Compare roles · AI exposure side by side
Actuaries
Put up to three roles next to each other: the exposure figure, the inputs behind it, the window, what is driving change, and where the work moves. Every figure comes from the same method, so the gap between two scores means something.
Exposure
Window · adoption
until most of the change has landed
Medium-High adoption
In one line
AI is speeding up actuarial modelling, data preparation and report drafting, while pricing, reserving and sign-off stay under professional control.
Inputs behind the score · scaled 0–100
Task applicability
32Observed usage
7Official exposure tier
100Labour-market trajectory
n/aPublished adoption rating
55What is driving it
- Machine-learning pricing platforms
- Code assistants
- Automated data pipelines
- Generative drafting and summarisation
- Regulatory expectations on model risk
- Low observed usage so far
Skills worth building
- Machine-learning model validation
- Programming with assistants
- Model governance and explainability
- Communication with non-specialists
- Data engineering awareness
- Professional judgement in assumption setting
Tools in the work now
Where the work moves
More exposed
Different skills, growing
Complementary, less exposed
Full report
Scores are the CareerGuard Exposure Index v2: the same five inputs and weights for every role, so a gap of ten points means the same thing wherever it appears. How scores are built.