Customer service
ISCO-08 4222, 4225Customer service advisor, Contact centre agent, Customer service team leader, Complaints handler, Customer care manager, Back-office customer administrator
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Handling complex and emotional customer cases
- De-escalating complaints and retention calls
- Taking over conversations started by chatbots
- Spotting and reporting faulty bot answers
Losing value
- Answering simple information requests
- Writing standard email replies
- Logging and summarising calls
- Searching the knowledge base during calls
How the job will change
Customer service is one of the functions where AI is already in production. Chatbots and voice agents handle simple requests such as order tracking, address changes or contract information, while assistants suggest replies, summarise calls and fill in the CRM for advisors. Each advisor handles more contacts, and a growing share of simple requests never reach a human.
The calls that remain are harder: complaints, disputes, customers in difficulty, cases the bot failed to solve. The job requires more experience and emotional resilience than before, and team leaders must manage advisors and bots together. A good advisor tomorrow resolves the complicated case at first contact, keeps a frustrated customer and reports when automated answers go wrong.
- 2026-2027
- Assistants summarise calls and suggest replies; bots handle simple requests.
- 2028-2030
- Voice and chat agents resolve most routine contacts end to end.
- 2031+
- Smaller teams of experienced advisors handling complex and sensitive cases.
Natural turnover makes reductions easy, which tempts companies to automate too far. Remaining calls are harder, so advisor burnout and service quality need close watching as simple contacts disappear. 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