Law firms struggle with AI adoption despite workflow flaws
The legal industry has spent the past two years rapidly adopting AI tools, yet many law firms continue to rely on outdated workflows that limit the technology’s effectiveness. According to iManage adviser Ivy Grey, the next stage of legal innovation does not involve adding more AI products but instead requires rethinking workflows to ensure seamless AI integration. The emphasis must move from automation as an end in itself to structuring how work transitions between people, software, and AI systems.
Workflow diagrams typically outline steps and automation points, but the true difficulty lies in arranging those steps and guaranteeing smooth transitions. Whether work shifts from a senior lawyer to a junior associate, an AI tool, or another system, clear processes and accountability are non-negotiable. Without them, AI adoption loses momentum, and any efficiency gains vanish.
The fundamental problem is trust. When an AI generates a legal argument or cites a fact, someone must validate it. Previously, a physical document—such as a mortgage payment record—could be accepted as accurate without review. However, AI-produced text demands explicit confirmation: Was the amount accurate? Did the payment date match the contract’s cure period? A junior associate might verify these details and mark them as confirmed, allowing senior lawyers to concentrate on higher-level analysis.
The verification-value paradox undermines AI gains
Without this verification step, AI introduces a contradiction: Lawyers end up spending more time manually reviewing AI outputs than they would have without the technology. This creates what Grey terms the verification-value paradox, where the supposed efficiency benefits of AI disappear because no one tracks who performed the checks or whether they were completed.
Many workflow failures originate from poor transitions. Lawyers frequently instruct colleagues or AI tools to “perform task X” without detailing the 25 underlying requirements. A well-designed handoff, however, includes the objective, necessary resources, and guidance for handling uncertainties. This is more than best practice; it distinguishes delegation from mere task assignment.
Effective delegation demands clear context and measurable results. If a lawyer cannot evaluate whether a task was executed correctly, they lack the expertise to delegate it properly. The answer is not additional tools but improved instructions. For instance, a litigator drafting a brief from court filings should specify that each factual claim must be cross-referenced with the original document, with a visible record of verification.
Another essential principle is that workflows must align with existing legal practices. If attorneys depend on email for critical information, the system should incorporate email data rather than forcing them into a new platform. The objective is to embed accountability within current processes, not introduce extra steps that breed resistance.
How seamless workflows make AI trustworthy
AI tools generate drafts effectively, but their usefulness hinges on how those outputs are integrated into legal work. The most successful firms do not merely deploy AI; they restructure workflows so verification becomes a natural part of the process. A junior associate confirms facts with initials; a senior lawyer reviews the annotated work without redundant reviews. The system trusts these transitions because they are designed to be reliable.
This method does not replace human judgment with AI. Instead, it ensures that when AI assists, the results are dependable enough to advance without continuous double-checking. The alternative, adding accountability after the fact, creates delays that erase AI’s advantages.
Lawyers adopt new methods only when they align with established habits. A workflow that incorporates verification into familiar tools, rather than enforcing new ones, has the best chance of enduring. The conclusion is straightforward: AI will not transform legal work unless the surrounding workflows are rebuilt with accountability as their foundation.
To address this, Grey recommends mapping out each stage of a legal task, identifying where AI can assist, and ensuring that human oversight remains embedded. This approach requires collaboration between legal technologists and practitioners to create systems where AI and human expertise complement each other. Without this integration, AI risks becoming another layer of complexity rather than a tool for efficiency.
