Pricing

ISCO-08 2120, 2413

Pricing actuary, Pricing analyst, Pricing data scientist, Technical pricing manager, Head of pricing

AI exposure59%

Headcount need, in FTE

Exposure range

Your current headcount

FTE

Need in 203088 FTE-12 FTE (-12%)

Need in 203577 FTE-23 FTE (-23%)

How AI is used on these tasks today

Automation 41%Augmentation 59%

In AI conversations about this job's tasks: the AI does the task itself (automation) or helps the person do it (augmentation).Anthropic Economic Index, April and May 2026. Occupations: Actuaries, Data Scientists. AEI

1. Exposure 59%Average automation potential of the job's tasks. Range for this family: 47 to 68%.ILO occupations used: Mathematicians, Actuaries and Statisticians (56%), Financial Analysts (62%). ILO data

2. Strategic ceiling 100%Your call: how much of this potential do you want to capture? Nobody can set it for you.

3. Adoption speed midpoint 2030

Inherits your company stance, adjustable for this family.

4. Conversion to headcount sensitive 40%Default 40%: Insurers use the gain to reprice more often and by finer segments and channels, rather than to shrink teams.

Target AI skills

level 1 to 4

Foundation

Use AI assistants every day

Frame and phrase a request

Check and challenge AI outputs

Protect data and respect the rules

Applied

Rethink one's process with AI

Analyse data with AI

Produce content with AI

Delegate to and supervise AI agents

Expert

Build and integrate AI solutions

Evaluate and make AI solutions reliable

AI governance and risk management

Job skills

Growing in value

Explaining and governing machine learning pricing models
Testing tariffs for fairness and indirect discrimination
Balancing technical price and commercial strategy
Monitoring competitive market positioning

Losing value

Manual variable selection for GLMs
Writing data preparation and modelling code
Producing standard tariff impact studies
Documenting models by hand

How the job will change

Pricing teams already work with statistical models, and AI speeds up the whole chain. Code generation tools write data preparation and modelling scripts, automated machine learning tests many model variants, and assistants produce first drafts of model documentation and impact studies. A tariff revision that took several weeks can be prepared much faster and repeated more often.

The question shifts from building models to governing them. Supervisors and the European AI Act expect insurers to explain prices, avoid indirect discrimination and document every model. A good pricing professional tomorrow will understand what a complex model really captures, defend a tariff to management and the regulator, and balance technical price with commercial reality.

2026-2027: Code generation and automated modelling shorten tariff revision cycles.
2028-2030: More frequent, finer repricing under stricter model governance.
2031+: Teams focused on model governance, explainability and market strategy.

Watch out

The constraint is regulatory as much as technical. Teams need people able to explain and defend complex models, and these hybrid actuarial and data science profiles are scarce and courted by other sectors. See the seniority outlook below.

What if you hired fewer juniors?

Your 2036 seniors are the juniors you hire today.

Seniors available in 2036-5%2 FTE short

Gap above 5% from2036

Your seniority mix today

Juniors (0 to 3 yrs in the profession)25%Seniors (10+ yrs)30%

Mid-level (3 to 10 yrs in the profession, the remainder)45%

Cut in junior hiring -40%

For

Advanced settings

Juniors promoted each year25%Juniors leaving each year12%Senior gap filled by external hiring0%If every company makes the same bet, senior profiles will be scarce and expensive.

Seniority mix, % of today's headcount

Juniors25%Mid-level42%Seniors28%

2026 2036

Free guide

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

Download the AI Cookbook

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.

Book a call

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.

Public researchExposureAverage automation potential of the job's tasks, from the ILO's 2025 task-level scores.

Your decisionStrategic ceilingHow much of that potential you choose to capture. Your decision, not ours.

Your companyAdoption speedHow fast your company moves, set once for the whole company.

Job family defaultConversion to headcountHow much of the productivity gain becomes fewer people rather than more output.

Headcount effect

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.