Insurance Claims Adjusters Emerge as Workforce's Most Vocal AI Critics
Glassdoor research shows claims adjusters are critical of AI 98 percent of the time they mention it, amid rapid job losses tied to automation.
What happened
Glassdoor research identified insurance claims adjusters as the occupational group most critical of AI in the American workforce, with 98 percent of their AI-related reviews expressing negative sentiment. Adjusters report that AI systems introduced for initial loss reporting and claims processing generate misclassifications, hallucinations, and errors that require manual correction, increasing rather than reducing their workload. Between May 2025 and May 2026, employment in the claims adjustment sector dropped 21 percent according to Bureau of Labor Statistics data, with entry-level postings falling 50 percent since 2025. AI startups like Liberate and Pace have raised millions to automate claims handling, while companies like Lemonade report their AI chatbot handles 96 percent of initial reports with automation managing 55 percent of all claims. Claims adjusters express concern that they are training their own replacements and lack confidence in AI accuracy, citing instances where document smudges or missing details in AI summaries lead to incorrect payouts.
Context
The insurance industry is undergoing significant workforce contraction driven by AI adoption. As insurers scale automation to handle claims filing, photo analysis for damage estimates, and medical record summarization, the traditional role of human adjusters is being redefined or eliminated. Workers perceive the deployment as management imposing tools they view as error-prone and inadequately tested, creating friction between leadership's efficiency goals and worker concerns about job security and product quality. The acceleration of job losses among entry-level adjusters—the steepest decline in the data—signals a structural shift in how the industry onboards and staffs claims departments. Meanwhile, customers often attribute AI errors to adjuster incompetence, creating reputational pressure on adjusters for mistakes they did not make.