Insurance sales and advice
ISCO-08 3321Insurance adviser, Branch sales adviser, Telesales adviser, Account manager, Tied agent, Wealth and protection adviser
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Needs analysis for protection and savings
- Advice on retirement planning
- Building long-term client relationships
- Explaining cover and exclusions plainly
Losing value
- Quoting simple motor and home products
- Writing meeting notes and advice reports
- Searching product documentation for answers
- Prospect list targeting and call scripts
How the job will change
Simple products such as motor or home insurance are increasingly sold online or through conversational assistants, with no adviser involved. For advisers, AI prepares the meeting: client summary, detected needs, product suggestions. It then drafts the meeting note and the written advice report required by distribution rules, which used to take a large share of the time after each appointment.
Advisers concentrate on what customers still want to discuss with a person: protection, savings, retirement, cover for a business, a claim gone wrong. A good adviser tomorrow will understand a household's or a small business's situation, explain cover and exclusions honestly and keep the relationship going over years, while checking that the tool's suggestions really fit the client.
- 2026-2027
- Meetings prepared by AI, advice reports drafted automatically.
- 2028-2030
- Simple products mostly sold online, advisers refocus on protection and savings.
- 2031+
- Fewer remote sales roles, branch and field advisers on complex needs.
Turnover is already high in these roles. The main risk is mis-selling: if advisers follow tool suggestions without understanding the product, regulatory and reputational exposure rises. 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