Why AI matters at this scale

Cohen | Volk Economic Consulting Group provides expert economic analysis and testimony for complex legal disputes. As a mid-market firm with 1001-5000 employees, it operates at a critical inflection point: large enough to handle massive, data-intensive cases but facing pressure to deliver insights faster and more cost-effectively. AI is not a futuristic concept but a necessary tool to maintain competitive advantage. At this scale, manual review of millions of documents or granular analysis of vast economic datasets becomes a significant cost and timeline bottleneck. AI can automate these foundational tasks, allowing the firm's highly skilled economists and consultants to focus on high-value strategy, interpretation, and testimony. For a firm of this size, targeted AI adoption can directly improve profitability through greater billing efficiency and the ability to take on more or larger cases without a proportional increase in expert headcount.

Concrete AI Opportunities with ROI

1. AI-Powered Document Discovery & Triage: The discovery phase in litigation is extraordinarily labor-intensive. An AI system trained to identify relevant financial terms, economic concepts, and communication patterns can review and code documents at a scale and speed impossible for humans. The ROI is direct: reducing thousands of billable hours spent on initial review, accelerating case timelines, and allowing staff to focus on deeper analysis. This can improve margin on existing cases and increase case capacity.

2. Enhanced Economic Modeling with Machine Learning: Building damages models often involves analyzing historical data to project future losses. Machine learning algorithms can process more variables and identify complex, non-linear relationships in market data, leading to more robust and defensible models. This improves the quality of expert testimony, potentially increasing win rates or settlement values. The ROI manifests in stronger client outcomes and enhanced reputation for technical sophistication.

3. Automated Drafting of Analysis Summaries: Economists spend considerable time drafting reports and explaining complex analyses. Generative AI, guided by firm templates and past work, can produce initial drafts of data summaries, methodology sections, and even create explanatory visuals. This doesn't replace expert writing but drastically reduces the time from analysis to client-ready deliverable. The ROI is in improved utilization of top-tier talent, freeing them for more strategic work.

Deployment Risks for a Mid-Market Firm

For a firm in the 1001-5000 employee band, AI deployment carries specific risks. Integration Complexity is a primary concern; AI tools must work seamlessly with existing case management systems (e.g., Relativity) and data analysis platforms. A poorly integrated tool creates silos and reduces efficiency. Change Management is significant at this size—rolling out AI requires training hundreds of professionals, from analysts to partners, and overcoming skepticism about "black box" outputs. A clear internal communication strategy is vital. Data Security & Compliance risks are paramount. Handling sensitive legal and financial data requires AI solutions with robust, auditable security controls, often favoring private cloud or on-premise deployments over public SaaS AI. Finally, the Total Cost of Ownership can be misjudged. Beyond software licenses, costs include data engineering, ongoing model tuning, and dedicated oversight, which must be weighed against the projected efficiency gains and revenue opportunities.