Medical affairs and MSL
ISCO-08 2262, 2131Medical science liaison, Medical advisor, Medical manager, Therapeutic area medical lead, Medical director
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Critical appraisal of AI literature summaries
- Scientific exchange with key opinion leaders
- Designing real-world evidence studies
- Structuring field medical insights
Losing value
- Manual literature monitoring
- Writing congress reports
- Preparing training materials for sales teams
- Formatting scientific slide decks
How the job will change
In medical affairs, a large share of time goes into literature monitoring, congress reports, slide decks and training for field teams. AI assistants now screen publications, summarise posters within hours of a congress and draft the first version of a scientific deck. Insights reported by MSLs after their visits can be tagged and grouped automatically, a task that was done by hand until now.
The core of the job stays human: discussing data with an expert who disagrees, judging what a subgroup analysis really shows, deciding which evidence gap justifies a study. A good MSL or medical advisor will read AI summaries with the same suspicion as a weak publication, and spend the time saved in front of experts and on real-world evidence projects.
- 2026-2027
- AI literature monitoring and congress summaries become routine.
- 2028-2030
- Field insights analysed continuously and shared with marketing and R&D.
- 2031+
- Teams stable in size, more focused on evidence generation.
An MSL with deep expertise in a therapeutic area takes years to develop and is poached at every competitor launch. The planning risk here is a shortage of profiles. 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