After a multi-day incident, fire-district IR wants a coherent after-action draft from scattered shift notes. Which AI automation task best fits?

Document synthesis drafts a coherent after-action report from scattered multi-day IR shift notes, helping fire-district leadership capture timeline, decisions, gaps, and follow-ups without manually stitching every fragment first. Humans still validate factual accuracy, redacting, and conclusions; the AI accelerates structure, not final approval. Extractive summarization that drafts an after-action report from verbatim sentences only reuses exact sentences, so it cannot draft a coherent after-action report with timeline, decisions, gaps, and follow-ups. Document classification that drafts an after-action report by operational phase only sorts notes into categories, but it does not synthesize shift notes into a coherent after-action draft. Named entity extraction that drafts an after-action report from personnel lists only pulls out names and timestamps, but it cannot turn multi-day IR shift notes into an after-action report. Exam caveat: AI synthesis of IR notes into an after-action draft is appropriate automation; drafting an after-action report from verbatim sentences alone, drafting by operational-phase labeling alone, and drafting from personnel lists alone are mismatched tasks that do not build the after-action document from shift notes. Operational check: retain original shift notes under records policy, scrub PII before any wide distribution, require chief or IR lead sign-off on the after-action, and treat the AI draft as a starting artifact that must be corrected against timelines, tickets, and sensor evidence.