Junior underwriters are gaining confidence and career traction as AI platforms analyze risk data and generate recommendations, according to Juan de Castro, president of risk digitization provider Cytora. The shift pushes insurers to rethink training, data governance, and when human judgment must step in.
Junior underwriters gain confidence through AI
De Castro said AI tools let less experienced underwriters move faster. "It allows them to grow and develop much faster. We're going to see accelerated careers in underwriting," he said during a webcast hosted by Insurtech Insights. Those careers will tilt toward portfolio analysis rather than assessing individual risks one by one.
He compared the emerging role to operators in a factory control room. Underwriters will focus on optimizing the book for reinsurance and steering overall portfolio health, not just single submissions.
AXA XL seeks clarity on AI use policies
When a carrier evaluates AI adoption, it's not only about its own operations. Caitlin Alpern, team lead for large cyber and technology accounts at AXA XL, said the insurer asks clients about employee training, the data fed into models, and where human review is still required. "There's not a lot of consistency yet to how different carriers are approaching this," she said during a Newfront webcast. AXA XL also probes clients' AI use policies, compliance frameworks, and model choices.
The questions highlight a wider industry effort to map the boundary between automated decision-making and human oversight. For underwriters, that boundary defines where their expertise stays central.
Data scrutiny and human relationships remain central
AI can sharpen how underwriters evaluate risk data, but it won't erase the need for skepticism and personal ties, according to Jennilee Foo-Kune, head of underwriting strategy and performance for business solutions at Scor. "Certain elements will find a hard time automating or replacing. That's the human and the relationship elements," she said in the same Insurtech Insights webcast.
Foo-Kune added that as AI handles more data evaluation, underwriters gain time to understand the risk itself. The evolution doesn't replace people so much as redirect their attention. De Castro argued the pressure is already financial: "The rules of the game have changed, and we are already seeing those who have made progress using AI are accelerating growth, and those who haven't are actually stagnating."
Speed now factors into competitive positioning. He said insurers can no longer afford a five-day quote turnaround. In a soft market, growth must come from policy volume, not rate increases, and AI helps signal to brokers which risks fit the insurer's appetite before competitors step in.
These dynamics are reshaping the skill set underwriting teams need. Understanding how to integrate AI evaluations into the quote workflow becomes as practical as traditional risk assessment. For professionals building that expertise, resources such as AI for Insurance training can support the transition from manual review to AI-assisted portfolio management.
Why this matters for insurance professionals
Underwriting teams that delay AI adoption risk losing preferred risks to faster competitors. The shift elevates junior staff, compresses quoting timelines, and makes data fluency a core underwriting capability. Professionals who learn to combine AI-driven insights with relationship-based judgment will define the next phase of the market, while those who treat the technology as a distant experiment may find their pipelines drying up.
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