Compare roles · AI exposure side by side
Engineering Managers
Put up to three roles next to each other: the exposure figure, the inputs behind it, the window, what is driving change, and where the work moves. Every figure comes from the same method, so the gap between two scores means something.
Exposure
Window · adoption
until most of the change has landed
High adoption
In one line
AI accelerates design, code and documentation across engineering teams, changing what managers plan, review and staff for.
Inputs behind the score · scaled 0–100
Task applicability
33Observed usage
4Official exposure tier
70Labour-market trajectory
n/aPublished adoption rating
70What is driving it
- AI coding and design assistants
- Office copilots for management work
- Generative design and simulation optimisation
- Quality and governance demands
- High sector adoption
- Low observed usage by managers
Skills worth building
- Technical judgement
- Quality assurance and review design
- People development
- AI tool governance
- Stakeholder communication
- Outcome-based measurement
Tools in the work now
Where the work moves
More exposed
Different skills, growing
Complementary, less exposed
Full report
Scores are the CareerGuard Exposure Index v2: the same five inputs and weights for every role, so a gap of ten points means the same thing wherever it appears. How scores are built.