The Legora ROI Report: Measuring the Impact of AI on Law Firms is a vendor-commissioned qualitative study published March 30, 2026, written by legal-industry analyst Ari Kaplan on behalf of Legora. It draws on three months of named interviews with 31 customer law firms — including AmLaw, Magic Circle, and top international practices — across 14 countries, and reports an average of 4.3 non-billable hours per week recaptured per lawyer, which it converts to a potential $6.9 million in additional yearly billing per 100 lawyers. The author describes it as the most comprehensive customer-side ROI study of legal AI deployments to date.
| Field | Detail | |
|---|---|---|
| Author | [[entities/ari-kaplan | Ari Kaplan]] (legal industry analyst) |
| Commissioned by | [[companies/legora | Legora]] |
| Published | March 30, 2026 | |
| Sample | 31 law firms (AmLaw, Magic Circle, top international practices) across 14 countries | |
| Methodology | Three months of qualitative, named interviews |
Sample profile
Among the 31 participating firms, 51.6% have 500 or more lawyers, 38.7% have 200 to 500 lawyers, and 9.7% have at least 200 lawyers. By region, 35.5% are in the UK, 29% in EMEA, 22.6% in North America, and 12.9% in APAC. Interviewees were 54.8% innovation and AI leaders, 25.8% partners, and 19.4% other roles.
Findings
Productivity
Productivity gains were reported across the sample. On a 1-5 scale, 94% rated Legora's impact on client-response speed as faster (4) or much faster (5), and 65% said tasks that previously took hours now take minutes. Concrete examples cited include a 9,000-contract review collapsed from months to days, investment-review legal due diligence reduced from a week to hours, and a 4,500-trade-slip review with 75% time savings on a 130-hour project.
Capacity expansion
74% said Legora has expanded their capacity through more work, more revenue, or effort redirected to higher-value analysis, and 45% said they can support more clients in the same time.
Recovered non-billable hours
68% reported that Legora has reduced non-billable hours. The average recaptured was 4.3 non-billable hours per week per lawyer, which the report converts to a potential $6.9 million in additional yearly billing per 100 lawyers, using the formula: number of lawyers × hours per week × hourly rate × 46 weeks. On related measures, 35% reported fewer write-downs, 19% redoing less work, and 16% writing off less junior time due to quality issues.
Beneficiary distribution
Mid-level associates were identified as the primary beneficiaries by 68% of participants, followed by senior associates (55%), junior associates (48%), and partners (29%). Saved time was directed to higher-value legal analysis (65%), greater client interaction (52%), and increased billable work on other matters (42%).
Quality and confidence
71% said Legora identifies issues they otherwise would have missed. 61% reported being slightly more confident (4) or much more confident (5) in the quality of their work when supplementing it with Legora, and 42% rated Legora's impact on improving quality, issue-spotting, or reducing the risk of mistakes as large (4) or transformational (5).
Pricing and win rate
39% said Legora has made it easier to deliver fixed-fee or capped-fee work, 42% said it has helped them win new work directly or indirectly, and 45% said it has helped expand existing client relationships. 77% have cited Legora, or AI more broadly, when explaining pricing, value, or turnaround. Additionally, 23% take on work they might otherwise decline, and 29% scope matters more tightly with fewer professionals.
Talent and performance
65% reported a significant performance boost for high-performing professionals, and 32% saw meaningful gains across the team broadly.
Long-term stickiness
74% rated Legora 9 or 10 on a likelihood-to-recommend (NPS-style) scale. 55% reported that Legora has replaced or reduced the need for other tools or processes — 45% in document review, 23% in first-draft generation, and 16% in issue spotting. 52% said Legora has helped knowledge-management efforts, 61% rated its impact on the firm's ability to build internal AI skills, habits, or infrastructure as large or transformational, 87% said it has helped build internal AI skills, and 48% thought it sets them apart from competing firms on matters they work on.
Areas without measured impact
29% reported no meaningful change in write-offs, write-downs, or redo work. 58% had not won new work as a result of using Legora, or had not yet seen those results, and 55% had not seen an expansion in existing client relationships. The report notes that most participants lack formal dashboards correlating AI usage to billing outcomes, describing measurement as "still catching up to deployment."
Methodology caveats
The report is vendor-commissioned: Legora paid for the study, and all 31 firms are existing Legora customers willing to be interviewed by name, creating a selection bias toward satisfied users. It is qualitative; the report states that quotes and case studies are concrete and directionally useful, but that topline figures such as the $6.9M per 100 lawyers should be treated as upper-bound estimates rather than population means. There is no control or counterfactual — no comparison to firms using competing legal AI products such as Harvey, CoCounsel, or Springbok, or to firms using no AI tooling. There is no formal time-tracking integration, and most participants offered "general responses" when asked how they measure time savings.
Reception and context
The report presents itself as the first detailed customer-side ROI study of legal AI at scale, with named firm participants and concrete case studies. 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. The findings document a gap between productivity and revenue realization: productivity gains were reported across the sample, but converting them into billable revenue was uneven, with 29% reporting no change in write-downs or redo work and 58% not having won new work. The identification of mid-level associates as the primary beneficiaries bears on staffing-model and apprenticeship-pipeline questions. The finding that 39% report easier fixed-fee delivery relates to broader claims about erosion of the billable-hour model. No equivalent customer-side ROI study exists for Harvey, the dominant US legal-AI competitor.
Relationships
- commissioned-by: Legora
- authored-by: Ari Kaplan
- deploys-in: Legal Services — AI Deployment
- related: Harvey (the dominant US legal-AI competitor; no equivalent customer-side ROI study exists for Harvey), Intelligence Replaces Hierarchy (Pereyra's parallel framing), Professional Services AI Adoption
- supports: AI Labor Disruption (with caveats about vendor-commissioned methodology)