Insurers' Risk Leaders Cite AI Staffing Concerns

EY's third annual "New shape of risk" survey found corporate risk leaders reporting the impact of AI on staffing and specializations, Dig-in reports. The survey compiled responses from chief risk officers and senior risk executives at 106 insurance companies and Institute of International Finance member firms worldwide, dig-in reports. Dig-in reports the survey showed a 19 percentage point increase in respondents expressing concern about their staffs' ability to adapt, and respondents listed improving productivity while holding staff levels steady and evaluating AI's impact over the next three years as top concerns, Dig-in reports. "You need talent to support that," said Stu Doyle, principal in the consulting practice at EY, in comments carried by Dig-in.

Industry-pattern observations: Across financial services, reported AI adoption frequently shifts lower-skill, routine work toward automation while increasing demand for data-science and model-risk capabilities. Companies that have publicized AI pilots often report short-term productivity gains alongside longer timelines for integrating domain knowledge into models. For insurance risk teams, that pattern typically surfaces as increased hiring or retraining needs for data-science, model validation, and governance skills.

Editorial analysis: The Dig-in coverage places the EY survey in a broader trend where risk functions move from compliance-check roles toward active partners in product and underwriting oversight as AI instruments proliferate. That shift is relevant to practitioners because it changes the profile of required technical skills, the emphasis on model risk management frameworks, and the kinds of vendor relationships insurers pursue.

Editorial analysis - technical context: Risk teams evaluating AI impact should expect to engage more with: model validation pipelines, explainability tooling, data lineage and feature governance, and reproducible evaluation workflows. Observers following implementations often find that creating reliable evaluation datasets and integrating model monitoring into production are practical bottlenecks rather than raw model performance alone.

Track these indicators to follow how insurers respond to the concerns documented in the EY survey: changes in hiring mixes for risk and analytics roles; published model governance or validation policies; procurement of external model-audit services; and adoption of monitoring and explainability tooling. Also watch survey follow ups that quantify timelines for reskilling and external hires.

All reported survey figures and quotes are from Dig-in's May 5, 2026 report summarizing EY's "New shape of risk" survey and comments from Stu Doyle, principal in the consulting practice at EY, as presented in the Dig-in article.