How AI Tools Like Harvey Transform Legal Workflows

The legal profession is seeing a fundamental shift as AI tools like Harvey increasingly handle tasks such as research, drafting, and analysis. While AI’s capabilities are growing, effective use hinges on understanding its limitations and verifying its outputs. The stakes are high: a wrong citation or flawed interpretation can lead to real-world consequences, including sanctions for attorneys.

AI Can Outperform Lawyers in Specific Tasks

According to the 2025 Vals Legal AI Report, purpose-built legal AI tools have surpassed human lawyers in tasks like document analysis, information retrieval, and data extraction. AI tools scored seven percentage points higher than lawyers in legal research accuracy, while operating between 6 to 80 times faster. This shows that the technology is no longer a novelty but a meaningful productivity multiplier for law firms.

However, AI’s utility isn’t uniform. General-purpose chatbots rely on web-based training data and lack the precision of legal-specific platforms like Harvey. Tools like Harvey use retrieval-augmented generation (RAG) to ground answers in authoritative sources like statutes and case law, minimizing risks such as fabricated citations and incorrect statutory interpretations.

Verification Remains Non-Negotiable

No matter how advanced an AI tool is, the responsibility for verifying its answers remains with the lawyer. Several attorneys have faced sanctions for filing documents with fabricated citations generated by AI, emphasizing that accountability doesn’t transfer to the software.

Bar associations generally allow AI use, provided lawyers verify the outputs and apply their own professional judgment. Tools like Harvey are designed to streamline this process by surfacing sources and reasoning steps, making verification a routine task rather than a burden. This feature is critical as it reduces the risk of cutting corners, which often occurs when verification becomes time-consuming.

How Lawyers Can Frame Better Questions

AI’s answer quality depends heavily on the precision of the question. For example, a vague query like "Is this contract enforceable?" will produce an equally vague response. In contrast, a well-posed question—e.g., “Under New York law, does the limitation-of-liability clause in section 8 of the attached agreement survive a gross-negligence claim? Cite supporting authority.”—yields actionable results. Lawyers should specify jurisdiction, reference specific documents, and request citations to improve AI output reliability.

Tailoring AI Use to the User’s Level

The right AI approach depends on who's asking the question. For consumers, free general tools can provide basic orientation but are no substitute for legal advice. Solo practitioners and small firms can use purpose-built tools like Harvey for real legal work, provided they rigorously verify outputs and protect client confidentiality. For enterprise and in-house legal teams, the stakes are higher, requiring robust security, document ingestion capabilities, and third-party accuracy benchmarks.

Looking Ahead: AI in Legal Workflows

AI in the legal field is evolving from simple Q&A functionalities to agentic workflows, where tools research, draft, and analyze tasks through multi-step processes. Harvey exemplifies this trend by integrating with existing systems, grounding answers in verified sources, and supporting end-to-end legal workflows. This approach allows lawyers to focus on higher-order judgment tasks while maintaining control over the process.

For legal professionals, the skill set required to navigate this AI-driven environment is clear: precise questioning, critical evaluation of AI outputs, and thorough verification remain paramount. As tools like Harvey become more sophisticated, the firms that adapt quickly and responsibly will likely lead in this new era of legal practice.