The convention of pricing professional work by time recorded rather than by output or outcome, dominant in law firms and in parts of consulting and accounting.
Why it structures AI adoption
In most sectors, a tool that reduces the labour a task requires improves margin. Under time-based pricing it does not automatically do so, because the same hours are simultaneously the cost base and the revenue base. A firm that halves the time a matter takes, and bills accordingly, has halved the revenue from that matter.
This makes AI adoption in these sectors a pricing-model question before it is a technology question, and it is why adoption evidence from law and consulting behaves differently from adoption evidence elsewhere. The available responses — absorbing the gain as capacity, moving to fixed or outcome-based fees, or maintaining rates while reducing recorded time — have different distributional consequences for firms, associates, and clients, and the sector has not converged on one.
The ROI-measurement problem
The difficulty shows up directly in how legal-AI returns are reported. The Legora ROI Report: Measuring the Impact of AI on Law Firms "presents itself as the first detailed customer-side ROI study of legal AI at scale, with named firm participants and concrete case studies," and its "$6.9M per 100 lawyers" figure "has become a widely cited reference point for legal-AI ROI, and a contested one given the vendor-commissioned methodology."
Beyond the provenance question, a per-lawyer ROI figure requires a convention about what the recovered hours are worth — whether they are rebilled, redeployed, or simply not worked. Under time-based pricing those assumptions determine the answer, which is why figures of this kind vary widely between studies that appear to measure the same thing.
Relationships
- related: Legal Services — AI Deployment — the sector where the model is most entrenched
- related: The Legora ROI Report: Measuring the Impact of AI on Law Firms, Consulting, AI and Productivity, AI Labor Disruption