Carvana

Carvana's in-house legal team operates at the scale typical of a large enterprise, where attorneys routinely shoulder high volumes of repetitive, time-intensive work — contract review, research, drafting, and matter management — that crowds out higher-judgment activity. In corporate legal departments, this overhead is a structural problem: headcount grows with transaction volume, but the work that actually requires legal expertise remains bottlenecked. For a team of 70+ professionals, even modest per-person inefficiencies compound into hundreds of hours lost weekly, limiting the department's capacity to serve the business without proportionally increasing cost.

Carvana deployed Harvey AI's platform — built on large language models and generative AI — across more than 70 legal professionals to automate and accelerate routine legal workflows. Harvey AI specializes in AI applications purpose-built for legal work, offering capabilities that go beyond generic LLM tooling: context-aware document analysis, contract drafting assistance, legal research acceleration, and workflow integration suited to in-house environments. The rollout reached meaningful adoption scale, with the platform embedded into day-to-day workflows rather than used as an occasional research tool. This broad deployment across the legal department allowed the organization to capture compounding time savings at the team level rather than isolated individual gains.

The Carvana deployment produced consistent, measurable efficiency gains across the team:

• 7–10 hours saved per professional per week, sustained across 70+ legal staff
• At 70 professionals, this represents hundreds of hours reclaimed per week across the department
• Harvey AI has reported that corporate lawyers using the platform broadly save 15–50 hours per month

Beyond raw time savings, the scale of adoption — 70+ professionals actively using the platform — indicates strong workflow integration rather than surface-level experimentation. The gains translated to increased capacity for higher-value legal work without a corresponding increase in headcount.

• Broad deployment matters: individual AI tools add up to team-level capacity gains only when adoption is consistent across the department, not siloed to a few power users.
• 7–10 hours saved per professional per week is a meaningful ROI signal — in-house teams should model total-department time recovery, not just per-person figures, when evaluating legal AI.
• Purpose-built legal AI (Harvey AI) outperforms generic LLMs for in-house teams because it understands legal context, document types, and workflow patterns.
• Efficiency gains do not require headcount reduction to deliver value — recaptured attorney time can be redeployed to strategic work, risk management, and business partnership.