Technical documentation
ISCO-08 2641Technical writer, Technical documentation manager, Information developer, Technical translator, Product content specialist
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Structured content and documentation architecture
- Validating safety and regulatory content
- Managing machine translation and terminology
- Product data quality in reference systems
- Designing documentation workflows with AI
Losing value
- Writing standard installation and user manuals
- Manual translation and proofreading
- Updating documents after product changes
- Manual layout of manuals
How the job will change
Technical documentation is one of the most exposed activities in an electrical equipment company. AI now drafts installation instructions, user manuals and release notes from engineering data, translates them into dozens of languages and updates them when a product changes. Much of the writing, translation and layout work that filled a technical writer's week is absorbed, and what remains is review and validation.
The job moves towards content architecture and control: organising structured content and product data so that AI produces reliable documents, validating safety warnings and regulatory statements, managing terminology across languages. A good technical writer tomorrow will design and supervise a documentation chain and answer for what reaches the installer, especially where a mistake can cause an electrical accident.
- 2026-2027
- AI drafts and translates manuals, writers review and validate.
- 2028-2030
- Documentation generated from product data, teams shrink and specialise.
- 2031+
- A few content architects supervise automated documentation chains.
An error in a safety instruction engages the manufacturer's liability. Shrinking the team before validation of AI-generated content is properly organised exposes the company to compliance and safety risks. 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