Embedded software and IoT
ISCO-08 2152, 2512Embedded software engineer, Firmware engineer, IoT software engineer, Embedded validation engineer, Connectivity engineer, Embedded software architect
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Embedded architecture under memory and power constraints
- Cybersecurity of connected products
- Reviewing and testing AI-generated code
- Hardware integration and communication protocols
- Functional safety (IEC 61508)
Losing value
- Writing drivers and boilerplate code
- Writing unit tests by hand
- Code documentation
- First-level debugging of common errors
How the job will change
Code generation tools already write a good share of routine code in an embedded team: peripheral drivers, communication stack configuration, unit tests, documentation. They also help engineers understand legacy firmware nobody has touched for years. Gains are smaller than in web development, because code must fit tight memory and real-time constraints and be validated on real hardware, but they show in daily work.
The job moves towards architecture, integration and security. Rules on connected products require secure updates and vulnerability management over the product's life, which creates work AI does not remove. A good embedded engineer tomorrow will read generated code with suspicion, know the hardware well, and understand what makes a protection relay or a building controller safe in the field.
- 2026-2027
- Code generation for drivers, tests and documentation in daily use.
- 2028-2030
- Agents run test and integration cycles, engineers validate on hardware.
- 2031+
- Fewer junior coders, more architects and product cybersecurity specialists.
Junior firmware developers used to learn the hardware by writing drivers, which AI now does. Without structured mentoring on real boards, companies will lack engineers able to debug at hardware level. 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