Technical customer support (level 2)
ISCO-08 3512Level 2 technical support advisor, Technical hotline advisor, Remote support technician, Broadband and fibre support expert, Technical support team leader
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Diagnosing faults remote tests cannot explain
- Coordinating with field and network teams
- Explaining technical issues to frustrated customers
- Feeding the knowledge base from new cases
Losing value
- Running standard line and router tests
- Searching the knowledge base during calls
- Scripted reboot and reconfiguration procedures
- Writing ticket summaries
How the job will change
Much of level 2 support follows decision trees: run a line test, check the router, reset a configuration, book a technician visit. Remote diagnosis tools and conversational agents now do this directly with the customer, through the app or the voice server. When a case does reach an advisor, the assistant has already summarised the history, run the tests and proposed a cause.
Level 2 therefore receives fewer but harder cases: intermittent faults, Wi-Fi problems in large homes, faults at the boundary between network and customer equipment, customers on their third call. A good advisor tomorrow reasons beyond the script, works closely with field and network teams, and turns each unusual case into knowledge the agents can reuse.
- 2026-2027
- Assistants summarise tickets and suggest diagnoses; self-care apps resolve simple faults.
- 2028-2030
- Agents handle most level 1 and part of level 2 cases.
- 2031+
- A smaller expert level 2, focused on complex faults and knowledge.
Level 1 shrinks first, and that is where level 2 advisors used to learn the job. Without a new learning path, level 2 will lack candidates who know both the customer and the network. 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