AI Adoption Has Surged to 78 Percent in This 1 Industry—but There’s a Catch

One industry has gone from barely touching AI to mass adoption in just two years. AI adoption in the legal field jumped from 23 percent to 78 percent, which is faster than in finance and healthcare. Litify’s third annual State of AI in Legal Report, which surveyed hundreds of legal professionals across law firms, corporate legal departments, and plaintiff practices, found that legal professionals are now among the fastest AI adopters anywhere. But there’s a problem hiding inside that adoption number. Only 14 percent say AI is helping them reduce costs. Just 7 percent report billing more time. Legal firms rushed to buy the sports car, then kept driving it in first gear. The gap between “we use AI” and “this changed our economics” is enormous, and it’s widening even further. “At Litify, we view this as an ‘AI maturity gap,’” notes Curtis Brewer, CEO of Litify, the legal operations platform used by 55,000+ legal professionals. “A firm that relies solely on a general-purpose tool like ChatGPT is only at the first step of its maturity journey.” The Litify data reveals exactly where firms are stuck. ChatGPT dominates usage at 66 percent, followed by Microsoft Copilot (42 percent) and Google Gemini (24 percent). These are general-purpose tools—not legal-specific platforms. And while 66 percent use AI for legal research and 39 percent for summarization, only 6 percent use it for creating invoices and 5 percent for client communication. Firms are deploying AI for tasks that feel productive but don’t directly touch revenue. Why freemium tools hit a wall General-purpose AI tools work well for research and summarization. The problem isn’t that they’re bad, but that they plateau quickly. That ceiling is exactly why legal-specific platforms like Harvey—built from the ground up on legal data and trained on case law, contracts, and regulatory frameworks—have been gaining traction at major firms. Harvey now counts PwC, A&O Shearman, and half of the 100 highest-grossing law firms in the U.S. among its clients, and has raised over $1.2 billion, with reports of another $200 million round in the works at an $11 billion valuation—partly on the argument that generic AI simply wasn’t built for legal nuance​​​​​​​​​​​​​​​. “The primary limitation of these general-purpose tools is their lack of legal and business context,” Brewer says. “Legal work is defined by nuances — solicitation rules, jurisdictional requirements, compliance standards, and practice-area-specific workflows — that general models often overlook.” Then there’s the context problem. Ask ChatGPT to summarize a case, and it only sees what you feed it — not the case history or the client’s background. And since it also can’t take action after summarizing, it’s more or less a dead-end tool. “A legal-specific tool that lives alongside your data and processes can summarize the case and suggest the next best actions or additional questions to ask,” Brewer says. “As the industry raises the bar, firms that delay are doing more than just missing out on features — they are widening a performance gap that may soon become impossible to close.” The shadow IT security risk Here’s where the adoption-without-governance problem gets dangerous: Only 41 percent of firms have an AI policy, and only 45 percent say their staff receive sufficient training. But 78 percent are using AI tools. That means roughly a third of legal professionals may be using AI in what amounts to a shadow IT environment, where there’s no oversight, guardrails, or policy. “Security, security, security!” Brewer says. “Given the highly sensitive nature of legal data, business leaders should be concerned that nearly a third of their staff may be using AI in a ‘shadow’ environment without direct IT oversight.” When employees use public AI tools, they might paste in confidential client information or HIPAA-protected medical records without thinking twice. These systems have no real safeguards. One careless prompt could mean a data breach, regulatory violation, or destroyed client relationship. “When firms fail to provide proactive guidance and purpose-built tools, staff will seek their own solutions,” Brewer explains. “If AI adoption isn’t intentional and structured from the top down, firms risk losing the very efficiency gains they sought in the first place, while exposing themselves to additional risks.” What workflow integration actually looks like The difference between AI as an assistant and AI as a business driver comes down to integration. Consider billing. Asking ChatGPT to create an invoice is like using your smartphone’s calculator instead of the accounting app. Sure, it works. But you still have to manually punch in every client detail, every payment amount, and every line item. You saved five minutes on the template and spent an hour filling it in. That’s unproductive. “When AI ‘lives’ natively alongside your billing, client, and case workflows, the impact is fundamentally different,” Brewer notes. “It transforms from an assistant to a proactive business partner.” An integrated AI tool doesn’t just generate a branded invoice template with client and matter details pre-filled. It can automatically suggest missing time entries or proactively identify billing errors. That’s the difference between saving 10 minutes and changing the economics of the entire billing process. Litify’s clients who’ve embraced this level of integration are seeing dramatic operational scaling — some firms handle twice as many matters with the same staff, and the highest performers have grown headcount by up to 400 percent as they’ve expanded regionally and nationally. The four-dimension framework Brewer says firms need to move on four fronts at once. 1. Tools: You have to stop relying on ChatGPT alone, because that’s not going to get you there. You should move to legal-specific platforms that effectively integrate with your case management, billing, and client systems. 2. Readiness: Write an AI policy. Spell out which tools are approved, how to handle sensitive data, when humans must review output, and what to do when something goes wrong. Then treat training like a safety requirement, not an HR checkbox. 3. Task scope: Research and summarization are fine starting points. But firms that stay there are leaving money on the table. The next level is workflow automation — routing requests, running conflict checks, and building chronologies. Eventually, let AI assign cases, generate invoices, and handle intake. 4. Impact: Pick metrics before you spend another dollar. Cost per matter. Turnaround time. Write-off rates. Error rates. “The try-it-and-see period is ending,” Brewer says. “Leaders will expect ROI.” Ultimately, the firms pulling ahead didn’t just buy software. They rewired how legal work gets done — from intake to invoice and research to billing — with training, governance, and measurement baked in from the start. You can keep using the sports car in first gear. But eventually, someone in your market will figure out where the other gears are. BY KOLAWOLE ADEBAYO