Underwriting
ISCO-08 3321, 2413Underwriter, Commercial lines underwriter, Senior underwriter, Specialty lines underwriter, Underwriting assistant, Underwriting manager
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Technical judgement on atypical risks
- Broker relationship and negotiation of terms
- Steering the portfolio against risk appetite
- Critical review of model risk scores
Losing value
- Extracting data from broker submissions
- Quoting standard risks from rating grids
- Drafting routine quotes and endorsements
- Compiling loss histories from several sources
How the job will change
An underwriter receives submissions from brokers in every format: emails, questionnaires, schedules of locations, loss histories. AI now extracts and structures this data, checks it against underwriting guidelines and pre-prices standard risks. For small businesses and simple property risks, a large share of quotes can go out without an underwriter reviewing each one.
The underwriter's time shifts to atypical and large risks, to discussions with brokers and to steering the portfolio against the company's appetite. A good underwriter tomorrow reads a risk beyond what the data shows, knows when to override a model score and can defend a price or an exclusion in front of a demanding broker.
- 2026-2027
- Automated extraction of broker submissions, AI-assisted pre-pricing of standard risks.
- 2028-2030
- Small commercial risks largely underwritten automatically, underwriters refocus on complex business.
- 2031+
- Fewer, more senior underwriters steering portfolios and atypical risks.
Underwriting judgement takes years to build, and many senior underwriters will retire over the coming decade. If junior posts disappear along with standard risks, succession on complex lines will be thin. See the seniority outlook below.
What if you hired fewer juniors?
Your 2036 seniors are the juniors you hire today.
Advanced settings modified
2026 2036
The AI Cookbook 2026
albert's guide to cut through the noise around generative AI and turn it into workforce decisions.
- What AI actually changes in jobs and skills
- Why the junior pipeline matters more than most companies realise
- How to integrate AI into workforce planning

Run these scenarios on your actual workforce
AI Impact Diagnostic: 6 to 8 weeks, your data in albert, three costed scenarios and the projected seniority mix for each job family.
How the numbers are built
Every headcount figure combines four assumptions. Three come pre-filled from public research and job family defaults. The strategic ceiling is yours to set.
Seniority outlook
A flow model with three levels of experience in the profession. Promotion and exit rates are set so that today's mix stays stable when hiring does not change: any gap you see comes from the junior hiring cut alone.
Sources
- International Labour OrganizationGenerative AI and jobs: a refined global index of occupational exposure (2025)
- ILO datasetTask-level GenAI exposure scores by ISCO-08 occupation
- AnthropicAnthropic Economic Index (June 2026 release, April and May 2026 usage data)
- Stanford Digital Economy LabCanaries in the Coal Mine? Six facts about the recent employment effects of AI