive restrictions. Colorado’s House Bill 26-1421 has passed both chambers and awaits the governor’s signature. The Illinois bill goes furthest, specifically prohibiting MSOs from accessing or controlling client records, selecting or terminating attorneys or legal staff, and setting competency or productivity standards for legal professionals.

But no state bar has issued model governance standards for law firm MSOs. No court has adjudicated the boundary between permissible management services and impermissible control of legal practice. The ABA has not updated its guidance since reaffirming Model Rule 5.4 in 2022 without engaging the governance questions that MSOs present.

The profession needs to close that vacuum proactively. Breydo’s proposed framework offers a workable starting point: structural safeguards limiting MSOs to genuine support functions, independent directors and a board ethics committee with real veto authority over actions that threaten professional independence, and ongoing compliance monitoring by a Chief Compliance Officer who reports to the ethics committee rather than to the MSO’s chief executive.

For firms currently in or considering MSO negotiations, the contractual framework matters as much as the governance structure. Any MSA should include a unilateral termination right exercisable by the law firm for material MSO interference with attorney independence—and that right must be financially feasible to exercise, meaning no prohibitive make-whole fees or restrictive covenants that render the right illusory. The MSO should not be permitted to control client records, select or terminate attorneys or legal staff, or set competency or productivity parameters for legal professionals. The Illinois bill’s list of prohibitions is a reasonable baseline for what an attorney-protective MSA should include.

Who MSO law firms are actually right for

Given the risks, an honest assessment points to two specific attorney profiles for whom the MSO law firm model genuinely makes sense.

The first is the lawyer entrepreneur who wants to build a multi-jurisdiction practice through acquisition. For this attorney, the MSO is not simply a vendor. It is a consolidation platform. The MSO provides the operational infrastructure to absorb acquired firms, standardize back-office functions, and scale without rebuilding administrative capacity from scratch at every location. The risks around control and lock-in are more manageable for an attorney whose goal is to move progressively out of day-to-day management and into a rainmaker or strategic role within a growing platform.

The second is the senior lawyer seeking a structured transition for their firm toward retirement. An MSO transaction allows this attorney to take meaningful capital off the table—monetizing equity that would otherwise wait years for a traditional succession—while continuing to practice law and serve clients through a defined wind-down period. The long-term contract is less threatening when the attorney’s planning horizon is a 10-year transition rather than a 30-year career.

For attorneys who want to continue practicing law on their own terms, the erosion of control over staffing, technology, and operational direction that comes with even a well-structured MSO deal will, over time, feel significant. For attorneys who end up in a poorly governed arrangement with an underperforming or financially distressed MSO, that erosion can become an existential problem for the firm.

Clio Capital: Growth capital without giving up control

For firms that want capital to grow without the governance trade-offs of an MSO transaction, there is an alternative. Whether the goal is hiring associates, expanding office space, investing in technology, or funding marketing, growth shouldn’t require giving up the firm’s independence.

Clio Capital provides financing for law firms directly through Clio Manage. Eligibility is based on payment volume and history through Clio Payments, so firms that process client payments through Clio are pre-qualified based on actual financial performance—not citizenship status, FICO scores, or collateral availability. There is no lengthy application process, no requirement to document why the firm cannot obtain financing elsewhere, and no weeks of underwriting review.

The application takes minutes and applying doesn’t affect credit scores. Approved firms receive funds in as little as two business days. The total cost is a single flat fee. No compound interest accruing over the repayment period, no prepayment penalties if the firm pays early. Repayment is handled through a weekly automated debit from the firm’s operating account. Firms can select the financing amount that fits their needs, up to their pre-qualified maximum, and see all costs upfront before accepting.

What Clio Capital offers matters as much as what it leaves out. There is no management services agreement. There is no investor weighing in on operational decisions. There is no surrender of authority over who works at the firm or which technology the firm uses. Capital is available when needed, on terms the firm controls, without structural entanglements that extend for decades. 

For law firms that need growth capital to seize a near-term opportunity—a lateral hire, a second location, an AI tool investment, cash flow coverage while a major matter moves through billing—Clio Capital provides the financing at the speed growth requires. The firm keeps its independence; lawyers keep control.

Is an MSO law firm deal right for your practice?

An MSO law firm deal is a long-term trade. Capital and operational support now, in exchange for shared authority over staffing, technology, and firm strategy for the next 20 to 30 years. For the entrepreneur building a multi-jurisdiction practice or the senior partner planning a retirement glide path, the trade can work because the contract length matches the strategic horizon.

For attorneys who want to keep building on their own terms, the deal that looked like a partnership in year one can start to feel like a long-term service contract by year ten. And by then, the cost of leaving is usually higher than the cost of staying.

If you’re deciding whether to adopt an MSO model, weigh whether the trade-offs of a specific arrangement make sense for the firm’s goals over the full life of the contract. If the answer is no, growth capital is still available through other channels, including Clio Capital, without giving up authority over the firm.

Clio Capital is available for Clio Payments users in the United States. Clio Capital loans are issued by Celtic Bank and powered by Stripe. All loans subject to credit approval. Availability may vary by state.

Law firms handle some of the most sensitive information in any profession, including personally identifiable information (PII), financial records, medical details, and privileged communications. Protecting this information is a fundamental ethical and professional obligation. When physical documents are no longer needed, they must be disposed of securely. 

But without the right processes in place, improper disposal can create serious risks for both clients and firms:

• Data breaches: Sensitive documents can be retrieved from unsecured waste or recycling bins, exposing confidential client information.
• Loss of client trust: Clients expect their information to be protected every step of the way. Mishandling records can harm relationships and damage the firm’s reputation.
• Regulatory and professional consequences: Failing to safeguard confidential information can result in disciplinary action, fines, or legal liability.

Standard recycling and in-office shredders are not enough to prevent these risks. They fail to address two critical challenges: who decides what gets shredded and when, and whether destruction can actually be proven.

Without clear policies, sensitive records can be mishandled or overlooked. And when a regulator or client requests confirmation that documents were securely destroyed, firms may simply not be able to provide it.

Secure document destruction services manage both issues. They go beyond shredders and equipment to offer governance, documented procedures, and audit trails.

What documents should law firms shred?

Law firms generate a massive amount of paper. Yet not every document should be kept indefinitely, nor should it be discarded without review. Clear categories and simple rules can help firms manage document disposal responsibly. 

Common categories of documents to shred include:

• Client records past retention periods: Closed client files and supporting materials that have exceeded retention requirements and are no longer needed.
• Financial documents: Billing records, invoices, trust account materials, and other financial documents containing sensitive information.
• Case drafts and notes: Drafts, internal memoranda, research notes, and other printed materials that are no longer required.
• Administrative and HR files: Internal firm records, personnel documents, and operational materials containing confidential or personal information.
• Mail, copies, and duplicates: Printed emails, duplicate documents, and unnecessary copies that could expose sensitive data if not properly disposed of.

Retention rules vary by jurisdiction, practice area, and document type. Firms should always confirm the applicable requirements and ensure original records are not destroyed prematurely.

For example, lawyers in Missouri are required to retain a client’s file for six years after completion or termination of the representation, absent other agreement. Prior to a rule change in 2016, the retention period was for 10 years. Missouri lawyers whose matters ended prior to July 1, 2016 are just now approaching the end of their mandatory decade-long client retention period.

Trust accounts and billing records all will have a minimum retention period. California requires law firms to preserve all trust account records for at least five years after the final distribution of funds or property held in trust. If you held funds in trust for several years prior to the final distribution, you’ll need to preserve all the records relating to that entire time frame for at least five years. Combine the time of representation and the minimum file retention requirement, you could have a duty to maintain these records for close to a decade. Pick a file storage method that can sustain and adjust over long periods of time.

Florida lawyers are required to maintain trust account monthly reconciliations and comparisons, and an annual listings of  unexpended trust money for at least six years, as well as other financial records. In addition to financial records, lawyers in Florida that have a contingency fee agreement with a client and whose services enable a recovery, must retain a copy of the written fee contract with the client and an itemized closing statement for 6 years after execution of that closing statement. Plaintiff’s attorneys representing clients against that client’s insurer must retain signed copies of the state’s required Statement of Insured Client’s Rights for the same time period of six years.

Advertising records is another type of document that may have file retention requirements. Florida lawyers must maintain a recording of any advertisement submitted to The Florida Bar for review for three years after its last dissemination along with a record of when and where it was used. Tennessee lawyers must retain copies of advertisements for two years after its last dissemination.

Some client documents may never have a point where they may be destroyed at the law firm’s initiative. These documents have intrinsic value, like client’s securities, negotiable instruments, and original wills, deeds, and trust documents. For example, solicitors in the UK are instructed never to destroy the original document version of a will. These types of documents must be either securely stored indefinitely, transferred back to the client, or delivered to the appropriate governmental unclaimed property agency.

The three-question rule for shredding

Before shredding any document, ask these three questions:

1. Is this document outside the retention window?
2. Is it free from any litigation hold or audit requirement?
3. Do we have a secure digital copy stored with proper access controls?

If the answer to any of these questions is no, the document should not be shredded.

Types of law firm document shredding services

Once firms know what to shred, the next step is choosing the right method. The best option depends on your firm’s risk profile, document volume, and operational needs. 

On-site shredding vs. off-site shredding

Both methods can be secure when handled properly, but they differ in cost, visibility, and operational control.

On-site shredding

Documents are destroyed at your office, often using a mobile shredding truck.

Pros

• Visibility into the destruction process.
• Immediate disposal.

Cons

• Higher cost than off-site shredding.
• Requires scheduling and staff coordination.

Ideal use case 

Best for high-risk moments (e.g., end-of-matter purges, partner departures, or after a merger) when witnessing destruction can reduce internal and client concerns.

Off-site shredding

Documents are securely transported to a provider’s facility for destruction.

Pros

• Scalable for large volumes.
• More cost-effective than on-site shredding.

Cons

• Requires strong chain-of-custody controls.
• Less visibility into the destruction process.

Ideal use case

Best for routine, ongoing volumes, especially when paired with locked consoles and clear pickup logs that create a defensible chain of custody.

One-time purges vs. scheduled shredding

Depending on their needs, firms can also choose between one-time destruction services or recurring shredding programs. 

One-time purges 

Pros

• Flexible for occasional or unexpected needs.
• Useful for clearing large backlogs.

Cons

• Higher cost per service.
• Risk of inconsistent disposal practices.

Ideal use case

Best for periodic cleanups, such as closing old files, preparing for an office move, or conducting a major records review.

Scheduled shredding

Pros

• Predictable costs and budgeting.
• Reduced risk of document buildup.

Cons

• Ongoing service commitment.
• Not suited for firms with low paper volume.

Ideal use case

Best for firms with steady document output that want routine, controlled disposal and reduced operational risk.

As a rule of thumb, if you’re using more than two one-time purges per year, consider moving to a scheduled service.

Top document shredding services commonly used by law firms

Law firms can choose from several types of shredding providers depending on factors like their size and service needs.

National providers

• Representative examples: Iron Mountain, Shred-it, Access.
• Best for: Large or multi-office firms needing standardized, nationwide service and strong compliance controls.
• Core strengths: Consistent processes, enterprise-grade security, scalability, and robust audit documentation.
• Trade-offs: Higher costs, less flexibility, and more structured contracts.

Network aggregators

• Representative example: Shred Nations.
• Best for: Firms that want multiple quotes quickly or need help finding local providers.
• Core strengths: Fast vendor matching, competitive pricing, and convenience.
• Trade-offs: Service quality and security practices vary by provider; may require additional due diligence.

Regional specialists

• Representative examples: Corrigan, Secure Shredding & Recycling, FileShred, All Points Protects.
• Best for: Mid-sized firms seeking a balance of cost, compliance, and responsive service.
• Core strengths: Personalized support, flexible scheduling, and competitive pricing.
• Trade-offs: Limited geographic coverage and fewer enterprise-level resources.

Local independents

• Representative examples: LegalShred, Shredding LV, Shredding PHX.
• Best for: Small firms, low document volumes, or relationship-driven service needs.
• Core strengths: Direct communication, high flexibility, and cost-effectiveness for smaller workloads.
• Trade-offs: Limited scale, fewer formal compliance systems, and potentially less standardized processes.

Not sure which shredding service is right for your firm? Use this quick guide to match your needs with the right provider.

What to look for in a shredding service provider

Not all shredding services offer the same level of security or accountability. When evaluating providers, look for the following: 

1. Certificates of destruction: Confirmation that documents were securely destroyed. 
2. Security standards and processes: Clear, documented security procedures such as employee screening, secure transport methods, controlled destruction environments, and strong chain-of-custody practices.
3. Mixed-media destruction: The provider should securely destroy hard drives, USB devices, and backup tapes, as paper-only services can leave gaps in your firm’s information security program.
4. Insurance coverage and breach liability: Written confirmation of insurance coverage and breach liability terms to protect your firm if something goes wrong.

The hidden cost of paper (and shredding)

Secure shredding is an essential service, but it can also signal a firm’s ongoing reliance on paper. The real cost isn’t in destroying documents, but in managing them throughout their lifecycle. 

Law firms can spend up to 3% of annual revenue on printing and document output alone. Add storage, organization, and staff time to monitor retention periods and coordinate disposal, and the cost climbs even higher.

Every bankers box stored off-site represents a future shredding expense, along with the administrative burden of retrieving records if a client later requests them.

Storing paper doesn’t eliminate risk; it simply defers it. That risk often resurfaces during audits, client disputes, or partner transitions, when the stakes and costs are highest. Ultimately, many firms spend more managing paper than they would by digitizing records and implementing secure document management practices.

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How cloud-based document management reduces the need for shredding

Cloud-based document management tackles the root cause of the problem: paper itself. By digitizing files, centralizing storage, and automating retention policies, firms generate less physical paper in the first place. This helps them avoid the risks and costs associated with physical disposal. Key benefits include:

• Stronger security: Access controls and permissions protect documents more effectively than paper.
• Clear audit trails: Digital records create a transparent evidentiary trail, reducing malpractice exposure.
• Simplified storage and retrieval: Centralized digital storage replaces filing cabinets and bankers boxes, making documents easier to locate, manage, and govern.
• Automated retention and deletion: Modern platforms track retention periods by matter type, helping firms avoid accidental over-retention or premature destruction.
• Reduced paper flow: Less incoming paper means less to shred, and a smaller environmental footprint.
• AI-enabled capabilities: Secure search, summarization, and retrieval make digital files far more functional than paper.

Instead of solving yesterday’s paper problems, cloud-based systems prevent tomorrow’s information risks. 

Cloud-based practice management platforms like Clio Manage centralize legal documents in a secure, cloud system, making collaboration easier during active cases and organization simpler once matters conclude. 

With role-based permissions and searchable storage tied to each client and case, firms reduce reliance on physical files and eliminate much of the manual effort behind storing and eventually shredding paper.

A smarter transition for paper-based firms

Firms that recognize the benefits of reducing their reliance on paper don’t need to overhaul their systems overnight. The transition to a paperless law office can happen in stages:

1. Start with active matters: Focus on your current files and most important documents; leave lower-priority archives boxed for now.
2. Standardize document intake: Shift to digital-first intake using email, e-forms, and e-signatures to limit new paper entering the system. Platforms such as Clio Grow make it easy to streamline the process. 
3. Gradually reduce paper dependence: Expand digitization to more documents types over time, prioritizing files that create the most friction or risk.
4. Use shredding services strategically: Schedule destruction for closed legacy files as their retention periods expire, rather than trying to shred everything at once.

This incremental approach to modernizing your firm’s paper workflows maintains continuity and helps staff adapt to the new system. 

Shred what you must. Store smarter going forward.

You don’t need to be Tom Cruise in The Firm or a first-year law student in The Paper Chase to know that managing sensitive client documents responsibly is critical.

Secure shredding remains essential to protecting client information and ensuring your firm’s compliance with applicable standards. But long-term document security isn’t achieved through shredding alone; it comes from generating less paper in the first place. 

Firms that see the fewest incidents are those that make reducing paper a priority. As AI and digital workflows become the norm, paper-heavy practices will face rising costs, slower processes, and greater compliance challenges. Adopting a digital-first approach strengthens security, lowers risk, and improves efficiency, positioning your firm to thrive in the technology-driven world of today. 

Ready to shred less and work smarter? With Clio Manage, you can centralize your documents, automate retention, and reduce paper, all without compromising client security. Book your demo today.

Annual performance reviews can be stale, vague, and a surprise to staff. Part of the problem comes down to cadence. Most law firm performance reviews still revolve around a single annual conversation, but performance doesn’t operate on a once-a-year timeline.

In many firms, six or even 12 months of feedback gets compressed into one high-pressure meeting. That creates predictable challenges: recent events carry too much weight, feedback becomes vague, and opportunities to correct issues earlier are missed.

Without a regular feedback rhythm, disengagement and performance gaps are easier to overlook. Strong performers may feel unsupported, while struggling employees often don’t realize there is a problem until it has already escalated.

Effective performance conversations are ongoing, specific, and based on current information. Firms need both structured feedback processes and reliable performance data to support them.

What does good performance actually look like in a law firm?

It’s hard to have effective discussions if your firm hasn’t clearly defined what strong performance really looks like. Reviews often focus too much on outcomes like billable hours and revenue, while overlooking the habits and behaviors behind those results.

A more effective approach starts with a few key principles.

Billables are an outcome, not a behavior

Law firm performance is often reduced to billable hours: more hours are good, fewer are bad. But billable volume alone doesn’t actually tell you much.

Regardless of billing structure, strong billables stem from behaviors like organization, responsiveness, communication, initiative, and follow-through. When those behaviors are in place, performance and revenue tend to follow.

Managers who only focus on the numbers miss the opportunity to coach the habits that drive performance over time.

Performance looks different by role

Good law firm performance isn’t a monolith. While standards matter, expectations for an associate shouldn’t be the same as those for a paralegal, and neither follow the same path for professional development for legal support staff. Relying on a single, generalized standard across roles sets both firms and staff up to underperform.

For associates, performance is tied to work quality and matter ownership. Are they producing accurate, well-reasoned work? Can they manage files with increasing independence and judgment?

For paralegals, it’s about consistency and flow. Turnaround time, accuracy, and the ability to keep matters moving without constant oversight all matter. Strong performance shows up as work that makes everyone else’s job easier.

For law firm support staff, it’s less about legal output and more about coordination and responsiveness. Are requests handled quickly and reliably? Do they help keep the firm’s day-to-day operations running smoothly?

When firms don’t account for these differences, performance conversations become vague or mismatched. Role-specific clarity makes them more consistent and ultimately more useful.

Leading vs. lagging indicators

Lagging indicators measure results, while leading indicators help shape them. For performance management, you need to balance both.

Focusing on lagging indicators feels intuitive for law firms. Billable hours, revenue, and realization rates are important success metrics, so it’s natural to rely on them in performance conversations.

But lagging indicators are outputs. They tell you what’s already happened, not how or why. By the time those numbers shift, underlying issues may have been building for weeks or months.

Hence, leading indicators. Things like task completion time, responsiveness, communication patterns, and follow-through give an earlier signal of day-to-day performance and help surface bottlenecks before they show up in the numbers.

Individual performance connects to firm goals

Individual performance doesn’t exist in isolation. The way someone works directly impacts how a firm functions as a whole.

Together, individual behaviors compound and shape how efficiently the firm operates. An organized, proactive associate reduces friction for partners. A paralegal who consistently turns work around accurately keeps matters moving. A responsive support team prevents delays and keeps communication flowing.

Individual behaviors drive team efficiency, which impacts client experience and ultimately influences firm performance and growth.

When firms lose sight of that connection, performance conversations become disconnected from business outcomes. When they keep it in focus, those conversations become far more meaningful because they’re tied directly to how the firm actually succeeds.

Preparing for law firm employee performance evaluations

Learning how to manage a small law firm means learning how to have better performance conversations. But those conversations are only as useful as the preparation behind them.

Start with data and context

Effective reviews draw from multiple key performance metrics for law firm management. That might include quantitative factors like billable hours, matter volume, or realization rates, alongside qualitative feedback from colleagues, clients, and day-to-day collaboration.

This doesn’t mean obsessing over metrics. Instead, your aim is to identify patterns in how someone works. 

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Bring staff into the process early

If the first time someone reflects on their performance is during the review meeting itself, neither side will get much value from the conversation.

Asking staff to complete a self-assessment beforehand helps surface how they view their own work, what they’re proud of, and where they see gaps. That comparison between self-reflection and manager perspective is often where the most useful discussions begin.

Set the tone before the meeting with a purpose statement

A performance review isn’t a retrospective verdict. It should be a forward-looking conversation about development and expectations.

Scheduling the meeting with a clear purpose statement can set the right tone.

Even simple framing like, “This will be a conversation about how things are going and where you want to grow next,” helps make the discussion feel more collaborative from the start.

Prepare a focused set of points

Before the conversation, narrow in on a small number of specific discussion points: three to five is likely enough.

Your points should cover:

• Specific strengths with examples
• Clear growth areas with context
• At least one forward-looking opportunity or development goal

You don’t need to cover everything. Focus on the points that will actually help improve performance moving forward.

How to give feedback to law firm staff

If you want to make performance conversations more productive and less subjective, it’s not about increasing the amount of feedback. It’s about making each conversation more structured, specific, and grounded in real examples.

Start with what’s working

Legal professionals are typically high performers, but they’re still human. Anchoring the conversation in specific, observable strengths helps set a constructive tone and makes it easier to discuss areas for improvement.

Positive feedback also isn’t just a courtesy. It reinforces the behaviors you want to see more of when it’s tied to concrete actions. Research suggests that combining performance feedback with positive reinforcement can improve subsequent performance and outcomes.

Specificity makes the feedback credible, grounded, and easier to build on when shifting into development areas. Instead of “great job on this matter,” point to what actually made the difference: responsiveness to a difficult client, accuracy in a complex filing, or proactive ownership of a deadline. 

Use SBI to structure growth conversations

When it’s time to address areas for improvement, structure matters. One way to keep feedback clear and objective is to use the SBI model.

Here are employee performance feedback examples of the SBI model in practice:

• Situation: During yesterday’s witness preparation session for a deposition…
• Behavior: …you interrupted the senior partner while they were explaining privilege rules to the client…
• Impact: …which disrupted the client’s understanding and required us to spend extra time re-explaining the instructions.

Framing feedback this way shifts the conversation from interpretation to observable behavior, closing the gap between intent and impact. 

Shift from feedback to collaborative problem-solving

Instead of hyper-focusing on what went wrong, explore what’s behind the feedback in question, and what needs to change going forward to fix it.

Returning to the example above, the SBI structure helps clarify what occurred. From there, the focus can move to what needs to change in similar situations.

Here, open-ended questions help move the conversation away from the incident itself and toward ownership of next steps.

• What was happening in that moment?
• What would you do differently next time?
• How can you prevent this from happening again?

Asking questions like this can nudge performance conversations from manager-led to staff-led, which is key, as solutions from individuals are more likely to stick than instructions delivered in isolation.

The conversation can then focus on defining what success looks like going forward.

Turn discussion into clear development goals

The final step is turning the conversation into something concrete and mutually agreed upon. The manager and staff member should both leave aligned on what success looks like and how it will be measured.

SMART lawyer goals are useful. Clear expectations that are specific, measurable, achievable, relevant, and time-bound help remove ambiguity and make follow-up conversations easier and more productive.

For example, instead of a general expectation like “be more attentive in client meetings,” a clearer SMART goal could be that in future witness preparation sessions, the associate takes notes during partner-led explanations and reserves questions for designated breaks, to be reviewed over the next three matters.

Strong performance conversations don’t end with agreement on what needs to change. They end with clarity on what success will look like next.

Handling difficult scenarios in law firm performance management

Performance conversations get harder when things are already off track. While the instinct is often to either soften the message or over-explain it, your best path is familiar: stay close to specific behaviors and be consistent about expectations, even when the conversation is uncomfortable.

Underperformers

Usually, underperformance isn’t one bad moment. It shows up as a pattern that becomes harder to ignore over time, so documentation matters. 

It doesn’t necessarily have to be formal or heavy, but documentation is important for keeping a clear record of what’s been expected and what’s actually happening. It makes it easier to have a direct conversation without relying on memory or emotion.

Once the pattern is clear, a performance improvement plan can help reset expectations and timelines. Not as a last resort, but as a way to make the next steps explicit for everyone involved.

High performers

It’s a bit counterintuitive, but some harder feedback conversations may actually be with your strongest performers. In these instances, the risk isn’t underperformance. It’s losing them.

If someone is consistently delivering good work, they still need a reason to stay engaged. That usually means giving them more responsibility than just more of the same. Stretch work, ownership of more complex matters, or informal leadership roles can all play a part.

Without that, strong performers tend to plateau quietly and eventually look for their next step elsewhere.

Emotional conversations

Some performance review conversations get tense, usually when feedback feels unexpected or when someone disagrees with the framing.

In those moments, the most useful thing you can do is stay specific. Go back to what actually happened, not interpretations of it. If things escalate, slow the conversation down, take a pause, and stick to examples rather than general concerns.

What to track between performance conversations

Tracking a consistent set of legal team performance metrics between reviews helps surface patterns before they become bigger problems.

Exactly which metrics you track will vary by role, but common examples include utilization, realization rates, matter counts, turnaround time, and client feedback. The point is to understand how someone is performing over time, not just how they looked in the last week or two before a review.

You’ll also want to zoom out occasionally. Is it an individual performance issue, or is it actually a workflow bottleneck, uneven staffing, or a broader team problem? Reviewing metrics at the individual, team, and firm level helps add that context.

However, a caveat to note: for many firms, the bigger challenge isn’t collecting data. It’s actually using it. Performance data often gets pulled into reports but never meaningfully discussed. The value comes from looking at trends over time and using them to support more informed conversations.

A better approach to law firm performance management

Law firm performance management success isn’t about having one perfect annual conversation. Success grows from consistent, productive review conversations throughout the year. When feedback is part of how a firm operates, performance becomes easier to understand and manage. Teams are then free to focus on the work that actually moves things forward.

Shifting from annual retrospectives to a regular rhythm of feedback and coaching extends the impact beyond individual development. It helps create a firm where alignment improves, issues surface earlier, and retention is supported by clearer expectations and career paths.

To learn how to track the metrics behind stronger performance conversations, download our Performance Metrics for Legal Teams guide.

Want the full data behind what’s working at the firms growing revenue with AI? Download Clio’s 2026 Legal Trends for Solo and Small Law Firms.

Solo and small law firm AI adoption: Why revenue gains are lagging

Day-to-day, the picture in most smaller firms is encouraging. The 2026 Legal Trends for Solo and Small Law Firms report found that 64% of solo firms say AI has lifted the quality of their work, 62% say it’s cut tedious tasks, and 60% say they’re responding to clients faster. Almost half feel empowered to handle more complex matters, and 43% say AI has enabled them to take on more work overall. If your practice feels better than it did a few years ago, that’s the technology doing what it was supposed to do.

How hourly billing works against AI for solo lawyers

If AI helps you finish a five-hour task in an hour, and you bill by the hour, you’ve just given your client an 80% discount. The savings landed in their pocket, instead of yours. As long as your billing structure stays the same, you’ll need to bring in more new clients each year just to hold revenue steady.

Solo and small firms have been slowly moving in the right direction. Among solos, the share billing exclusively by the hour has dropped from 55% in 2019 to 50% today. Small firms have moved further over the same stretch, from 53% to 43%.

But that shift hasn’t kept pace with AI’s impact on the work itself. The report found that 86% of solo firms and 78% of small firms haven’t changed their pricing at all since they started using AI, compared with 51% of mid-market firms and 46% of enterprise firms. And the small number that have moved aren’t doing it boldly: Only 3% of solos and 5% of small firms have raised prices to reflect the higher-quality work AI now lets them deliver.

The flat fee vs. hourly billing question is one most clients have already answered for themselves. Clio’s 2024 Legal Trends Report found that 71% prefer flat fees over hourly when given the option. Moving away from exclusive hourly billing protects your margin and gives clients the pricing model they were going to ask for anyway.

Is AI fueling solo and small firm growth, or slowing it down?

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What hourly billing will cost your firm over the next two years

It helps to put some numbers on this. Take an estate planning matter that used to take 30 hours at a $250 rate and bill out at $7,500. If AI saves 40% of the time on that kind of work, the same matter takes 18 hours and bills at $4,500. The 12 hours that used to show up on the bill no longer do, and the savings stay with the client.

AI capabilities are only getting more powerful. If next year’s tools save you another 15 to 20% on the same matter, you’re down to roughly 14 or 15 hours of billable time. That same estate plan, at the same hourly rate, becomes a $3,500–$3,750 invoice. Two years in, you’ve lost more than half the revenue on a matter you’re handling at least as well as before.

Clients are still figuring out what AI should mean for legal pricing. That gives firms a chance to adjust now, before expectations settle. Wait a year or two and it gets harder. Competitors will have moved, clients will have new reference points, and the same change will feel like a price hike. The firms growing revenue with AI today priced for it from the start.

How to turn AI efficiency into law firm revenue

Firms growing revenue with AI tend to do two things differently: They’ve changed how they price their work, and they’ve found a way to put the saved hours back to use. Most are working on both at the same time, and neither one means rebuilding your practice.

Lever one: Rethink your billing model

Flat fees let you charge for the outcome instead of the hours, and once you do, efficiency stops working against you. Firms that navigate this shift best pick one practice area where the scope is predictable, such as estate planning or an uncontested divorce, and price the work as a package.

Many smaller firms have already started moving in this direction. Exclusive hourly billing has dropped at solo and small firms every year since 2019, which means flat fees and hybrid arrangements are how a growing share of work already gets priced.

A practical place to start: Choose the matter type where AI has saved you the most time over the last six months. Work out what it used to take, what it takes now, and what your hourly bill on that work has become. The gap is your repricing opportunity.

Lever two: Fill the capacity AI creates

Saving time only helps your revenue if you use that time to take on more work. Most solos haven’t gotten there yet. AI can free up several hours a week, but those hours don’t fill themselves with new clients. The firms growing their revenue from AI are actively bringing in more work to fill the time they’ve saved.

Three changes do most of the heavy lifting, and none of them require new headcount:

• Cut your response time on inbound inquiries. A same-day reply puts you ahead of most of your competition. With online intake forms, lead information lands in one place the moment a prospect submits it, so you’re not piecing together details from voicemails and emails before you can respond.
  
• Reduce the friction in onboarding. Clients shouldn’t have to wait while you mail forms or chase signatures. Clio Scheduler lets prospects book consultations directly from your website, and e-signatures on retainer agreements close the loop without an in-person meeting.
  
• Follow up automatically on leads that don’t convert right away. A short email sequence after the initial contact will recover business you’d otherwise lose. Clio Grow runs those sequences in the background and shows you where each prospect stands, so a missed reply doesn’t become a lost client.

These three changes are the core of growing a solo law practice in a way that compounds: Faster intake brings in the work, lighter onboarding closes it, and steady follow-up recovers what would otherwise slip away.

Why generic AI tools aren’t enough for small law firms

Most solo and small firms are using AI, but many are using technology that wasn’t built for legal work. About 47% of solos and 48% of small firms rely on generic, consumer-grade tools like Claude, ChatGPT or Microsoft Copilot. They save time on basic tasks, and as a starting point, that’s fine. The limitations show up once the work gets more complex.

Generic models don’t understand legal context, which means a lot of re-prompting before you get something usable. They’re also prone to AI hallucinations—confidently citing case law, statutes, or regulations that don’t exist—because they aren’t grounded in verified legal sources. And they don’t connect to your matter records, so you end up copying and pasting between systems just to make them work for you.

Then there’s the confidentiality risk. Generic AI tools may use your input data to train their models, which means anything you paste into them can leave your firm and contribute to a public model. California is moving toward legislation that would prohibit law firms from entering confidential client information into public generative AI tools, and other states are likely to follow.

That’s a problem for most solo and small firms, because they don’t have the guardrails in place. The report found that 55 to 57% of solo and small firms have no AI policy at all, which means there’s nothing in writing telling staff what tools are allowed or how client data should be handled.

So how can you tell whether the AI tool you’re using is built for legal work? Here are four questions worth asking:

1. Does it understand legal context, or do you have to re-explain your practice area, jurisdiction, and matter type every time you open it?
   
2. Does your client data stay inside a secure, purpose-built environment, or is it leaving your firm the moment you paste it into a prompt?
   
3. Does the tool connect to the rest of how you run your firm (such as billing, intake, matter management, or document storage), or is it another login on a list that’s already too long?
   
4. Is it saving you time on billable work, or only on tasks you weren’t billing for in the first place?

If the answers point you toward a separate, generic tool with no legal context and no integration, the time savings are smaller than they look. Firms using legal-specific platforms with AI built in tend to have fewer adoption barriers and better outcomes, in part because the friction of switching tools and re-prompting diminishes when intake, billing, and matter management already live in the same place. That integrated approach is the idea behind Clio’s Intelligent Legal Work Platform, where AI works across the case record rather than alongside it.

Three ways to turn AI efficiency into revenue this quarter

Turning saved time into revenue takes deliberate work, but it doesn’t take a full strategy reset. Three small changes this quarter will get you most of the way there.

1. Audit one practice area. Pick the matter type where AI has cut your time the most. Pull a few recent matters and run the math: What your hourly bill used to be on that work, what it is now, and the difference. If a matter that used to bill at $7,500 now bills at $5,000, you have a clear number to anchor a repricing conversation.
   
2. Introduce a flat-fee option for your most repeatable work. There’s no need to change everything at once. Pick one practice area where the scope is predictable and offer a flat fee alongside your hourly rate. Set the price based on the value of the outcome and what the work used to take, not on what it takes you now. Most clients will choose it, and your margin will improve.
   
3. Track where your reclaimed time is going. If the hours AI is saving you aren’t going to billable work or business development, repricing won’t fix that. Block a week and account for what’s filling the time.

The bottom line

If you’ve put AI to work in your firm and revenue hasn’t moved, the problem isn’t you. Adopting AI is the first step, and you’ve taken it. The next step is deciding what to do with the time it gives you back, whether that’s rethinking how you price your work, building a steadier client pipeline, or both. That’s what solo and small law firm profitability ultimately comes down to.

Ready to see exactly what the firms growing fastest are doing? Download the 2026 Legal Trends for Solo and Small Law Firms report for the full data, the benchmarks, and the strategies behind them.

If you’re ready to see what a legal-specific platform looks like in practice, one that builds AI into intake, billing, and matter management, book a demo of Clio today.

The most difficult reality of law firm attrition is that it’s expensive. The American Bar Association estimates that employee turnover costs law firms between $200,000 and $500,000 per lawyer. That’s taking into account recruiting fees, onboarding, lost billable hours during transition, disrupted client relationships, and the specialized knowledge that individuals have with respect to specific clients, legal domains, and wider firm operations. 

Internal employee turnover costs in law firms are felt more sharply in mid-sized organizations than in larger ones. A 500-lawyer firm expects a certain law firm turnover rate in any given time period, and often has the dedicated processes and people to navigate these changes. 

A 75-lawyer firm, however, depends more on individual people to own certain responsibilities within their teams and with individual clients. Firms of this size often can’t compete on compensation with Big Law when it comes to finding new people. To avoid these challenges, finding ways to keep people should be a top priority. 

Why the standard retention playbook isn’t enough

Just a few years ago, retention for most firms was about offering competitive pay, flexible work schedules, wellness benefits, mentorships, and career development opportunities. These will continue to matter, but it’s worth acknowledging that they’re often not enough to keep people, especially in the demanding field of law. 

The daily experience of work in a law firm can be mentally and emotionally exhausting. Add in the frustration of any systems and processes that make the work more difficult than it needs to be, and employees can feel like they’re spending too much time on manual administrative tasks on top of their actual legal work. 

Research into the problem is telling. Bloomberg Law’s research shows lawyers report experiencing burnout 52% of the time. In another study, 24% of female and 17% of male lawyers were considering leaving the legal profession due to poor mental health, burnout, or stress.

While retention strategies focus on reward, growth, and flexibility, they often don’t take into account what it’s like to actually work at the firm. With AI, this will become even more of a defining factor for employees doing the work. 

AI won’t replace your lawyers, it makes them want to stay

While firm owners and managers worry about employees leaving the firm, many lawyers are worried about losing their jobs. AI is a key concern, especially with headlines about Baker McKenzie laying off support staff in favor of AI, and Axiom’s recent survey showing 76% of lawyers fear AI will replace them in their roles.

While some firms have consolidated their workforce, the reality is that there is still a lot of work to be done. The wider trend is that most of the biggest firms plan to hold onto the people they have. In recent research from Harvard Law School, none of the AmLaw 100 firms interviewed planned on reducing their attorney headcount, even despite the 100x productivity gains many of them have seen. 

AI is creating better work environments for legal professionals, and in turn, leading to better business outcomes for law firms. 

At mid-sized firms, among those using AI, 58% say AI has empowered them to handle more complex work and 57% say the technology has improved their work-life balance. 50% also experience less stress, and 46% say AI makes them more likely to stay at their current firm for the next two years. 

AI makes the daily work at a law firm manageable, and that keeps legal professionals from looking for new roles.

How AI reduces cognitive load and prevents burnout

Clio recently undertook a neurological study to understand how AI can support the mental capacity of legal professionals in their law firms. 

Cognitive load is the mental effort required to process information, switch between tasks, hold details in working memory, and manage administrative overhead alongside substantive legal work. The operational friction of constant context-switching, manual data entry, and managing information across email chains can take its toll on brain function and can contribute to burnout. 

In the research, legal technology was shown to reduce cognitive load in legal professionals by up to 25%. Additionally, those using Clio’s AI were twice as likely to answer legal questions correctly after reviewing a will, and 40% more were able to complete the assignment than those not using AI. 

Firms that support AI use among their staff can reduce the mental toll of legal work, which in turn reduces the risk of burnout and staff turnover. At the same time, they give their staff the ability to do better work more efficiently. 

Read our lawyer burnout guide to learn how to spot the warning signs before they lead to turnover.

What AI-enabled law firms look like in practice

When putting aside the cost of employee turnover and retention initiatives, the use of AI also helps firms achieve better outcomes for clients. 

65% of those working at mid-sized firms say that AI allows them to handle more work volume, which saves the need for additional headcount. 44% also report improved client satisfaction, and 42% say AI has helped differentiate their firm from competitors. 

Most importantly, 39% of AI-users have seen revenues improve at their firm. 

When firms are doing good work for clients and growing, employees tend to find the most satisfaction from their work, and that motivation itself can be a powerful means for retention.

One of the distinctions for mid-sized firms in their AI use is that they’re more likely to be using specialized solutions for legal work (though not as much as larger enterprise firms). This is important because generic solutions like ChatGPT can end up requiring a lot of prompting work to tailor responses to a given legal situation (which can also create issues for data privacy when using free and lower-tier versions).

Legal-specific AI solutions on the other hand have tailor-made workflows built into their systems, which can greatly expedite the work and improve quality. Again, this reduces the burden on employees to get the results they need from these tools, and also gives them more confidence in their work. 

For mid-sized firms especially, only legal-specific solutions, tailored to the types of work they do,will allow them to adapt to new opportunities.

The cost of not investing in AI for your team

Looked at from the opposite perspective, firms that don’t support the use of AI are essentially asking employees to do more with less. Compared to other firms, prospective employees see a workplace that requires more cognitive work, more administrative burdens on their time, and more friction that makes their days more difficult and limits their contributions. 

This will be especially true for younger lawyers who are quicker to adapt to new technologies, and for whom AI is becoming core to their work. 

Research from Harvard Law School indicates that new grads are entering the workforce expecting that law firms will provide technologies to help them “think more and repeat less” in their work. Debevoise makes a similar prediction, suggesting that AI capabilities and fluency will become a major influence on where legal professionals look for work. 

The Debevoise prediction shows that it’s not just availability of AI that’s important. That “fluency” expectation means that workers are going to look for organizations that have adapted their workflows and culture to working with AI. Data from Legal Trend for Mid-Sized Law Firms suggests that larger firms are ahead when it comes to policy. Still, nearly a third of mid-sized law firms have no policy on the use of AI. 

In addition to having guidance on the use of AI, firms need the right systems to integrate them. When adopting tailored AI solutions designed for legal work, being able to connect them with internal knowledge databases is what ensures that workers aren’t jumping between systems to complete simple tasks. 

Cloud-based practice management solutions create a stark improvement over server-based systems. Not only do they give employees more flexibility in their work, they make information more accessible. This is a huge advantage for connecting AI solutions that can then very quickly catch up and use important case notes and documentation for analysis and drafting. 

While 86% of mid-sized firms have adopted AI, only 57% use cloud-based practice management systems. For most, this means that employees at these firms are likely juggling multiple systems and tools. Read more about how cloud-based practice management solutions can support your firm’s technology stack in “Why Cloud Solutions Are the Foundation for AI in Law Firms.”

How to make AI part of your retention strategy

AI can make the daily work at a law firm more manageable, which reduces burnout and keeps legal professionals from looking for new roles. To successfully leverage AI as a retention advantage, firms should take concrete steps to audit, formalize, and invest in purpose-built legal technology.

1. Audit current AI usage at your firm. Are people using legal-specific tools integrated into their workflows, or are they patching together generic platforms on their own? The goal is to learn what tools your people are actually using. If they’re using solutions outside of what the firm provides, there’s likely a reason. Read more about shadow IT and AI in law firms. 
2. Formalize an AI policy. Just sixty percent of mid-sized firms have an AI policy. If yours doesn’t, staff are likely making judgment calls about how to use confidential data with these systems. If your firm hasn’t done its due diligence to identify what solutions can be used, and provided guidance on their use, this could put your firm’s data privacy at risk. Not sure where to start? See our guide to building a law firm AI policy.
3. Invest in integrated, legal-specific AI. The cognitive load reductions cited in the Legal Trends Report  come from purpose-built legal technology rather than consumer chatbots. Generic tools don’t deliver the same retention dividend. Investing in a platform solution that keeps your system connected will save time on manual prompting and greatly increase the quality and impact of work. 
4. Measure the retention signal. Add an AI question to your next staff engagement survey. The Legal Trends Report benchmark is that 46% are more likely to stay with their firm due to AI. See where your firm lands and where the gaps are.

Want more research and analysis on the use of AI?

Read our 2026 Legal Trends for Mid-Sized Law Firms report to learn more about how firms are using AI to benefit their staff and their business.

Read the report

The mid-sized firm retention advantage

The biggest law firm employee retention risk in a mid-sized practice is asking talented people to do demanding work without the tools to do it well. Pay and culture matter, but they can’t make up for that gap on their own.

Firms that invest in AI see less stress, less burnout, more capable teams, and significantly higher intent to stay. The firms that treat AI as a retention strategy, beyond its productivity gains, will hold onto their best people while the market moves around them.

For a deeper look at how mid-sized firms are using AI to retain talent and grow, read the 2026 Legal Trends Report for Mid-Sized Law Firms.

In a legal context, AI hallucinations are one of two things. They’re either citations to cases or statutes that don’t exist, or citations to real authorities for propositions those authorities don’t actually support.

The first kind is the one making headlines. A lawyer or pro se litigant uses a general-purpose chatbot like ChatGPT, Claude, Gemini, Copilot, or Grok to help draft a brief. The model, predicting the statistically likely next word, decides a citation belongs in a particular spot, and produces one. The reporter might be real. The volume number might fall within the right range. The Bluebook formatting is often better than what most associates produce. The case itself just doesn’t exist.

The second kind is older than AI. Lawyers have always occasionally cited a case for a proposition that the case doesn’t stand for. AI has made this kind of error easier to commit and easier to catch.

If you’re hoping the next generation of models will fix this, set that hope aside. Sam Altman has acknowledged that hallucinations aren’t a bug in large language models. They’re a feature of how the technology works, and GPT-5 hallucinates more than GPT-4 did. The hallucinations have gotten more convincing, not rarer. That’s not a reason to swear off AI. It’s a reason to choose your tool wisely, and be disciplined about your workflow. We’ll cover both below.

Why the citations look so convincing

There’s a psychological trap with hallucinated citations. In a brief with 19 citations, an AI tool may produce 18 that are real and one that isn’t. Reviewing the first several and finding them accurate lulls you into trusting the rest. Then citation 14, perfectly Bluebooked and perfectly plausible, points to nothing.

For a generation of lawyers, polished writing has been a proxy for careful lawyering. That proxy is now broken. A motion can be simultaneously flawlessly written and badly lawyered. The perfect Bluebooking is no longer a signal that anyone actually read the case.

That puts the burden of supervision back where it has always belonged: on the supervising lawyer, at the end of the drafting process, before the document goes out. This is already required by ABA Model Rules 5.1 and 5.3. Accuracy is also required by federal Rule 11 (and its state-court analogs). In a court filing, Rule 11 states that everything above your signature is true and correct, whether it came from a paralegal, a first-year associate, or an AI-backed tool. Supervision is one piece of a broader set of ethical duties that apply to AI in legal practice.

Some jurisdictions are responding by adding AI-specific rules. California is considering amendments to its professional conduct rules to address AI directly, and Florida has already done similarly. Those rules will probably not age well. The duty to supervise people and tools that produce work in your name has existed since the profession’s inception. It applies to AI for the same reason it applies to a typist or a junior associate. We probably don’t need a new rule. We need lawyers to follow existing rules. 

How often are AI hallucinations really happening?

Damien Charlotin, a researcher who tracks AI hallucination legal cases worldwide, has documented around 1,400 cases globally where AI-generated errors made it into a filing. More than 955 of those are in the United States.

For context, Docket Alarm contains roughly 40 million U.S. cases filed since January 1, 2023, when ChatGPT-style tools entered widespread use. That works out to one documented hallucination per 41,000 cases, or about 0.002 percent. Across the roughly 200 million filings in those cases, the rate is even smaller.

Two caveats. First, that count only includes hallucinations that were caught. The real number is almost certainly higher, since some bad citations slip past both opposing counsel and the court. Second, the denominator includes every filing, not just AI-assisted filings. If only a fraction of lawyers are using generic chatbots in drafting, then the rate within that subset is much higher.

A few other patterns from the data:

• More than 60 percent of the U.S. cases involve pro se litigants, not represented parties.
• The cases that do involve lawyers cut across firm sizes and practice areas. Sullivan & Cromwell was recently called out for hallucinated citations. These AI hallucination lawyer stories aren’t just a small-firm problem.
• The lawyers who get caught with hallucinations sometimes double down. They deny that they used AI. They might insist that the cases are real—until they’re proven wrong. 

You’re statistically more likely to encounter hallucinated citations in an opponent’s filing than to produce one yourself. Which is exactly why this matters in both directions.

How to keep AI hallucinations out of your own work

Strong AI hallucination guardrails for legal work come down to four things to look for in any AI tool you use.

1. It’s trained on real legal authority, not the open internet. A general-purpose chatbot is trained on pablum like Reddit threads and YouTube comments. You wouldn’t do legal research in those dubious sources. So don’t use a research tool that learned from them either. Solutions like Clio Work and Vincent by Clio are grounded in actual case law, statutes, and rules. We’re obviously not unbiased about those products, but the principle stands regardless of which tool you choose: use a tool that uses real law.
2. It can be confined to your jurisdiction. A persuasive case from another circuit isn’t the same as binding authority. Your AI tool should let you direct it to the law that actually applies to your matter.
3. It produces verifiable output with hyperlinks. Inside Clio, a phrase is more frequent: “hyperlinks or it didn’t happen.” Citations in AI-generated drafts should link directly to each underlying authority, making the citation easy to verify. The absence of a working link is itself a red flag. Before you file, click every link. Trust but verify. 
4. It produces a defensible record of how you used it. If a court ever asks how AI fits into your workflow, you should be able to show your AI interactions, the output, and your verification steps. Tools built for legal use create that “trust but verify” audit trail. Public chatbots don’t.

Even with all four in place, you still need that end-stage supervision. Read the cases. Click every hyperlink. If a citation doesn’t resolve to a real case that actually says what the brief claims it says, that’s the moment to catch it, before adding your signature. 

Practice the future of law today

With Clio Work, you go beyond generic chatbots and use AI that understands the context of your matters and delivers precise, cited legal research, analysis, and drafting that moves your cases forward.

Discover Clio Work

What to do when you find AI hallucinations in opposing counsel’s brief

You will run into this, either in your work, or work from someone else. When you do, you have an obligation to catch it. The duty of competence requires you to verify the law cited against you, the same way the supervising lawyer on the other side should have verified it before filing. In Noland v. Land of the Free, L.P., a 2025 California Court of Appeal (Second District) decision, the court sanctioned a party about $10,000 for filing a brief with hallucinated citations. When the non-erroneous party then sought attorney’s fees for the work caused by the hallucinations, the court denied them, finding that they should have caught the errors themselves. Attorney’s fees in these cases tend to track the extra work caused by bad citations, not a separate failure to flag the misconduct, but the principle remains the same. Courts expect you to read the law cited at you.

You also have a choice about how to handle hallucinations once you’ve found them. The model rules guide you either way. Rule 3.3 (duty of candor to the tribunal) and Rule 8.3 (duty to report misconduct) both support raising the issue with the court. Nothing requires you to give opposing counsel a heads-up first.

That said, there’s a strong professional courtesy argument for notifying opposing counsel before the court. We’ve heard an anecdote from a lawyer in a contentious case where opposing counsel had been condescending throughout. He filed a brief with hallucinated citations. She had every reason to drop it on him with the court. Instead, she reached out to him directly, told him what she’d found, and offered him a chance to file an amended brief. His response was to threaten her with sanctions if she was making it up. About a week later, he refiled the brief with the citations corrected, no acknowledgment.

Even in that interaction, courtesy was the right call. The lawyer you’re across from today might refer you a case next year. Zealous advocacy doesn’t require being rude.

Consider giving opposing counsel a chance to fix it if you can. If they decline, or if their response makes you doubt their good faith, report it to the court and consider seeking fees for the time it took you to identify and document the error. Bring receipts. Show the cases that don’t exist or the propositions that aren’t supported. Courts are taking this seriously, and you should ask them to compensate for the work it takes to clean up someone else’s mess.

What to do if you’re the one who filed the hallucination

If you find a hallucination in something you’ve already filed, or opposing counsel does, take responsibility. That sounds obvious. But watching how some lawyers handle it in the moment, apparently it isn’t.

The pattern in the catalogued cases is striking. Confronted with a hallucinated citation, lawyers sometimes deny using AI. They often blame their associate, their software vendor, or their paralegal. They might pivot to attacking opposing counsel’s behavior. Or they sometimes insist the cases are real, then quietly correct the brief without explanation a week later. None of this works. Courts can see what happened. The deflection makes things worse.

The model for the right response is what Sullivan & Cromwell did when it happened to them: own the mistake, take personal responsibility, apologize, correct the filing, and don’t try to delegate the fault. You may still face a sanction. The sanction is almost always smaller, and the professional damage almost always less, than what comes from compounding the mistake with denial.

The bottom line on AI legal hallucinations

AI legal hallucination risks are real, but manageable. They can and do happen, but there are a few best practices you can adopt to keep them out of your work and to handle them when they show up in someone else’s.

• Use legal AI for legal work. General chatbots are great for marketing copy. But they’re not built to cite case law. If you’re producing legal work, use a tool grounded in real legal authority—yesterday’s case, yesterday’s statute, yesterday’s regulation—with hyperlinked citations and a verification workflow.
• Read the cases. Or at the very least, click the hyperlinks and pull a parenthetical quote from each one. The duty to supervise belongs at the end of the drafting process, on the supervising lawyer, before the document goes out. That has always been true. AI just made it more visible.
• Civility costs nothing. If you find hallucinations in opposing counsel’s filing, give them a chance to fix it before going to the court. If they decline, then file. If you’re the one who filed the hallucination, take responsibility quickly and cleanly.

Lawyers using purpose-built legal AI tools like Clio Work and Vincent by Clio, where citations are grounded in real law and verification is built into the workflow, will catch most hallucinations before they leave the office, in their own work and in the briefs filed against them. Used well, AI is a force multiplier in legal practice. Used carelessly, it’s a sanctions risk. The difference is the supervision step.

Mid-sized law firms appear to be ahead on AI maturity. Most have gone beyond simply trying out the technology; instead, they’ve taken a more deliberate, structured approach, with 60% establishing formal AI policies, and 38% actively encouraging staff to use AI in their work. 

And it’s paying off. Mid-sized firms are seeing the benefits to using AI: 

• 39% report that AI has directly improved their revenue. 
• 65% say AI has enabled them to handle a higher volume of work.
• 44% have seen improved client satisfaction.

But despite strong AI adoption, guidelines, and realized benefits, many mid-sized firms still struggle with a critical vulnerability in their AI infrastructure. 

The problem with how most law firms are using AI

Right now, the most common AI tools in mid-sized firms are generic, consumer-grade solutions like ChatGPT, Claude, or Gemini. While these tools are easy to pick up, and in many cases are free to use, they come with serious limitations for legal work. For example:

1. Data security risk. Generic AI tools weren’t built with attorney-client privilege in mind. Free versions may use your input data for model training, and even paid tiers may not offer the enterprise-grade security controls that legal work demands. In United States v. Heppner, a court ruled that materials generated using a free AI chatbot were not privileged, precisely because the tool lacked the confidentiality protections legal work requires.
2. Accuracy and risk of error. Generic AI tools don’t have access to up-to-date legal databases that include the latest case law, statutes, court filings, and regulatory guidance. Without a verified legal library to ground their outputs, these tools are prone to hallucinations and other types of errors: citations that don’t exist, case law that’s been overturned, or legal reasoning that sounds convincing but doesn’t hold up under scrutiny. For a mid-sized firm handling complex, high-stakes matters, relying on AI that can’t verify its own legal conclusions can be a liability.
3. Integration and connectivity. Typically, when working with a generic AI solution, your staff will work with an isolated chat interface with no ability to reference any of the context that lives in your firm. This means that each person on your team essentially starts from scratch every time they formulate a query. They’ll need to cite relevant case facts and upload documentation—and re-enter everything whenever they start a new chat or case details change.

There’s also the risk that if anything gets missed in briefing your AI, your team won’t get outputs that are relevant to their actual legal matter. 

For simple, routine tasks that don’t require any references or involve client data, generic AI solutions might be fine. But for actual legal work—contract analysis, case strategy, document review (especially across hundreds of files)—generic AI solutions won’t give you the benefits you need. 

The benefits of legal-specific AI

Legal-specific AI addresses these challenges directly. These tools are purpose-built for the work lawyers actually do. Clio Work, for example, can analyze, research, and draft legal content, and is grounded in a comprehensive legal library of over one billion court filings and legal documents. It also includes citation verification to reduce hallucinations, and it comes with the data privacy safeguards that legal work requires. 

When a legal AI tool can base its output in authoritative sources, it gives you results that you can verify and use. That value improves even more when your legal AI tool can reference key matter details to ground its research and analysis. It’s what gives you better, more informed outputs from your AI and saves your staff from spending significant time and effort on prompting. 

These solutions help address accuracy and the risk of error, but on their own, they may not completely solve the issue of integration and connectivity. 

Want more research and analysis on the use of AI?

Read our 2026 Legal Trends for Mid-Sized Law Firms report to learn more about how firms are using AI to benefit their staff and their business.

Read the report

How cloud platforms make AI actually useful for legal work

Cloud-based platforms act as a central hub for your law firm. Rather than a collection of disconnected tools, everything draws from a shared foundation. Different teams can use the software that works best for them without creating new silos or duplicating data.

When it comes to AI, all of those case details, documents, communications, and billing records can be securely referenced, giving AI tools the context they need to take on meaningful tasks across the firm and keep your team ahead in their work. And when a cloud platform is paired with a legal AI tool that’s grounded in the law, that tool can stay current on both the law and your matters to deliver quality, situation-specific results with minimal prompting. This is what’s known as “context-aware” AI—and it works much differently than a tool that doesn’t know anything about your practice.

The practical impact shows up across the firm’s daily work. Here are a few examples: 

• Contract review is more precise when AI can reference the firm’s prior agreements and established language, not just generic templates. 
• Legal research is sharper when AI considers the specific jurisdiction, facts, and procedural history of the active case. 
• Document drafting moves faster when AI pulls relevant details directly from the case file instead of waiting for someone to provide them manually. 

This is the kind of approach that Clio Work takes. You can connect your AI analysis, strategy, and research to live matter data in Clio Manage so the tool already understands the case before anyone asks it a question.

Context-aware AI can also support key administrative functions. For example, it can automatically draft bills on a set schedule and notify the responsible attorneys to review them, saving your office team the work of chasing these down themselves.

As with other tasks, your AI can do this work in minutes, not hours. Having a cloud solution for legal practice in place to connect these parts of your firm is what allows you to get more out of your AI solutions.

The broader benefits of cloud software for law firms 

The advantages of cloud software reach well beyond AI, too. Yet many mid-sized law firms are still using server-based systems to run their practice, or they don’t have any practice management system in place at all. According to the Legal Trends for Mid-Sized Law Firms, only 57% of mid-sized law firms have moved to a cloud-based practice management system, compared to 71% to 74% of solo and small firms.

What’s telling is that 30% of mid-sized firms also say their biggest technology challenge is integrating new tools into existing workflows. Without a cloud-native foundation, every new tool—including AI—becomes another disconnected system. 

For more than a decade, cloud-based solutions have created more interconnectivity between software platforms for law firms. They allow teams to work from one centralized database of information, making it much easier to keep everything accessible and up to date. This is a benefit for everyone working at the firm, saving them the trouble of hunting down information or keeping it updated in multiple places. 

Beyond centralized data, cloud platforms handle much of the operational burden of server-based systems. Updates and security patches are applied automatically with no downtime or IT support. And as a firm grows, the platform scales with it: Adding users, matters, or new tools doesn’t require investing in additional hardware or infrastructure. That frees up both budget and IT resources for work that actually moves the practice forward.

Because data lives in the cloud rather than on a local server, lawyers and staff can work from any device, anywhere, without a VPN. That same accessibility protects the firm if something goes wrong. A hardware failure or office disruption won’t put client records at risk or bring operations to a halt. And for firms with multiple offices or practice groups, everyone is working from the same system in real time, which eliminates version control issues and makes cross-team collaboration far more seamless.

The benefits extend to clients, too. Cloud-based firms can offer clients real-time visibility into their matters, easier document sharing, and online payment options, which clients increasingly expect. For mid-sized firms competing against both smaller agile practices and larger well-resourced ones, that client experience can be a meaningful differentiator.

Cloud solutions vs. server-based solutions for law firms

Here’s an overview of how cloud and server-based approaches compare in supporting the use and benefits of AI:

How to evaluate cloud readiness at your law firm

Knowing that cloud infrastructure matters is one thing. Figuring out where your firm actually stands is another. Before evaluating specific platforms, it helps to get an honest picture of your current setup to know where the gaps are, where the risks live, and where the biggest opportunities exist.

These five questions are a good starting point for any managing partner or firm administrator thinking through the shift:

Where does our firm’s data currently live, and who maintains it?

If your case files, billing records, and client communications are spread across a local server, individual desktops, and disconnected cloud apps, that fragmentation is already costing your team time. Understanding who handles backups, updates, and security helps clarify what a cloud migration would actually change.

What AI tools are our attorneys already using, formally or informally?

In most firms, AI adoption isn’t entirely top-down. Lawyers and staff are already experimenting with tools like Claude and ChatGPT on their own, whether or not the firm has sanctioned it (a situation known as “shadow IT”). Getting a clear picture of what’s in use (and what data is being entered into those tools) can help identify if an alternative is needed.

Do our current systems allow AI tools to access case and client data securely? 

If your practice management system can’t connect to AI tools through secure integrations, every interaction requires manual input, and every manual input is a potential security exposure. The answer here often reveals whether your current setup can support the AI strategy you’re building toward.

What are our biggest integration pain points today?

Most firms don’t need a full tech stack audit to answer this. There are the workarounds that everyone complains about: data entered twice, documents needed in a system where they don’t live, reporting that pulls from three different sources. These challenges are usually the clearest signals of where a cloud-native platform would have the most immediate impact.

Do we have an AI usage policy, and does it address data security for generic tools?

Sixty percent of mid-sized firms have formal AI policies, which puts them ahead of smaller practices. But if your guidelines don’t specifically address how client data should and shouldn’t be used in generic AI tools (or if staff aren’t confident about what’s allowed) there’s likely room for improvement.

The mid-sized firm advantage—if you act on it

Mid-sized firms are in a unique position within their respective markets. They have the budgets, processes, and team structures to adopt AI with real sophistication and without the layers of bureaucracy that slow enterprise firms down. The data from Legal Trends for Mid-Sized Law Firms confirms that mid-sized firms investing in AI are already outperforming smaller practices across revenue, client satisfaction, and competitive positioning.

But to close the gap with enterprise firms, AI adoption alone isn’t enough. The firms that will see the greatest returns are the ones building on a strong cloud foundation. These firms will be able to ensure that their AI use is an integrated part of how the firm operates.

If you want to learn more about getting the most out of AI at your firm, our experts can show you how cloud software and tools built for legal work make it possible. Book a demo to get started.

Why we’re upgrading

Over the private preview period, we evaluated GPT-5.5 through Clio’s core evaluation set, designed by legal experts to reflect the work our customers do every day. This includes legal research, document analysis, drafting, discovery, and scenario-based advisory. These evaluations measure end-to-end system performance, including core agentic capabilities that support longer-horizon work, such as memory management and context retention across multi-turn sessions. They assess how effectively models orchestrated through Clio’s AI retrieve, integrate, and act on firm context, while leveraging our authoritative legal content library, one of the largest in the world.

We evaluated GPT-5.5 against every other frontier model available to us, including OpenAI’s prior-generation GPT-5.4 and models from other leading AI labs. This evaluation covered hundreds of scenarios and thousands of graded criteria, at multiple reasoning-effort levels for every model. Within Clio’s AI, where models are combined with our agentic systems and legal data, GPT-5.5 delivered the strongest performance we recorded, achieving an overall score of 87.2%.

Bigger gains on the hardest legal work

Two categories of work push frontier models hardest in our evaluation, and they are where GPT-5.5 moves furthest from the prior generation.

The first is legal research that requires citing the controlling authority, including the specific case, the exact statutory section, and the leading commentary, rather than merely describing the rule. On these tasks, GPT-5.5 delivers a roughly 20% relative improvement over the prior generation, closing gaps that earlier systems consistently left open.

The second is difficult, open-ended document work, which includes contract analysis, deal-point extraction, multi-document review, and discovery across large file sets. Earlier models would reliably surface the right answer but could miss the qualifying language, scope clauses, and secondary requirements that might alter its legal meaning. GPT-5.5 reads further into the document and captures key information: the survival periods, the fraud carve-outs, the jurisdictional conditions, the conditions of exercise. Across our document-analysis scenarios, this translates to a ~7% relative improvement over the prior generation, and the difference is even larger at higher reasoning effort. The result is an answer that is not merely directionally correct but more legally complete.

More efficient use of the context window

GPT-5.5 is markedly more efficient in how it uses tokens during reasoning. It spends fewer tokens deliberating internally for the same quality of answer than other frontier models we tested. In one comparison, it used ten times fewer reasoning tokens per tool call. In practice, this means two concrete things for our customers: faster responses, and more headroom in the context window for Vincent to retain context in long, multi-turn sessions and autonomous agent work.

What this means for our customers

Clio Work and Vincent’s agent modes now run on GPT-5.5, and customers will start to see the difference in their day-to-day work. This includes:

• Legal and matter context are seamlessly integrated. Clio’s AI brings the relevant documents, notes, and matter history into its reasoning without user hand-holding.
• Drafted documents find more relevant precedent. Clio’s AI search and retrieval work is more thorough, and that thoroughness shows up in the documents it produces.
• Routing is faster, deep analysis is better. Simple legal research questions are answered quickly; Clio’s AI still triggers deep analysis when the task demands it and the quality of that deep analysis is meaningfully higher.
• Reasoning across provisions is stronger. On tasks that require connecting the dots between multiple contractual provisions or several authorities, Clio’s AI delivers more complete and confident answers, with minimal boilerplate and unnecessary qualification.

Intelligence grounded in legal context

Both Clio Work and Vincent are grounded in the Clio Library, our authoritative legal content spanning case law, statutes, and commentary across multiple jurisdictions. Clio Work can also connect to Clio Manage, allowing it to draw directly from the matter it’s working on, including documents, notes, communications, tasks, and deadlines, without the user needing to paste or re-explain context.

This grounding is what makes our AI answers usable rather than merely plausible. When our AI cites an authority, it is one it actually retrieves. When it references a clause from an engagement letter, it is one it actually reads. Because GPT-5.5 reasons more reliably across longer, richer inputs, customers get more value from the context they bring. Drafts need less cleanup, research lands closer to the final answer, and lawyers spend less time re-explaining the matter to the AI.

A foundation for the next generation

The upgrade is also foundational for the next generation of Clio’s agentic AI capabilities. Our AI can now autonomously locate and use the matter context required for a task, including documents, notes, tasks, and intake forms, without any user intervention. We are expanding these agentic capabilities so Clio’s AI takes a more active role across legal work.

In Clio Work and Vincent, GPT-5.5’s extended autonomy and reasoning ability directly supports our roadmap of highly reliable autonomous legal agents that perform relevant legal work at scale. And, as we continue to deepen our AI ability to leverage Clio Library and DocketAlarm data, the model’s stronger reasoning translates directly into more actionable guidance for legal professionals.

Looking forward

Consistency, reliability, and verifiability are the characteristics that unlock the full value of legal AI. That is the bar. Clio’s AI is built to meet it by integrating frontier models with our own systems, grounded in deep legal context. We partner closely with OpenAI and leading AI labs to push capabilities forward and bring the best of what’s possible to our platform, so those advances translate directly into the quality and scope of work our customers can achieve.