Treasury, credit and collections
ISCO-08 2413, 4311, 4214Treasurer, Cash manager, Credit manager, Credit analyst, Collections officer, Cash collection specialist
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Negotiating payment plans with key debtors
- Credit risk judgement on large accounts
- Supervising automated dunning sequences
- Cash forecasting scenario analysis
- Bank relationships and financing negotiation
Losing value
- Manual cash allocation and lettering
- Standard reminder letters and calls
- Compiling daily bank positions
- Building weekly cash forecasts by hand
How the job will change
Collection teams spend their days matching incoming payments to invoices, sending reminders and chasing customers by phone, while treasury teams compile bank positions and update cash forecasts. These are rule-based, high-volume tasks. Automatic cash allocation, dunning agents that adapt the tone to each customer and forecasting models fed by the ledger already take over a large part of them.
What remains is the work where judgement and negotiation count: a strategic customer paying late, a distributor in difficulty, a credit limit to raise before a large order. The good professional tomorrow reads a customer's risk beyond the score, negotiates a payment plan and sets the rules the agents follow. Treasurers gain time for financing, hedging and bank relations.
- 2026-2027
- Automatic cash allocation and AI-assisted dunning become standard.
- 2028-2030
- Agents run most reminders; teams focus on disputes and large accounts.
- 2031+
- Small credit and treasury teams supervising an automated cash cycle.
Collections often sit in shared service centres with a high share of junior staff. Reductions will be fast and concentrated there, which calls for early redeployment planning rather than a late restructuring. 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