Signal Whose readiness counts? Disagreement within and between sectors in perceived AI and robotics preparedness

Summary

AI and Industry 4.0 readiness assessments typically summarize preparedness with a single score per organization, application domain or sector, but such summaries can conceal disagreement about identical technologies and variation among applications grouped under one sector label. The authors tested how much information is lost through this aggregation using a card-based survey in which 982 respondents provided 15,200 readiness evaluations across 17 named AI and robotics challenges, defining readiness as perceived community preparedness and available resources rather than personal willingness or audited organizational capability. Respondents frequently disagreed about identical challenges, with card-level readiness standard deviations of 1.03-1.26 on a five-point scale. A variance decomposition attributed 32.7% of variation to stable respondent differences, 7.3% to differences among challenges, and 60.0% to response-level variation including measurement error, while differences among challenge-family means accounted for only about 2%. Manufacturing had the highest mean readiness overall, yet shop-floor robotics, process-optimization AI, and general decision-support applications within it were judged quite differently, and computer-science/AI-ML respondents reported higher readiness than non-technical respondents across challenge families. The authors recommend treating sector readiness rankings as portfolio summaries rather than evidence that an industry is uniformly ready or behind, and argue readiness reporting should retain application-level disagreement and disclose whose judgments form the average.