Stable anonymous code
Use the same code for every follow-up.
After deployment · dated follow-up
Monitor the effects of AI in real work
Choose a defined observation period and document effects on workload, autonomy, skills, collective work, service quality and human oversight.
10 minindicative duration18questionsDatedlongitudinal comparisonAnonymouslocal project code
Use the same anonymous code at each follow-up to compare reports without identifying the organisation.
Exploratory, non-psychometric tool. The result supports observation and corrective action; it is not a diagnosis or proof of compliance.
Before you begin
Define the period being observed
Answer from real operation, including workarounds, team differences and uses that were not originally planned.
Defined period
Archive the PDF with previous reports.
Follow-up result
Observed-effects profile
0/100—
Mapping
Nine dimensions
Priority corrections
Archiving and local history
Longitudinal archive
Keep this dated review
Save the report as PDF and retain it with previous reports using the same anonymous code.
Reviews stored in this browser
Method, limitations and interpretation
Compare observed effects, then decide
Same nine-dimension framework
The follow-up uses six psychosocial-risk dimensions and three AI-specific axes to support comparison over time.
Visible uncertainty
Questions can be marked “not assessed” so missing evidence does not create false precision.
Respond to warning signs
Incidents, overload, lost autonomy, surveillance or degraded quality justify corrective action with occupational-health and worker-representation expertise.