Finance and controlling (FP&A)
ISCO-08 2411, 2413Management controller, FP&A analyst, Financial analyst, Finance business partner, Budget controller, Industrial controller
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Scenario modelling and driver-based forecasting
- Challenging operational assumptions with managers
- Presenting trade-offs to the executive committee
- Consistency checks on model outputs
Losing value
- Collecting data from multiple ERP extracts
- Spreadsheet restatements and reconciliations
- Writing standard monthly variance comments
- Building recurring reporting packs
How the job will change
In most finance departments, the monthly cycle is still built on extracting data, restating it in spreadsheets, checking it against the ledger and writing variance comments. AI assistants inside planning and reporting tools now draft those comments, flag anomalies and rebuild forecasts from operational drivers, which cuts the time needed to produce a monthly pack or a budget iteration.
The role moves closer to the business. Controllers will spend more time testing assumptions with plant managers or sales directors and turning several scenarios into a clear decision. The good controller tomorrow is the one a business unit head calls before deciding: someone who can explain why the forecast moved and who notices when a model output makes no sense.
- 2026-2027
- Assistants draft variance comments and speed up reporting packs.
- 2028-2030
- Rolling forecasts largely automated; budget cycles get shorter.
- 2031+
- Smaller teams focused on scenarios and business partnering.
Many current controllers were hired for their spreadsheet skills, not for their influence with operations. The main risk is a skills gap in the business partner role rather than a headcount surplus. 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