COOKED verdict contested
The daily reality of this job is GIS work at a screen: cleaning spatial datasets, running buffer/overlay/suitability analyses, producing cartographic outputs, and writing up findings for planners, agencies, or clients — and AI plus automated geoprocessing pipelines now do large parts of that at usable quality. What resists is research design (choosing the right spatial model and defending it), fieldwork and ground-truthing, and being the person who stands behind a land-use or hazard conclusion in front of a commission. There is no license and no signature requirement, so the occupation has no regulatory floor, and at 1,400 US jobs it is small enough that modest productivity gains absorb hiring.
Mixed — a routine tier and a judgment tier. An 8 reflects that the bread-and-butter deliverables — geocoding, raster reclassification, weighted-overlay suitability models, choropleth map production, remote-sensing land-cover classification — are already scripted in ArcPy/GEE or handled by AI-assisted pipelines, while the parts that hold (specifying a spatial model that survives a peer or commission challenge, deciding what the census tract boundary artifact is doing to your result) are a minority of billable hours rather than the whole day.
Some physical or field component. A 6 is for the field weeks that punctuate an otherwise screen-bound year: GPS ground-truthing of classified imagery, site reconnaissance for a land-use study, survey administration in the study area — real but seasonal, and much of the observational work is being displaced by higher-cadence satellite and drone imagery you never leave the office to collect.
No licence, no signature requirement. A 2, not 0, because some geographers carry GISP certification or work under a PE/PLS who seals survey-derived products, but no state requires a licensed geographer to sign a suitability analysis or hazard map, so nothing legally blocks an employer from replacing your workflow with software.
Some relationship component. A 6 covers the repeat-client and agency relationships — the planning department that calls you because you know their parcel data's quirks and their commission's history — but the deliverable is a map and a report that another analyst could produce, and procurement often treats it that way.
Meaningful discretion. A 10 is right because you genuinely choose the spatial weights, the scale of analysis, and which uncertainty to disclose in a hazard or siting conclusion, but the decision itself is made by the planning commission, agency, or client who takes your map as one input among many and bears the consequences.
Has AI actually changed your work?