Adopting AI Solutions in the Legal Industry and How to Minimize Barriers

We’ve talked about how AI is reshaping the legal industry in previous articles, but it’s important to remember that simply deploying an AI platform doesn’t automatically create value. Not only are there practical barriers that can keep lawyers from using AI on real client work, but teams need to identify workflows where the technology can make a measurable difference and prove its ROI. This means that adoption is less a question of whether AI can accelerate legal work and more a question of whether it can do so reliably, securely, and economically for your specific firm and its needs.

The Biggest Challenges and Barriers to Legal AI Adoption at Law Firms

AI implementation needs to be thoughtfully planned rather than rushed into and bolted onto random processes. Law firms also have good reason to scrutinize new technology before introducing it into their client work. Legal work in general must meet an unusually high threshold for accuracy, confidentiality, and governance. At the same time, legal AI solutions need to deliver enough practical value for lawyers that it actually shifts ways of working to deliver on its advantages. Some key questions that we often hear from lawyers about AI platforms include:

• Can we trust AI outputs on client work? There is no room for unsupported conclusions, inaccurate citations, or outputs that take more time to verify than they save. Legal AI solutions like Harvey ground analysis in trusted legal sources, your firm’s knowledge, and actual source documents for citations and outputs that you can verify and trust.
• Can we protect confidential, privileged, and client data? Legal teams routinely handle sensitive information, so any uncertainty about how an AI provider stores, accesses, and uses data can hamstring adoption before it even starts. Look for a legal AI platform that’s independently audited against standards like SOC 2 and ISO 27001, doesn’t use client data to train its models, and allows you to control access, logging, and data retention.
• Will lawyers actually use the tool after the pilot? Even if your pilot program is successful, lawyers may still return to familiar processes once the novelty wears off. Legal AI solutions should reduce barriers like extra verification work and disconnected workflows. Harvey leverages reusable workflows and Playbooks, role-based education, and adoption data to help innovation and knowledge teams understand their firm’s usage.
• Will AI fit the systems and workflows we already use? Understandably, lawyers are less likely to adopt and use tools that force them to move documents between systems, restate context, or leave the apps where they already work. When choosing a legal AI platform, it should integrate with common tools like the Microsoft Suite, as well as document management systems like iManage, so legal teams can bring AI into existing workflows instead of the other way around.
• Will faster work improve firm economics or reduce billable hours? For firms built around billable hours, time savings alone don’t establish ROI — it matters what happens to the capacity that AI creates, whether that means handling more matters, reducing write-offs, and improving margins on fixed-fee work. Cuatrecasas reported a major reduction in time spent on a banking due diligence review, allowing their lawyers to focus more on high-value activities like risk analysis and negotiation strategy.
• Will this actually work for a firm our size? While BigLaw firms have been some of the most prominent to adopt legal AI platforms, the economics of adoption depend on more than headcount. Firms of all sizes are seeing benefits because they focus on a set of repeatable workflows and model potential ROI using their own assumptions (and tools like our ROI calculator) to determine whether the investment makes sense.

Turn Objections Into Testable Questions

The barriers and hesitations above become easier to evaluate when firms stop treating them as broad arguments for or against AI. Instead of asking “Will attorneys use it?” or “Will AI save enough time?” firms can translate each concern into a specific hypothesis to test during a pilot program. This way, an effective pilot will answer more than whether the tools work. It will test whether lawyers use them effectively, whether the output meets your quality and risk standards, and whether any efficiency gains outweigh the time required for human review.

The key is to establish your baseline and success criteria before the pilot begins so you can measure the change brought about by AI-assisted workflows. This also makes adoption easier to diagnose and to identify where AI works, for whom, and whether the value is significant enough to scale.

How Law Firms Measure ROI of Legal AI

ROI needs to reflect concrete changes in how work gets done because usage activity alone doesn’t show whether AI is making work faster, increasing capacity, improving quality, or changing the economics of a matter. We recommend using a measurement model that spans three layers:

1. Investment
2. Adoption
3. Business impact

Start by accounting for what it takes to deploy and operate the technology, then determine whether lawyers are using it consistently, and lastly measure what changes in the workflows where usage occurs. This may materialize as reclaimed lawyer time, fewer write-offs, stronger margins, greater matter capacity, faster turnaround, or more consistent work product (or all of the above).

Metrics and How to Track Them

No single metric captures the ROI of legal AI, and the measures that matter most to your firm will depend on its practice mix and business model. Because of this, we recommend that firms combine adoption indicators with outcome indicators that show whether adoption is producing measurable value. Below, we’ve detailed a few of each category:

For more information on ROI measurement, see our in-depth guide.

Which Legal AI Workflows Show the Clearest ROI?

When it comes to showing ROI, the best candidate workflows are typically repetitive, high-volume, and measurable. This way, firms can establish a clear baseline for how long the work takes and compare it to how long AI-assisted completion takes. At the same time, the best starting point doesn’t have to be the task with the largest potential AI opportunity — a frequent workflow that produces a consistent deliverable can often make a better ROI test.

• Due Diligence and Bulk Contract Review: Reviewing large document sets for recurring terms, exceptions, and risks is one of the areas where AI excels most. The impact is also relatively straightforward to measure, as Cuatrecasas cut their due diligence time when using Harvey to extract key terms from credit agreements.
• Drafting, Redlining, and Issues Lists: These processes involve repeatable cycles of producing language, comparing versions, identifying deviations, and incorporating precedent. Harvey can quickly draft from firm precedents, compare contracts against playbooks and clause libraries, analyze redlines, and generate issues lists organized around the changes that require human judgment.
• Litigation Document Analysis and Case Prep: Synthesizing large records, identifying responsive evidence, building chronologies, and preparing arguments can be tedious when performed manually. AI helps accelerate this by querying large evidentiary records, extracting structured information, analyzing witness and expert material, and grounding drafts in the record so lawyers can focus on case strategy.
• Legal Research and Synthesis: The legal research process often combines time-intensive source discovery with reading, comparison, synthesis, and drafting. These activities are well suited to measuring both research time and verification effort, making ROI clearer and more provable. Harvey enables lawyers to move more quickly from a research question to a source-grounded analysis.

Best Practices: How Law Firms Maximize ROI

Maximizing ROI starts with measuring AI against the economics of your own firm, not assuming that benchmarks for the largest global firms will translate directly. Practice mix, billing models, utilization, matter volume, write-offs, and how your lawyers use reclaimed time all change the value of the same efficiency gain. We recommend using our law firm ROI calculator to estimate the potential economic impact of reclaimed lawyer time using conservative assumptions rather than relying on a one-size-fits-all benchmark.

To get the most value out of your AI investment (regardless of firm size), start with your own baseline and economics. Look beyond headline time savings and ask whether faster execution changes matter capacity, realization, fixed-fee margins, or the amount of lawyer time available for client and strategic work. From there, ensure that training is contextual to the work. In other words, don’t just show your team what the AI platform does — show them why they should return to it. Providing support beyond the initial onboarding also goes a long way in establishing strong adoption. Use questions, usage patterns, and workflow results to identify where lawyers need additional support before reinforcing successful use cases with practical examples.

Strategies for AI Adoption at Mid-Sized Firms

Mid-sized law firms often compete with much larger organizations on responsiveness and quality while operating with leaner teams. At the same time, many have enough practice-area breadth, firm knowledge, and technology infrastructure that adoption needs to scale beyond a handful of power users. Here are some ways that mid-sized firms can leverage legal AI platforms like Harvey:

• Standardize firm-specific workflows, playbooks, and precedent-based use cases: Turning successful individual uses of AI into repeatable workflows is especially valuable for mid-sized firms because it allows expertise developed by individual partners or practice groups to benefit the wider organization.
• Integrate AI into Word, DMS, and other systems lawyers already use: With a lean team, mid-sized firms should minimize the number of new behaviors required for adoption by bringing AI into the apps and repositories where legal work already happens.
• Measure ROI by practice group and workflow, not just firmwide usage: A single firmwide adoption rate can hide major differences between practices, potentially concealing areas where a different approach is needed.
• Track usage data to identify where adoption is deepening versus stalling: Monitoring which groups are returning to AI, which capabilities they use, and where engagement drops can help concentrate limited enablement resources on practices with promising early results or diagnose friction before it weakens a wider rollout.
• Create role- and practice-specific training paths instead of generic AI training: Partners, junior associates, litigators, and transactional lawyers all perform different work and have different reasons to adopt AI, so tailoring their education around the relevant tasks for each group makes demos and adoption feel more concrete.

Strategies for AI Adoption at Small Firms

Going a step smaller to boutique firms doesn’t mean that legal AI platforms can’t fit your needs. However, they face a different adoption equation that revolves more around expanded capacity and freeing limited lawyer time for higher-value work. Fewer organizational layers can also make it easier to focus the firm around a small number of high-value use cases, incorporating strategies like:

• Have the managing partner or owner model daily AI use personally: In small firms, leadership behavior is highly visible, so the managing partner can make the difference between AI as a part of normal legal work and a side initiative delegated to junior lawyers.
• Start with one revenue-critical workflow that affects most matters: Instead of launching several experiments at once, we recommend choosing a single recurring workflow that’s tied closely to how your firm earns revenue or serves clients (such as first-draft preparation, document review, or matter intake).
• Prioritize eliminating non-billable and write-off-heavy work first: Starting with work that consumes scarce lawyer time without reliably generating equivalent revenue can be especially impactful for smaller firms, where administrative and repetitive work often hamper capacity creation.
• Create a short approved-use policy instead of a large governance program: Governance needs to remain rigorous but proportionate to the organization, meaning that small firms can often start with a more concise policy that defines approved tools, human-review expectations, and escalation paths instead of a full committee structure.
• Convert repeatable work into templates and standardized AI-assisted processes: Small firms can get more leverage from a limited number of experts by capturing recurring approaches in templates, precedents, and repeatable AI workflows. For example, Harvey turns your firm’s best work and knowledge into reusable assets available to the entire team.

What to do When Adoption and ROI Tell Different Stories?

Adoption and ROI measure different things, so firms shouldn’t expect them to automatically move together. While usage tells you whether lawyers are incorporating AI into their work, ROI tells you whether that behavior is producing meaningful change in cost, capacity, or speed. When these signals diverge, it’s important to diagnose why before responding.

Harvey’s ROI Calculator Makes it Easy to Visualize the Impact

The biggest barriers to legal AI adoption are trust, security, workflow fit, lawyer engagement, and uncertainty about its economic impact. These are also the same questions that firms should use to evaluate whether a solution is worth scaling. For mid-sized and boutique law firms, that makes firm-specific measurement especially important. Results from the world’s largest firms can demonstrate what legal AI is capable of, but can’t tell you what the investment will look like for your firm.

To help estimate economics and ROI, use our calculator that’s designed for firms of all sizes to anticipate the expected impact of legal AI like Harvey. If you want to assess where Harvey could fit into your existing workflows, technology, and adoption strategy, request a demo below to explore the use cases most relevant to your team.