AI 中文总结
本研究通过调查发现AI与机器人技术准备度评估的单一分数会掩盖分歧，揭示变异来源并指出部门排名仅宜作组合总结，需保留应用层面分歧。
AI 中文摘要
人工智能（AI）与工业4.0的准备度评估常采用组织、应用领域或部门的单一分数来概括准备度，这类概括可能掩盖针对同一技术的分歧以及归为同一部门标签的各类应用之间的差异。本研究通过一项基于卡片的调查，测试这种聚合会损失多少信息：982名受访者对17项已命名的AI与机器人技术挑战给出了15200项准备度评估，这里的准备度指感知到的社区准备度与可用资源，而非个人意愿或经审计的组织能力。受访者对相同挑战的分歧频繁，卡片层面准备度的标准差在5分制下为1.03至1.26。交叉分解显示，观测到的变异中32.7%源于稳定的受访者差异，7.3%源于挑战间的差异，60.0%源于包含测量误差的响应层面变异；挑战类别均值间的差异仅占变异的约2%，而人员、应用及人员类别判断间的变异大得多。制造业的平均准备度最高，但车间机器人、流程优化AI及通用决策支持应用的评估存在差异；计算机科学与AI/ML受访者在各类挑战中报告的准备度高于非技术受访者，而工程受访者未报告更高的制造业准备度。因此，部门排名最好用作组合总结，而非某一行业统一准备或落后的证据；准备度报告应保留应用层面的分歧，披露构成平均值的判断来源，并考虑伦理、网络安全、素养与能力需求，而非将其压缩为单一分数。
英文摘要
AI and Industry 4.0 readiness assessments often summarise preparedness using a single score for an organisation, application domain or sector. Those summaries can conceal disagreement about the same technology and variation among applications grouped under one sector label. We test 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. Readiness is perceived community preparedness and available resources, not personal willingness or audited organisational capability. Respondents frequently disagreed about identical challenges, with card-level readiness standard deviations of $1.03$-$1.26$ on a five-point scale. A crossed decomposition attributes 32.7% of observed variation to stable respondent differences, 7.3% to differences among challenges, and 60.0% to response-level variation that also contains measurement error. Differences among challenge-family means account for only about 2% of variation, with substantially more variation among people, applications and person-family judgements. Manufacturing has the highest mean readiness, yet shop-floor robotics, process-optimisation AI and general decision-support applications are judged differently. Computer-science and AI/ML respondents report higher readiness than non-technical respondents across challenge families, whereas engineering respondents do not report higher Manufacturing readiness. Sector rankings are therefore best used as portfolio summaries rather than evidence that an industry is uniformly ready or behind. Readiness reporting should retain application-level disagreement, disclose whose judgements form the average, and consider ethics, cyber security, literacy and capability needs without collapsing them into a single score.
Comments33 pages, 12 figures, 8 tables
