This study examined common remediation strategies by analysing publicly available IT incident reports. A six-category taxonomy (“Software Fix”, “Rollback”, “Traffic Switch”, “Hardware/Infrastructure Repair or Operation”, “Self-Resolved”, and “Undisclosed/Not Specified”) was developed to classify implemented solutions. Subsequently, a corpus of 1268 recent public incident reports sourced from the VOID community database was collected, from which the solution description of each report is classified utilising a promptbased approach with the LLaMA3.3-70B-Versatile large language model (LLM). The LLM classifier
demonstrated substantial agreement (with Cohen’s κ = 71.4%, Macro F1 = 80.6%) with manual annotations on a ground truth subset of 127 reports. The primary findings revealed that a significant majority (76%) of the reports do not disclose specific technical solutions. Among reports with identifiable fixes, software fixes (5.5% of total) were the most common. Exploratory analysis also showed a statistically significant but small relationship between solution category and incident duration. This research highlights the utility of LLMs for analysing incident reports and powering AIOps and underscores the need for improvement in incident reporting.