Team meetings generate action items. Action items get lost in notes. Someone forgets. Project stalls. Built 6-node workflow. 47 action items extracted last month. Zero missed. Summary hits Slack before the meeting room empties. THE MEETING NOTES DISASTER: Someone takes notes. Types them up later. Emails the team. Action items buried in paragraphs. Nobody reads until someone asks "wait, who was supposed to handle that?" Project manager spending 30 minutes post-meeting reformatting. Action items scattered. Owners unclear. Follow-ups forgotten. THE DISCOVERY: Document extraction pulls structured meeting data. Attendees, decisions, action items with owners and due dates. Priority levels assigned automatically. One extraction. Everything organized. No manual parsing. THE WORKFLOW: Google Drive trigger watches notes folder → Download meeting notes → Document extraction pulls attendees, decisions, action items with priorities → Code formats summary with owner assignments → Sheets logs complete meeting record → Slack posts formatted summary immediately. 6 nodes. Meeting chaos eliminated. THE ACTION ITEM EXTRACTION: JSON Schema captures: Meeting title, date, duration, attendees with roles, decisions with rationale, action items array with owner, due date, priority (High/Medium/Low), follow-up meetings, parking lot items. Code formats for Slack. High priority flagged. Owners bolded. Due dates highlighted. THE TRANSFORMATION: Before: 30 minutes reformatting notes. Action items buried in email threads. "Who was handling vendor call?" After: Summary in Slack within 15 seconds. Clear ownership. Zero ambiguity on assignments. THE NUMBERS: 47 action items extracted last month 12 team meetings processed 0 missed assignments 30 minutes → 0 minutes manual formatting Priority-based flagging automatic UNEXPECTED BENEFIT: Project manager now participates in meetings instead of transcribing. Team gets summary before they pack up laptops. Template in n8n and All workflows in Github

Every AI system scales two things at once: capability and exposure. Most teams obsess over throughput, automation rate, cost savings. Very few quantify risk propagation. When an error happens, how far does it travel before detection? How many downstream decisions inherit that mistake? A mature AI audit measures blast radius, detection latency, and recovery cost, not just accuracy. It tests edge cases, monitors drift, and tracks override patterns over time. If your monitoring starts only after complaints, you are not scaling intelligence, you are scaling delayed failure. As AI Transformation Partners, our responsibility is to ensure speed does not outrun control. Scale without guardrails is not leverage. It is amplified fragility.