AI exposure: Gambling Managers

41.8% of this occupation's weighted task load is exposed, which puts Gambling Managers at the 74th percentile of 923 occupations. The capability is largely there. Its average task scores 2.6 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 19 tasks it averages 1.92 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 Direct the compilation of summary sheets that show wager amounts and payoffs for races or events, at 73.3%. The least is Notify board attendants of table vacancies so that waiting patrons can play, at 0.0%. A gap of 73.3% 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 more exposed than most. The median across the 56 roles in the group is 41.2%, 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 19 tasks, 13 are currently banded exposed, 3 assisted and 3 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.