Unlike traditional firms that bill by the hour, personal injury attorneys on contingency fees are highly motivated to adopt AI. Efficiency gains don't reduce billable hours; they directly boost profit margins by settling cases faster and with less manual work, creating clear and immediate ROI.

Traditional SaaS companies are trapped by their per-seat pricing model. Their own AI agents, if successful, would reduce the number of human seats needed, cannibalizing their core revenue. AI-native startups exploit this by using value-based pricing (e.g., tasks completed), aligning their success with customer automation goals.

As agencies adopt AI to increase efficiency, clients will rightfully question traditional pricing models based on billable hours. This creates an "arbitrage" problem, forcing agencies to redefine and justify their value based on strategic insight and outcomes, not just the labor involved.

C-suites are more motivated to adopt AI for revenue-generating "front office" activities (like investment analysis) than for cost-saving "back office" automation. The direct, tangible impact on making more money overcomes the organizational inertia that often stalls efficiency-focused technology deployments.

Contrary to its reputation for slow tech adoption, the legal industry is rapidly embracing advanced AI agents. The sheer volume of work and potential for efficiency gains are driving swift innovation, with firms even hiring lawyers specifically to help with AI product development.

By using AI to respond to discovery requests instantly, plaintiff firms can force defense counterparts, who bill by the hour, to either spend significant time (and client money) responding or settle faster. This tactical use of AI directly exploits and undermines the core business model of their opponents.

To penetrate tech-resistant markets like personal injury law, the winning model is not selling AI software but offering an AI-powered service. Finch acts as an outsourced, AI-augmented paralegal team, an easier value proposition for firms to adopt than training existing staff on new, complex tools.

VC Keith Rabois highlights a core conflict: law firms billing by the hour are disincentivized from adopting AI that makes associates more efficient, as it reduces revenue. This explains why corporate legal departments are faster adopters—their goal is to cut costs.

Within the last year, legal AI tools have evolved from unimpressive novelties to systems capable of performing tasks like due diligence—worth hundreds of thousands of dollars—in minutes. This dramatic capability leap signals that the legal industry's business model faces imminent disruption as clients demand the efficiency gains.