The adoption of generative artificial intelligence in the public sector has been treated predominantly as a technological problem, with the expectation that productivity gains would follow from the availability of increasingly capable models. This paper argues, drawing on two auditable cases in the Brazilian Public Service, that the determining barrier to adoption observed in these units was not technological but training-related, and describes the four-layer structured pedagogical methodology developed by the author. The method was applied in two units with distinct institutional profiles: the Sectoral Internal Control Office of the Federal District Department of Health (SES/CONT) throughout 2024, and the Internal Control Unit of the Federal District Department of Economic Development, Labor and Income (UCI/SEDET) throughout 2025. In both cases, the official indicators from the Electronic Information System of the Federal District Government (SEI-GDF), verifiable by third parties, recorded substantial gains: average processing time fell by 18.2% at SES/CONT and by 50% at UCI/SEDET, with UCI also recording a 92% increase in technical-report production, the issuance of 288 formal recommendations to public managers, and the analysis of cases totaling USD 104.3 million in financial volume. In neither unit did internal control mechanisms identify any information-security incident, sensitive-data leakage, or formal compliance challenge from external oversight bodies during the period examined. The analysis is consistent with the hypothesis that the method is portable across agencies with distinct mandates, operates within protocols designed to comply with international and national data-protection law and with the principles of public administration, and is accessible to public entities under budget constraints, since it used free AI models.

翻译：生成式人工智能在公共部门的采用主要被视为技术问题，期望随着日益强大的模型可用性能够自动带来生产力提升。本文基于巴西公共服务部门两个可审计案例，论证了在这些单位中观察到的采用决定障碍并非技术层面，而是培训相关，并描述了作者开发的一种四层结构化教学方法。该方法被应用于两个具有不同机构特征的单位：2024年全年的联邦区卫生部部门内部管控办公室，以及2025年全年的联邦区经济发展、劳动与收入部内部管控处。在这两个案例中，来自联邦区政府电子信息系统、可由第三方验证的官方指标记录了显著成效：SES/CONT的平均处理时间下降18.2%，UCI/SEDET下降50%，同时UCI的技术报告产出增加92%，向公共管理者发出288条正式建议，并分析了总财务规模达1.043亿美元的案例。在审查期间，两个单位的内部管控机制均未发现任何信息安全事件、敏感数据泄露或来自外部监督机构的正式合规挑战。分析结果与以下假设一致：该方法可在不同职责的机构间移植，运行于符合国际和国内数据保护法及公共行政原则的协议内，且因使用免费AI模型而对预算受限的公共实体具有可及性。