AI exposure: Hydroelectric Production Managers

Hydroelectric Production Managers is one of the unusual cases where capability and friction are both high. Current systems can produce a great deal of this work, with average capability across its tasks at 2.2 out of 4. Only 29.4% of the weighted task load comes out as exposed, because 24.5% of it runs into something structural. Jobs in this band tend not to disappear. They change shape, and the person stays.

What holds the line here is context. Across this occupation's 18 tasks it averages 2.28 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 or enforce hydrostation voltage schedules, at 80.0%. The least is Plan or manage hydroelectric plant upgrades, at 3.3%. A gap of 76.7% 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 management occupations, this one is less exposed than the median of 41.4% across the group's 56 roles, with 55 scoring higher. Being in an exposed family does not make a particular job exposed, and the reverse holds too.

What would move this score. Of 18 tasks, 9 are currently banded exposed, 4 assisted and 5 untouched. For that distribution to shift materially would take cheaper ways to verify output, since the cost of checking is currently doing more to hold this work in place than the cost of producing it. The score is re-computed every quarter against a fresh capability reference, and the change is published rather than quietly applied.