Market access and pricing
ISCO-08 2413, 2421, 2631Market access manager, HEOR manager, Pricing and reimbursement manager, Health economist, Value and access lead, Market access director
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Negotiating with pricing committees and payers
- Building the value case for HTA bodies
- Challenging AI-built health economic models
- Anticipating payer objections by country
Losing value
- Drafting standard sections of HTA dossiers
- Manual systematic literature reviews
- Adapting global value dossiers country by country
- Building international price benchmarks
How the job will change
Market access teams produce large written and quantitative files: global value dossiers, HTA submissions, systematic literature reviews, cost-effectiveness and budget impact models, international price benchmarks. AI absorbs much of this production. It screens the literature, adapts a global dossier to a national template, updates a budget impact model with new inputs and drafts the first answer to an HTA body's questions.
The value of the job concentrates in strategy and negotiation: choosing the comparator, framing the population where the product makes a difference, defending the price in front of a committee that has its own budget constraint. A good access manager tomorrow can take apart a model they did not build, and knows the people and the doctrine of the payer opposite.
- 2026-2027
- Literature reviews and dossier adaptations largely AI-assisted.
- 2028-2030
- European joint clinical assessments and AI-built models reshape dossier work.
- 2031+
- Leaner production teams; senior negotiators and strategists remain scarce.
Senior negotiators who know payers and HTA doctrine are few, and many are close to retirement. Automating dossier work must not remove the junior roles where that judgement is learned. 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