## 5 Discussion  
The application of AI-driven analytics to user interaction logs yielded meaningful insights into the patterns underlying informatics complaints in the public sector. The semantic clustering of free-text error descriptions revealed strong alignment with known sources of user frustration--such as ambiguous error messages, inconsistent data formats, and system bottlenecks during peak usage periods. Notably, the use of sentence embeddings allowed the model to group complaints that were lexically dissimilar but semantically equivalent, demonstrating the value of text mining in surfacing latent problem structures often overlooked by traditional keyword-based methods. From an operational perspective, the implementation of the AI framework resulted in a measurable reduction in complaint volume by enabling early detection and preemptive mitigation of recurring errors. Automated classification of import errors and predictive alerts for high-risk submissions accelerated issue resolution, significantly easing the burden on support teams. Moreover, visual dashboards and explainable models empowered non-technical staff to interact with AI outputs in a meaningful way, reinforcing the system's usability and practical value. Internal monitoring after the system's deployment showed promising operational results. Over a six-month observation period, there was a 27% reduction in repeated complaint categories, as measured by duplicate or semantically similar issues flagged in the system. Furthermore, the average time to classify and route complaints decreased by approximately 32%, based on support team handling logs. While these improvements were measured within a single agency and specific operational context, they highlight the potential of semantic clustering and predictive analytics to significantly enhance complaint management workflows. Future work will involve more extensive impact evaluation across multiple agencies and longer timescales to validate the scalability and generalizability of these gains. While the framework demonstrated promising results in a Portuguese local government setting, its broader applicability across different administrative, linguistic, and cultural contexts remains to be explored. Public sector organizations vary significantly in their reporting structures, complaint typologies, and user behavior, which may impact both semantic clustering and classification performance. In particular, language-specific embeddings may need adaptation for non-Portuguese corpora, and taxonomies of technical issues may require localized calibration. To support transferability, the proposed framework was designed to be modular, allowing the substitution of language models, metadata fields, and priority schemas. Future work should involve cross-jurisdictional deployments in varied institutional contexts--including international agencies and multilingual environments--to systematically assess adaptability, retraining requirements, and governance alignment. User-centric improvements were also observed. By identifying and addressing the most common causes of failure, the system facilitated small but impactful changes to the user interface, such as clearer input validation messages and automated guidance for correcting errors. These adjustments, informed directly by the AI-driven insights, enhanced user experience and reduced frustration at the source. To address potential bias, metadata on user gender was leveraged to support fairness auditing. Classification performance was disaggregated by gender, and demographic parity metrics were computed during model validation to detect disparities in predictive accuracy. These audits were reviewed by domain experts to ensure equitable model deployment across user groups. In addition to demographic parity, future iterations of the framework will incorporate further fairness metrics to deepen algorithmic governance.