Product and collection development
ISCO-08 2163, 3323Product developer, Collection manager, Product manager (leather goods), Development engineer, Sourcing and development buyer
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Arbitration between design, cost and feasibility
- Supplier negotiation on new materials
- Managing tight development calendars
- Sustainability and traceability requirements
Losing value
- Building tech packs by hand
- Compiling cost sheets from supplier quotes
- Chasing samples by email
- Updating collection tracking spreadsheets
How the job will change
A large part of a product developer's week goes into tech packs, cost sheets, sample tracking and supplier emails. AI assistants now draft technical files from a sketch, compare quotes, flag delays in the development calendar and prepare supplier correspondence. Product lifecycle management tools will add assistants that pre-fill these steps.
The job concentrates on arbitration: what can be made, at what cost, by whom and by when. A good developer tomorrow will know materials and suppliers in depth, challenge the costings a tool proposes, and hold the line between the creative intent and the industrial and sustainability constraints.
- 2026-2027
- Assistants draft tech packs, supplier emails and cost comparisons.
- 2028-2030
- PLM tools pre-fill development steps; shorter cycles, more options tested.
- 2031+
- Smaller teams focused on arbitration and supplier relationships.
Knowledge of materials and suppliers is built over many collections and rarely written down. Cutting assistant roles too fast removes the path through which that expertise is passed on. 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