AI exposure: Solar Energy Systems Engineers

37.8% of this occupation's weighted task load is exposed, which puts Solar Energy Systems Engineers at the 66th percentile of 923 occupations. The capability is largely there. Its average task scores 2.4 out of 4 on what a current system can produce, and the frictions that hold other jobs in place are comparatively weak here.

What holds the line here is context. Across this occupation's 13 tasks it averages 2.00 out of 3, the highest of the five friction dimensions. In plain terms, the work depends on knowledge the model cannot hold. Much of this job runs on things that were never written down: what this particular organisation does, what happened last week, what the person across the table actually meant. That context is the barrier, and it erodes as systems are given more access.

The most exposed thing this job does is Create plans for solar energy system development, monitoring, and evaluation activities, at 73.3%. The least is Test or evaluate photovoltaic (PV) cells or modules, at 8.3%. A gap of 65.0% between two parts of the same job is the reason this index publishes at task level. An occupation-wide number would have hidden both.

Within architecture and engineering occupations, this one is more exposed than most. The median across the 56 roles in the group is 37.5%, and only 23 of them score higher than this. Occupational families are not uniform, and the spread inside them is often wider than the gap between them.

What would move this score. Of 13 tasks, 8 are currently banded exposed, 2 assisted and 3 untouched. For that distribution to shift materially would take a change in who is permitted to sign the work, which is a question for regulators rather than for engineers. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.