Insurers accelerate AI deployment but face structural and regulatory constraints, study finds
The insurance sector is expanding artificial intelligence use amid talent gaps, regulatory uncertainty, and concerns over enterprise-wide deployment readiness and risk
Artificial intelligence (AI) adoption across the insurance sector has accelerated sharply, but structural weaknesses, regulatory uncertainty, and capability gaps are tempering expectations of widespread enterprise deployment, according to research from GlobalData.
The findings suggested insurers, brokers, and reinsurers were under increasing competitive pressure to deploy AI-enabled tools, particularly in customer service and operational efficiency, even as concerns persisted around system maturity and governance.
The GlobalData survey, conducted across Q1 and Q2 2026 and based on 113 respondents, found that a significant share of industry participants did not believe AI was yet ready for broad-scale use within insurance operations.
According to the research, concerns were driven in part by limited regulatory alignment and unresolved questions over accountability where AI systems generate errors or incorrect outputs.
Ben Carey-Evans, Senior Insurance Analyst at GlobalData, said current deployments were still largely concentrated in narrow use cases such as chatbots and customer-facing tools, rather than end-to-end underwriting or claims automation.
He added: “Regulation has not fully caught up yet, and there is concern around who is liable for mistakes made by AI.”
The survey also identified internal capability constraints as a major challenge, with a lack of AI expertise cited as a significant issue at the company level.
However, respondents expressed relatively low concern about consumer readiness, suggesting customers were already becoming accustomed to AI tools across other sectors.
GlobalData’s labour market analysis showed 63,293 active job postings related to AI roles in insurance during 2025, representing a 50.9% increase on the previous year. The data pointed to a rapid industry-wide effort to close the skills gap, although demand continues to outpace supply.
Carey-Evans noted that insurers were likely to face persistent implementation challenges given the scale of expectations surrounding AI and suggested a phased approach focusing on specific functions such as customer service, acquisition, or claims handling could help manage deployment risk.