Internal audit and permanent control
ISCO-08 2411, 2413Internal auditor, IT auditor, Audit engagement manager, Permanent control officer, Second-line control analyst, Head of permanent control
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Auditing AI models and automated processes
- Root-cause analysis of control failures
- Data analytics on full populations
- Presenting findings to executive and audit committees
Losing value
- Manual sample testing
- Drafting standard reports and working papers
- Collecting evidence from operational teams
- Filling in control checklists
How the job will change
Auditors and permanent control officers spend much of their time testing samples, collecting evidence, filling checklists and writing reports. AI changes the method: whole populations of transactions or files can be tested, anomalies come out ranked, and working papers and draft reports are produced from the evidence. Many second-level controls become continuous and automated, with the officer reviewing the exceptions.
The profession moves from checking to judging. Audit plans will cover AI systems themselves: credit scoring models, automated KYC, agents in operations. A good auditor tomorrow can question a dataset, understands how a model was built and tested, and writes a finding that a business head accepts. Seniority and business knowledge will count for more than the ability to run tests.
- 2026-2027
- Full-population analytics; AI-drafted working papers and reports.
- 2028-2030
- Continuous automated controls; audit plans cover AI systems and agents.
- 2031+
- Smaller control teams; more senior auditors with data and AI expertise.
Supervisors will expect banks to audit their AI systems, yet few auditors today can assess a model. This skill takes years to build and must start before regulatory pressure peaks. 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