AI exposure: Precision Agriculture Technicians

47.4% of this occupation's weighted task load is exposed, which puts Precision Agriculture Technicians at the 86th percentile of 923 occupations. The capability is largely there. Its average task scores 2.9 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 verification cost. Across this occupation's 22 tasks it averages 1.86 out of 3, the highest of the five friction dimensions. In plain terms, checking the output costs more than producing it. Where an undetected error is expensive, dangerous or irreversible, the economics change. Someone has to verify the work, and verifying can cost as much as doing it. This is the friction most likely to fall as tools for checking improve.

The most exposed thing this job does is Use geospatial technology to develop soil sampling grids or identify sampling sites for testing..., at 73.3%. The least is Collect information about soil or field attributes, yield data, or field boundaries, at 10.0%. A gap of 63.3% 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 life, physical and social science occupations, this one is more exposed than most. The median across the 60 roles in the group is 35.8%, and only 7 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 22 tasks, 17 are currently banded exposed, 2 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.