Project management and PMO
ISCO-08 2421, 1219Project manager, Programme manager, PMO analyst, PMO manager, Project coordinator, Transformation lead
Headcount need, in FTE
Target AI skills
level 1 to 4Job skills
Growing in value
- Stakeholder management and sponsor alignment
- Anticipating risks and framing decisions
- Change management with affected teams
- Running delivery with AI-supported tools
Losing value
- Weekly status reports and steering committee decks
- Meeting minutes and action tracking
- Manual updates of schedules and resource plans
- Consolidating portfolio dashboards
How the job will change
A large part of project work is administrative, and that is what AI absorbs: meeting minutes and action lists, weekly status reports, steering committee decks, schedule updates, consolidation of portfolio dashboards. Agents can chase action owners, update the planning tool and flag slippage from the data. PMO roles devoted mainly to collecting and formatting information from project teams are the most exposed.
Project managers spend more time on what tools cannot do: aligning sponsors with diverging interests, anticipating risks, making trade-offs on scope and budget, carrying change with the teams affected. The PMO moves from reporting to portfolio arbitration and decision support. A good project manager tomorrow is judged on decisions obtained and value delivered, much less on the quality of the status deck.
- 2026-2027
- Minutes, status reports and committee decks drafted by AI assistants.
- 2028-2030
- Agents track actions and schedules; reporting-focused PMO teams shrink.
- 2031+
- Project roles centred on sponsorship, arbitration and change management.
Junior PMO roles were a common entry into project careers. If they disappear, organisations will struggle to grow project managers who have learnt governance and planning from the inside. 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