Insurance sector deploys AI faster than it can manage, study shows
Insurers are accelerating artificial intelligence deployment across customer service and operations, but regulatory gaps and internal skill shortages are slowing enterprise-wide rollouts, according to research from GlobalData based on 113 industry respondents surveyed in early 2026.
The findings reveal a sector caught between competitive pressure to adopt AI and practical constraints that limit where and how the technology can be used safely. Most current deployments remain confined to narrow applications like chatbots and customer-facing tools rather than complex tasks such as underwriting or claims automation.
Liability and regulation lag behind deployment
Regulatory uncertainty ranks among the top concerns. Insurers lack clarity on who bears responsibility when AI systems make errors or produce incorrect outputs-a critical gap for an industry built on risk assessment and accountability.
Ben Carey-Evans, Senior Insurance Analyst at GlobalData, said: "Regulation has not fully caught up yet, and there is concern around who is liable for mistakes made by AI."
A significant share of survey respondents do not believe AI is ready for broad-scale use within insurance operations. This skepticism stems directly from unresolved questions about system maturity and governance.
Talent shortage outpaces hiring efforts
The industry is hiring aggressively to close skills gaps. Job postings for AI roles in insurance reached 63,293 in 2025, a 50.9% increase from the previous year.
Despite this hiring surge, demand for AI expertise continues to outpace supply. Internal capability constraints remain a significant barrier, with many insurers reporting insufficient expertise at the company level to implement and oversee AI systems effectively.
Customers ready, companies are not
Respondents expressed relatively low concern about consumer acceptance. Customers across other sectors have already grown accustomed to AI tools, suggesting insurance customers will adapt without resistance.
The bottleneck is internal. Carey-Evans said insurers should adopt a phased approach, focusing on specific functions such as customer service, acquisition, or claims handling to manage deployment risk rather than attempting enterprise-wide transformation.
For professionals in insurance, this means AI adoption will likely proceed function-by-function over the next several years rather than as a sector-wide shift. Understanding which use cases your organization prioritizes-and why-will be essential for career planning.
Learn more about AI for Insurance and AI for Customer Support to develop skills in the areas your company is likely to deploy first.
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